d1.4- final project management report...d1.4 - final project management report cognet version 1.0...

D1.4- Final Project Management Report

Document Number D1.4

Status Final

Work Package WP 1

Deliverable Type Report

Date of Delivery 31/12/2017

Period Covered 1st July 2015 – 31st December 2017

Responsible Unit WIT

Contributors Diarmaid Brennan (WIT),Martin Tolan (WIT),

Teodora Sandra Buda (IBM), Olga Uryupina

(UNITN), Alberto Mozo (UPM), Marius Corici

(FOK), Angel Martin (VIC), Domenico Gallico

(IRT)

Keywords Project Management, Objectives, Results,

Achievements.

Dissemination level PU

D1.4 - Final Project Management Report

CogNet Version 1.0 of 51

Change History

Version Date Status Author (Unit) Description

0.1 30/11/2017 Working Martin Tolan (WIT)

Diarmaid Brennan (WIT)

TOC & Section responsible


Diarmaid Brennan (WIT)

Angel Martin (VIC)

TOC and sections refined

WP6 consolidated contribution

0.3 10/12/2017 Working Marius Corici (FRAUN) WP5 contribution

0.4 15/12/2017 Working Angel Martin (VIC)

Teodora Sandra Buda (IBM)

WP6 updated contribution

WP2 contribution

0.5 23/12/2017 Working Olga Uryupina (UNITN)

Domenico Gallico (IRT)

Teodora Sandra Buda (IBM)

Martin Tolan (WIT)

WP3 contribution

WP7 contribution

Updated WP2 contribution


Alberto Mozo (UPM)

Marius Corici (FRAUN)

WP4 consolidated contribution

WP5 updated contribution

0.7 30/12/2017 Working Martin Tolan (WIT) Review of all sections.

0.8 31/12/2017 Working Diarmaid Brennan (WIT) Review of all sections.

1.0 31/12/2017 Final Martin Tolan (WIT) Final version for release



Abstract

Keywords

The goal of this deliverable is to describe the objectives of the CogNet project as set out in the

original proposal and to capture the solutions that were synthesised addressing those objectives.

This document also captures some of the elements of the project management approach applied

that provided for a smooth running of the project including collaboration and communications

between work packages and risk identification and mitigation.

5G, Project Management, Collaboration, Risks, Integration, Testing, Validation, Demonstrators,

Infrastructures, Testbed.



Table of Contents

Change History .............................................................................................................................. 2

Abstract .......................................................................................................................................... 3

Keywords ........................................................................................................................................ 3

Table of Contents .......................................................................................................................... 4

1. Project Context and Objectives........................................................................................... 5

2. Main Scientific and Technical Results ................................................................................ 7

2.1. WP2 - Requirements and Architecture ......................................................................................... 7

2.1.1. Objectives considered by this work package....................................................................10

2.2. WP3 – Advanced Machine Learning for Data Filtering, Classification and Prediction ......11


2.2.2. Achieved Results .....................................................................................................................12

2.3. WP4 – Network Resource Management ....................................................................................16


2.4. WP5 – Network Security & Resilience.........................................................................................19

2.4.1. WP5 Objectives and Approach ............................................................................................20

2.4.2. Achievements of the work package ....................................................................................22

2.5. WP6 – Validation & Integration ...................................................................................................25


3. Risks and mitigation actions ............................................................................................. 32

4. Potential Impacts ................................................................................................................ 35

4.1. Main Dissemination Activities .......................................................................................................36

4.2. Publications and events participation .........................................................................................36

4.3. Collaboration with other EU Groups and Projects....................................................................47

4.4. Exploitation of Project’s foreground ...........................................................................................48

5. Project Details ...................................................................................................................... 49

5.1. Meeting Metrics...............................................................................................................................50

6. References ............................................................................................................................ 51



1. Project Context and Objectives

The goal of the CogNet project was to make a major contribution towards autonomic management

of telecoms network infrastructure through the use of available network data and applying

Machine Learning algorithms to yield insights, recognise events and conditions and respond

correctly to them. The project goal was to develop solutions that provides a higher and more

intelligent level of automated monitoring and management of networks and applications, improve

operational efficiencies and facilitate the requirements of 5G. The project conducted and exploited

leading research in the areas of data gathering, machine learning, data analytics and autonomic

network management. The ultimate objective was to enable the larger and more dynamic network

topologies necessary for the 5G networks, improve the end-user QoS, and to lower capital and

operational costs through improved efficiencies and the use of node, link and function

virtualisation. To realise this, and to create this value, the multi-stakeholder CogNet consortium

identified a number of key project objectives; each of which is associated with a distinct set of

innovations.

Objective 1: Research and develop a system of data collection from network nodes that involves

pre-processing data to allow the node classify the data it generates and identify the most important

and irregular data for submission to network management while filtering routine and regular data.

This is an important step in the development of scalable network management as it dramatically

reduces the scale of data required to be processed centrally.

Objective 2: While working on the principles of a self-organising network, research and develop,

within existing policy management frameworks, a system to allow network nodes to self-manage

based on their available data while escalating higher importance issues to central network

management.

Objective 3: Apply Machine Learning algorithms to develop a system of service demand prediction

and provisioning which allows the network to resize and resource itself, using virtualisation, to

serve predicted demand according to parameters such as location, time and specific service

demand from specific users or user groups. This is achieved while optimising performance and use

of available network and VM resources while minimising overall energy requirements and costs.

Objective 4: Apply Machine Learning algorithms to address network resilience issues. This includes

using Supervised ML to identify network errors, faults or conditions such as congestion at both a

network wide and a local level and automatically taking mitigating actions to minimise overall

impact.

Objective 5: Use anomaly detection algorithms to identify serious security issues such as

unauthorised intrusion or fraud and liaise with autonomic network management & policies to

formulate and take appropriate action.

Objective 6: Develop a number of demonstrable applications using real-world data gathered via

current 4G network nodes which demonstrate the core project innovations, and serve to highlight

the exploitation potential of CogNet. The applications will include tests to demonstrate the



potential improved performance and capacity that can be achieved by utilising the CogNet

algorithms over conventional approaches used in today’s Network Management Systems.

The project consists of seven work packages, each of which are summarised here:

WP1 is responsible for all project administrative, technical coordination, innovation

management, and quality management. WP1 has a series of complementary, yet dedicated,

tasks each of which focuses on a particular management aspect of the project. In addition

to administrative and technical project coordination, this WP deals with the management

of project innovations, including IPR, with the inclusion of a dedicated Innovation Manager

role – closely aligned with this activity is the exploitation and dissemination of project

results (managed by WP7). WP1 provides oversight on all project activities from the

perspectives mentioned above, as detailed in the WP description tables.

The overall goal of WP2 is to identify the project requirements, scenarios and use cases

which will drive all project technical activities. A set of initial scenarios will be developed by

the project team, and these will be used as the basis for our requirements, focusing on

business, stakeholder and technical aspects. Additionally, this work package will specify all

technical decisions and architectural viewpoints of the CogNet system.

WP3 will focus on the development and adaption of Machine Learning algorithms to filter,

classify and develop insights into the data that will be used to fulfil the use cases being

researched in WP4 and WP5.

WP4 and WP5 represent some the core applications of the CogNet project. WP4 will focus

on the application of Machine Learning to Resource Requirement Prediction and Efficiency

for Telecoms Networks with the particular application being the autonomic management

of resources for Network Function Virtualisation.

WP5 will focus on the application of Machine Learning to Security issues (including fraud

detection, intruder detection and subversion of machine purpose) and Network Resilience

(Error detection and correction, performance degradation and correction). This research

will also feed into applications in Autonomic network management.

The objective of WP6 is to integrate all developed software components and to carry out

test and validation of same. WP6 also focuses on integration of the core CogNet system

with complementary technologies with a particular focus on data visualisation, and on the

development of a number of exemplar demonstrators to highlight the value of the key

CogNet innovations.

Finally, WP7 is dedicated to the dissemination and exploitation of project results.

Dissemination and exploitation is a key element in the project. The objective is to reach out

to relevant communities of stakeholders through a comprehensive dissemination and

communication strategy. In parallel, the project outputs will be exploited through a series

of exploitation activities – as set out in the individual partner exploitation plans.

The CogNet project ran from July 2015 until December 2017 and successfully achieved all of its

proposed objectives.



2. Main Scientific and Technical Results

The main scientific and technical results for the CogNet project is broken up on a work package

basis and captures the following for each work package:

Problem description including the State of the art.

Solutions detailing what innovations are included.

Overall project objectives met by the solutions.

2.1. WP2 - Requirements and Architecture

Figure 1 WP2 tasks’ composition, methodology and outputs.

CogNet WP2 entitled Requirements and Architecture is responsible for the following three tasks,

illustrated in Figure 1:

1. Task 2.1: Identify the Use Cases and Scenarios of the CogNet project that can illustrate the

impact of the advancements of network management in 5G in some real life scenarios.

2. Task 2.2: Model and design the technical and business requirements for the CogNet

project, covering the representative set of usage scenarios and arranged in a hierarchy

supported by a CogNet information model.

3. Task 2.3: Engineer the high-level architecture of the CogNet system as a harmonious set of

services, service components and configurations that meet the requirements in all

representative deployment domains.

Figure 1 illustrates with yellow the main outputs of WP2, which are detailed in the following

paragraphs.



Figure 2 Final set of CogNet challenges, use cases and scenarios.

The initial efforts in WP2 concentrated on identifying and defining a set of challenges, use cases

and scenarios and their requirements, related to the 5G network, specifically from a network

management perspective. Deliverable 2.1 introduced six use cases of CogNet based on the

challenges of the future 5G network management, such as network resource utilization, network

performance degradation, and energy efficiency. The use cases explored in this project were: (1)

Situational Context, presenting how the system will handle exceptional situations due to external

environmental conditions which cannot be directly detected within telecommunication systems.

(2) Just-in-time Services, referring to how cognitive network management techniques will enable

the reduction of creation and deployment time for network services in 5G. (3) User-Centric Services,

moving towards a richer and more complex service catalogue, with the capacity of tailoring services

to the particular user’s needs. (4) Optimized Services in Dynamic Environments: enabling the

network to be deployed, scaled and migrated with ease and speed unheard of in today’s networks,

specifically by relying on the virtualization of the network functions. (5) SLA Enforcement, handling

in an automated and efficient way the level of service guaranteed to a user or service by the

network operator. (6) Collaborative Resource Management, where both the network and the

applications at both endpoints exchange metadata about the network flows in order to improve

network conditions and user experience. Furthermore, Deliverable 2.1 introduced eleven initial

scenarios pivoting around the above use cases in order to facilitate more specific research

questions of high impact value in a real life situation. These were reduced to seven scenarios in

Deliverable 2.2. The scenarios range from large scale events prediction and urban mobility

awareness to massive multimedia content consumption. The challenges, use cases and scenarios

identified in CogNet are presented in Figure 2. Furthermore, their associated requirements were



presented in Deliverable 2.2, which included an analysis of the functional, non-functional and the

business related requirements from the various viewpoints: (i) 5G scalability - increasing demands

for higher performance and better quality of user experience in the 5G network, the widespread

generation of big data via the 5G network, security and privacy concerns, data ownership; (ii) 5G

autonomic network management, and last but not least (iii) 5G sustainability. Moreover, Deliverable

2.2 illustrated how the proposed architecture can serve in delivering the CogNet final set of

scenarios by presenting the sequence diagrams of each scenario and thus the communication flow

along the components involved from the architecture.

Figure 3 CogNet Architecture Overview.

The CogNet project has developed solutions integrating machine learning and Software Networks

to provide a higher and more intelligent level of network management to ensure quality of service

(QoS), improve operational efficiencies and reduce operational expenditure of 5G networks. To

achieve this goal WP2 focused on engineering a high-level architecture of CogNet bringing a

cognitive solution to NFV/SDN management that aims to tackle the challenges in the area (Buda,

et al., 2016). The overview of the CogNet architecture is presented in Figure 3. Compared with

several related architectural frameworks that handle 5G network management, such as (Sanchez,

et al., 2015) (Jiang, Feng, & Qin, 2015) (Jeon, Corujo, & Aguiar, 2015), the proposed architecture is

enhanced by both batch and real-time machine learning solutions to enable much more flexible

and dynamic networks, which can scale horizontally or vertically to handle various 5G scenarios.

The CogNet architecture (Xu, et al., 2016) aims to complement the NFV reference architectural

framework of European Telecommunications Standards Institute (ETSI) (ETSI, 2012), in which

hardware resources are orchestrated and managed, with machine learning capabilities towards an

automated network management solution. The state and consumption records on the hardware

resources are gathered in real-time from multiple functional blocks constituting the layered

architecture. The collected records will be processed by the CogNet Smart Engine (CSE) or

Lightweight CSE (LCSE) periodically or in (near) real-time, to create insights from telecom data or

to recommend policies best matching the network management goals. Real-time analysis is one

of the core contributions of this work. Such a capability is crucial to 5G network management since

it aims to provide immediate response to changes. Furthermore, WP2 abstracted the underlying

CogNet Solution

Network Management Existing Solutions

NFV/SDN-based Environment

Data Collector

Policy Engine

DataStream

ScoresPolicies

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services supported by CogNet through the portfolio of CogNet services presented in Deliverable

2.2, which encapsulate the core work developed in CogNet into a set of services, such as location-

based services, and quality assurance services. Multiple services can be selected and integrated

based on the requirements of an operator and the availability of data. Data services are used to

import and process the data required by the machine learning modules. Machine learning services

provide the core predictive functionality and the planning services orchestrate the predictive

services for action recommendation and policy implementation. In addition, Deliverable 2.2

introduced the associated CogNet information model, which illustrates the core information

captured by the CogNet architecture. This clarifies the information expected to flow between the

CogNet architectural components and their corresponding interfaces.

Finally, the deliverables of WP2 served as a guideline to identify the research questions and their

solutions for the other work packages. Based on the final identified use cases and scenarios,

CogNet proposed candidate solutions on the supporting CogNet architecture to address them.

These solutions materialized into a set of associated demonstrators which are presented in WP6.

2.1.1. Objectives considered by this work package

The main objectives considered and met by the WP2 are:

Objective Coverage Description

Objective 1 The CogNet architecture has dedicated components for data collection and pre-

processing (such as filtering, cleaning, transformation). Furthermore, the

architecture supports the streaming of real-time and offline data that passes

through these components and if necessary through additional more

sophisticated pre-processing blocks such as feature extraction depending on

the requirements before being passed to the Batch or (Near) Real-time

Processing Engine for analysis. Moreover the Data services were designed to

handle the data gathering, preparation and dimensionality reduction aspects

related to this objective.

Objective 2 The Policy Engine within the CogNet architecture supports the recommendation

of actions to the MANO block based on the Machine Learning insights gathered

from the particular environment under analysis. Moreover, the Planning services

were designed to support the actions recommendations and policies

implementation.

Objective 3 The Machine learning services were designed to support core predictive

functionality. Moreover, most scenarios focus on the application of Machine

Learning algorithms to specific network management issues. For instance, the

Urban Mobility Awareness scenario targets this objective by utilizing a network

demand prediction model considering the users’ mobility in an urban region

and their associated patterns of utilization. In addition, Massive Multimedia

Content Consumption (corresponding to the Media SLA demo) focuses on

service demand prediction and provisioning. Moreover, the requirements were

formulated with additional considerations for Intelligence and associated

Machine Learning KPIs.

Objective 4 The Dense Urban Area scenario and associated demo, built following the

CogNet architecture specifically targets resilience issues and thus this objective

through deep-learning based anomaly detection.



Objective 5 The Dense Urban Area scenario, Massive Multimedia Content Consumption

(corresponding to the Media SLA demo) and Detection and Reparation of

Network Threats have been formulated to consider the underlying performance

degradation detection and their corresponding actions, SLA compliance and

associated policies in case of SLA violation detection, and security threats

identification specifically targeting this objective. Moreover, the CogNet

architecture and information model supports this objective and associated

solution.

Objective 6 The high level architecture design and information model emerged from the

use cases and scenarios defined in Deliverable 2.1 and is leveraged further in

WP6 for the design of the common infrastructure. The architecture thus is the

underlying pillar supporting the demonstrators built in this project.

Table 2-1 Coverage of CogNet Objectives in WP2

2.2. WP3 – Advanced Machine Learning for Data Filtering,

Classification and Prediction

The prospective 5G networks will provide services to a myriad of diversified devices that are

constantly exchanging massive volumes of data according to their communication needs for a

plethora of various applications. To function smoothly without interrupting the provisioning of

their services, while being at the same time cost-efficient and secure, the networks will require

effective and highly responsive management techniques, adapted as much as possible to the

specific situation. Manual management of these networks by means of a set of rigid predefined

rules or policies does not scale. Therefore, the prospective 5G networks should be capable of highly

adaptive cognitive self-management.

In CogNet, we focus on a wide variety of use cases and scenarios to tackle challenges of the 5G

networks. In particular, we expect a sharp increase in the network consumption, both quantitatively

(large number of connected devices in the Internet of Things paradigm) and qualitatively (complex

typologies of users and devices in a network, different and constantly changing types of request).

To deal with this complexity, CogNet proposes dynamic solutions to network management based

on machine learning, implemented within the framework of CogNet Smart Engine. Given the variety

of addressed scenarios, the CogNet Smart Engine supports different types of machine learning

models. All these models have been optimized to operate in the Big Data setting, providing scalable

and robust processing of large amounts of data. Work Package 3 focuses on design and

implementation of algorithms within the CogNet Smart Engine as well as integration and

adaptation of off-the-shelf state-of-the-art machine learning modules. Below we discuss the WP3

objectives and accomplished results.


This Work Package is responsible for studying, designing and implementing new machine

learning (ML) algorithms for network/cloud management. It was designed to follow four main

research lines:



Application of the latest result of the statistical learning theory, such as kernel methods

and structured output spaces for modelling the interaction between network nodes.

Domain adaptation methods for enabling the use of supervised techniques in the

network environment, where the high variability of data requires a high level of

abstraction such that automatic classifiers can effectively work on unseen input.

Unsupervised and semi-supervised approaches for helping the domain adaptation ability

of the classifiers (thus they can work in different conditions).

The use of the above methods for prediction tasks using time series.

In addition to the novel directions above, this WP was expected to take care of the application of

traditional and well assessed best practices of ML to the classification/regression/prediction tasks

in the 5G domains:

Processing the raw data, harvested from distinct sources, for transforming it in a suitable

format for ML algorithms.

Feature engineering and extraction from the raw data.

Setting the right parameters for the different ML algorithms, applying feature selection

techniques to reduce space dimensionality and thus improve efficiency, filtering

inconsistent (and so useless) training examples.

To determine the strength and weaknesses of the ML models, i.e., to ensure quality, each model

was supposed to be tested according to efficiency and accuracy. Finally, WP3 delivers the ML

models to WP4 and WP5, which further refine, specialize and adapt them for their test case.

While the output of WP3 contributed to all the project objectives listed in Section 1-Project

Context and Objectives above, it is particularly important for the successful achievement of

Objectives 1, 3 and 4.

2.2.2. Achieved Results

Work Package 3 is one of the central building blocks of the CogNet solution, with 7 partners

contributing with about 120 person months. This effort has been invested into creating a reliable

ML-based technology for 5G network management, achieving all the declared objectives. The main

output of WP3 is presented in detail in four deliverables (M8, M16, M22, M26). Deliverable D3.1

outlines the technology to be developed and the main issues to be addressed, Deliverables D3.2

and D3.3 release the prototypes and, finally, Deliverable D3.4 presents an extensive evaluation

report. Below we provide a brief summary of the developed solution.

2.2.2.1 CogNet Smart Engine

All the ML components developed within the work package form part of the CogNet Smart Engine

(CSE). The CSE, designed following the architectural specifications proposed within Work Package

2, delivers a principled solution to a wide variety of ML tasks arising within 5G scenarios. All the

WP3 partners have contributed to the design and implementation of the CogNet Smart Engine,

producing individual components or adapting state-of-the-art ML solutions to 5G tasks through



extensive optimization and, in particular, parallelization to improve the CSE scalability and

robustness in the Big Data context.

In total, the CSE comprises 20 different components, addressing various ML tasks. They have been

extensively evaluated on several datasets, either common benchmarks or generated specifically

within the project (see Deliverable D3.4 for details). All the components run on the same

infrastructure and can communicate with each other through the CogNet infrastructure as

developed in WP6.

Table 2-2 presents CSE components from the deployment perspective, showing the 5G use cases

and scenarios relevant from D2.2. The modules that were selected to be included or used as a pre-

processing step in CogNet demos are highlighted with a light blue background.

Table 2-2 WP3 CSE components and their application in the 5G context.

Component Name

(Developer)

5G Application (Relevant WP)

CogNet Demo (If applicable)

WP deployed to

(Deliverable reported

in)

Spark IterFS (UPM) Feature selection for 5G Tasks (WP4)

This module was used as a pre-processing

step in the MMCC (Traffic Classification)

for feature selection.

WP3 (D3.2, D3.3)

PICS/PPICS (UPM) Feature selection for 5G Tasks (WP4)

These modules were used as a pre-

processing step in the MMCC (Traffic

Classification) for feature selection.

WP3 (D3.2, D3.3)

NetSpark (UNITN) Automatic feature engineering for 5G

tasks (WP4, WP5)

WP3 (D3.2, D3.3)

ML4MQ (Orange) SLOs breaches identification at service

level, Throughput prediction, SLA

enforcement (WP5)

WP3 (D3.2, D3.3)

Connected-cars-ml:

Mobility Pattern

Prediction (VICOM)

Mobility patterns for connected cars

(WP4)

This module is used in the Connected

Cars demo.

WP3 (D3.3), WP4 (D4.2,

D4.3, D4.4), WP6 (D6.1,

D6.2)



Anomaly Detection

Engine - ADE (IBM)

Performance degradation detection

(WP5)

This module is used in the Dense Urban

Area demo.

WP3 (D3.3), WP5 (D5.3),

WP6 (D6.2)

NDP (IBM) Network demand prediction (WP4)

This module is used in the Urban Mobility

Awareness.

WP3 (D3.3) WP4 (D4.3)

NTC (IBM) Network traffic classification (WP4) WP3 (D3.3)

RCMR (IBM) Recurrent crowd mobility recognition for

network consumption analysis (WP4)

WP3 (D3.3)

PSCEG (UPM) Clustering of network metrics (WP4)

This module was used in the preliminary

steps to explore data clusters in MMCC

(Traffic Classification) and Noisy

Neighbours.

WP3 (D3.2)

ForwardEC (Orange) Prediction of network metrics (WP4) WP3 (D3.3)

FunCo (Orange) Network resource management (WP4,

WP5)

WP3 (D3.2, D3.3)

FunPrev (Orange) Forecasting anomalies in networks (WP5) WP3 (D3.3)

Distributed Application

Performance Optimizer

(VICOM)

Network resource management (WP4,

WP5)

This module was used in the preliminary

steps in MMCC.

WP3 (D3.2, D3.3)

SPARK-TK: Machine

Learning for Structural

Input (UNITN)

Explicit modelling of network

configurations and other structural data

(WP4)

This module is used as a preprocessing

step in the Large Scale Events.

WP3 (D3.2) WP4 (D4.2)

LSSVM-SP: Structured

Output Prediction

(UNITN)

Explicit modelling of network

configurations and other structural data

(WP4)

WP3 (D3.2, D3.3), WP5

(D5.3)



ModelsDiff: Log-based

Behavioral Differencing

(Nokia)

Network performance evaluation (WP3) WP3 (D3.3)

TCDC (IBM) Model evaluation and selection (WP3) WP3 (D3.1)

Stream-Cluster (VICOM) Network resource management (WP4,

WP5)

WP3 (D3.2)

Stream-Regression

(VICOM)

Network resource management (WP4,

WP5)

WP3 (D3.2)

2.2.2.2 Scientific Output

WP3 was conceived as a more theoretical and research-oriented work package: within its context,

we have developed novel ML techniques and investigated possibilities for adapting state-of-the-

art ML solutions to 5G problems from a more general perspective. The output of WP3 has then

been further integrated into WP4 and WP5 for more empirical studies. In accordance with this view,

the work package has produced a considerable amount of academic dissemination: to ensure the

high quality of the CogNet technology, we have always stayed in touch with the research

community, paying close attention to the latest ML developments as well as presenting and

discussing our results. In particular, WP3 has produced the following:

40 conference papers, including several top-tier venues, winning 2 best paper awards,

2 journal papers and 2 invited journal papers in preparation,

1 book chapter and

4 invited talks.

These achievements are reported in more detail in WP7 deliverables.

One especially important WP3 achievement is the organization, in collaboration with WP5, of the

NetCla Traffic Classification Challenge, collocated with the ECML-PKDD 2016. The challenge,

providing an open source benchmark for traffic classification studies, has attained a considerable

attention of both academic and industrial communities, with 25 participants from all over the world

submitting their solutions. This competition has a two-fold impact on the 5G industry: first, it

presents a reliable setting for evaluating prospective technologies and second, with a wide variety

of high-quality ML solutions submitted by the participants, it provides insights on state of the art

in machine learning for 5G domains.



2.3. WP4 – Network Resource Management

Work Package 4, named Network Resource Management, aimed to research and develop smart

real-time analytics techniques for optimizing the performance of Virtual Network Functions (VNFs)

in terms of energy efficiency, quality of service and resource elasticity by means of orchestration

mechanisms. More specifically, WP4 addressed the limits of existing analytics and machine learning

techniques for software network environments.

WP4 goals and activities have been driven by CogNet Objective 3 that proposed the application of

Machine Learning algorithms to develop a system of autonomous service demand prediction and

provisioning according to parameters such as location, time and specific service demand from

specific users or user groups. Additionally in the context of this objective, WP4 has investigated

how to optimize the performance of available network and infrastructure resources jointly with the

minimization of the overall energy requirements and costs.

To this end, four tasks were initially proposed: 1) Task 4.1 Data gathering and pre-processing, 2)

Task 4.2 Techniques for prediction in NFV scenarios, 3) Task 4.3 Smart self-managed NFV

ecosystem for optimal elasticity and energy efficiency, 4) Task 4.4 Online network layer traffic

classification and prediction, and Task 4.5 Integration and correlation of network data and NFVI

events.

Given that early identification of use cases and scenarios was provided by work package 2, an

alternative organization based on use cases and scenarios was adopted in WP4 activities instead

of the normal task oriented approach. The goal of this new approach was to maximize the number

of research topics under exploration and to foster rapid and flexible collaborations among small

groups of partners. Therefore, WP4 activities were carried out in parallel and following six different

research avenues for different use cases and scenarios. In the following subsection we show the

specific objectives considered by this work package, classified based on the chosen use cases and

scenarios.


2.3.1.1 Optimized Services in Dynamic Environments (Use Case)

With the advent of Network Function Virtualization (NFV) and Software Defined Networking (SDN),

Network Functions (NF) will no longer be tightly coupled with the hardware they are running on.

This flexibility entails certain challenges when it comes to managing the infrastructure resources.

Among these, a recurring issue is the one known as the “Noisy Neighbour”. This problem arises

when various virtual machines compete for the same physical resources, causing performance

degradation.

In this work we have built a controlled cloud environment to reproduce the “Noisy Neighbour”

effect in order to collect a sufficient set of labelled examples for training supervised machine

learning models that can detect this issue. Initially, we designed and trained several classifiers based

on traditional state-of-the-art machine learning techniques such as random forest and support

vector machines obtaining decent accuracy results (around 0.95). After that, and in order to

increase the accuracy of the models, we proposed more complex models based on deep



convolutional neural networks. As expected, when these deep models were trained in a big data

regime (i.e. using hundreds of thousands of labelled examples), the accuracy levels improved

significantly when compared to traditional ones even in the presence of complex scenarios. In

addition, we trained a regression model to predict high level KPIs that are difficult to compute

within the cloud infrastructure such as the jitter values that an end-user can be experiencing during

the reception of a multimedia stream. After testing several machine learning models, the ones

based on fully connected deep neural networks were revealed as being more accurate.

Preliminary research results of this work were presented in ESANN-17 conference and

complementary results (to appear in 2018) were submitted to Plos ONE journal (ranked in the first

quartile of the prestigious ISI/JCR index). Finally, we plan to submit an additional publication

containing the final results of the research done in this use case to a top-tier journal specialized in

the cloud and telecom domains.

2.3.1.2 Traffic Classification (Use Case)

The use of application-level encryption is becoming more and more prevalent in today’s Internet,

making it difficult for network service providers to characterize the traffic that traverses their

infrastructure. In addition, privacy issues preclude them from inspecting the packet payload (i.e.

application data) for classification purposes. Therefore, new methods for traffic classification and

characterization are being sought by network service providers. Under the hypothesis that

adequate mappings can be found using traffic features extracted from the transport and network

layers and below, we have investigated the application of machine learning models to help network

managers in the described scenario.

We have built a controlled environment entitled Mouseworld that generates realistic network traffic

data and can label automatically big amounts of traffic flows into pre-configured traffic categories.

Using these labelled traffic datasets we have trained traditional and deep classifiers to generate

accurate traffic characterizations (in the range of 0.85 and 0.95 of accuracy). Two different models,

forensic and real-time have been designed, trained and tested. Firstly, forensic models were

produced using random forest and deep fully connected artificial neural networks. Secondly, we

designed real-time models that can produce an accurate classification even when a small number

of packets have been received (e.g. 5-10 packets). These models were based on deep convolutional

neural networks and utilized as input time series of the features of a flow jointly with context values

obtained from concurrent flows. The results obtained at the end of this work confirm that the

researched techniques outperform state-of-the-art deep packet inspection systems with the

advantage of not needing to inspect application data.

Having recently finalized our last experiments, we plan to submit the research outcomes of this

work to a top-tier journal specialized in the telecom domain.

2.3.1.3 SLA Enforcement (Use Case)

SLA (service Level Agreement) refers to the level of service guaranteed (often through contract) to

a user or service by the network operator. With many modern IT services requiring different levels

of guaranteed bandwidth, latency and priority over other traffic, SLAs have become more important

and more differentiated depending on the nature of the service. For example, some types of



services may need to take priority if there is contention for resources such as emergency service

communications, and in other SLA and situations, security may be the critical factor, so special

authentication of the sender and receiver, encryption and or selective routing of data through the

network may be a key issue that is addressed through the SLA.

The activities in this work target to set up an autonomic SLA management relying on Machine

Learning techniques to predict SLOs (Service Level Objectives) breaches. The use case chosen to

deploy the cognitive SLA enforcement was a streaming service running on SDN and NFV

infrastructure, in which a testbed was setup based on real cloud infrastructure where a streaming

service was running.

For the identification of the SLO breaches we focused on two types of Artificial Neural Networks,

namely Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN). In particular

we used a derivative of RNN called Long Short Term Memory (LSTM) for evaluation and comparison

purpose.

2.3.1.4 Network Demand Prediction (Scenario)

Prediction of demand in a network is an important part of autonomic network management. If a

5G operator can reliably forecast the demand in an area resource allocation can be done more

effectively by changing policies dynamically in CogNet infrastructure. This will lead to reduced

operation cost for operators and increased user satisfaction. A new approach for increasing the

network demand prediction accuracy is using the functional regions of a city. Functional areas in

dense urban areas change dynamically creating network demands that are dependent on both

location and time. Improvements in big data storage and processing capabilities enable us to build

location based prediction and forecasting models that take dynamic functionality of a region into

account.

The network demand prediction (NDP) module researched and developed in WP4 forecasts the

median throughput in a dense urban area for a flexible time unit. Forecast of network demand aids

a 5G network operator in planning for the changes in user demand. In addition, this module can

help service providers in identifying potential bottlenecks in their service ahead of time to achieve

higher operational performance.

The research results of work have been accepted to be presented in PAKDD-18 conference and are

under revision in the IEEE Transactions on Knowledge and Data Engineering.

2.3.1.5 Large Scale Events (Scenario)

This scenario employed machine learning to analyse traffic demand patterns based on network-

external evidence of social behaviour, extracted from social media and streams. The obtained

contributions evolve around applying machine learning algorithms for the network service demand

prediction, thus targeting the Objective 3 of the CogNet project.

Within this scenario, we have developed novel algorithms and models for different aspects of the

demand prediction problem. Thus, we have first created a prototype, publicly released within

Deliverable D4.2, for clustering Twitter data with the purpose of further events extraction. We have

then conducted exploratory analysis, correlating the mobile network consumption statistics

provided by TruConnect LLC and the Twitter/Foursquare data, contributing the respective software



to Deliverable D4.3. Finally, we developed a machine learning model for traffic prediction

combining social media and historical network load data (see D4.4 for details).

The scientific output of this work has been presented at top-tier conferences such as CIKM

2015/2016, NAACL 2016, ACL 2017, EACL 2017, ECML-PKDD 2017, and in Journal of Natural

Language Engineering (to appear in 2018).

2.3.1.6 Connected Cars (Scenario)

In 5G systems there is a need to discover new wireless technologies between the handset and the

base stations that can handle very high-speed transmissions. Recently, antennas communicating

in Terahertz-band to achieve data rate that is close to Gbps have been proposed for small cells

with small coverage areas. The considered scenario, entitled Connected Cars, investigated how to

dynamically adapt mirrors to optimize 5G coverage in small cells and in particular researched

machine learning based methods for estimating the optimal orientation for each reflecting mirror

installed in a 5G small cell covered by one antenna based on the location of nodes (vehicles in this

case).

The main achievements and outcomes of the Connected Cars scenario are the following:

A system for collecting and pre-processing floating car data was developed.

A self-organised network management system was developed, which employs a policy

manager to broadcast new network configurations.

Two machine learning algorithms were developed. First, a Genetic Algorithm, and then a

convolutional neural network to obtain a faster online computation. These algorithms

were integrated into the CogNet’s Common Infrastructure.

The module developed in WP4 is part of a demonstrator that predicts the future vehicle

mobility pattern and mitigates the effect of vehicle traffic congestions reconfiguring the

network resources.

A demonstrator was developed, putting together the module developed in WP4, and the

mobility pattern prediction module developed in WP3. This demonstrator was integrated

in the Common Infrastructure developed in WP6. A floating car data dataset was

generated using a simulator of urban mobility (SUMO).

The research results of this work have been submitted and published in top-tier journals ranked in

the first and second quartiles (Q1, Q2) in the prestigious ISI/JCR index such us Journal of Real-Time

Image Processing, IEEE Transactions on Mobile Computing and IEEE Transactions on Broadcasting.

2.4. WP5 – Network Security & Resilience

WP5 is the work package handling the development of extensions of the management plane of

software networks with added value machine learning mechanisms enabling the preparation and

the prophylaxis for exceptional events.

During the project, the software network environment, initially based on the NFV and SDN

technologies as standardized by ETSI and by ONF evolved towards more diversified environments

while at the same time, a scope reduction was observed.



Seen from the perspective of the end of the project, the software networks on top of cloud

infrastructures with or without NFV are becoming a norm in network developments and

deployments. From this perspective, WP5 mechanisms of resilience and security used for the

software networks and managed by the machine learning mechanisms are still very new, reaching

initial prototyping and products. Because of this, the results of WP5 are becoming essential for the

immediate next stage of development of software networks. This is underlined by the interest of

the operators and of the vendors in the results of the project as well as in the continuation of the

research and innovation activities towards other projects with higher TRL, closer to production.

Furthermore, SDN passed the expectancy gap and is becoming now an important constituent

technology of the 5G networks as well as of the dynamic data center networking solutions. As a

result, SDN solutions are currently maturing in the new generation of software products.

Security and resilience are usually features which come after the initial functional components of

the products are developed. Because of this, the results of WP5 are currently highly relevant not

only from the perspective of how machine learning can help with the management, but also from

the perspective of how remote decisions could be taken based on active system information and

how mitigation actions can be transmitted in the highly distributed software network environment.

2.4.1. WP5 Objectives and Approach

Concentrating on the security and the resilience of the software network functions, WP5 had

provided different objectives, which albeit being equally represented within the description of work

and added in tabular form underneath, evolved during the project and changed proportions mainly

due to the interest of the industry in specific areas.

WP5 aimed at reaching an appropriate set of security mechanisms based on the machine learning

addressing the dynamic network function environment of the software networks on top of cloud

(Objective 1). Although being a single objective, due to the very large interest of the industry the

project resulted into three distinctive prototypes each considering a specific angle of security. Even

with this, there still a very large potential to extend these prototypes and to further apply them

towards real-life deployments, especially because of the lack of proper security mechanisms still

noticeable in the cloud deployments.

For the end-to-end reliability of the network (Objective 2) a more pragmatic approach was taken,

specifically concentrating on the SLA maintenance for the specific services as seen from the

perspective of the subscribers. This approach is aligned with the NFV approach towards software

services where the management of the system has to maintain the SLA towards the subscribers

while adapting in an automatic and transparent way.

For the high availability (Objective 3) the approach taken was to integrate anomaly detection

mechanisms together with the high availability mechanisms of the software networks in such a way

as to be able to predict failures and reduction of performance and to adapt the system dynamically.

A final objective of the work package (Objective 4) aimed at providing an integration between the

machine learning mechanisms and the network management mechanisms for specific

deployments of specific networks, thus representing the convergence point between the



developments coming from WP3 with the specific security and resilience mechanisms of the

network.


Objective 1

Large scale distributed security mechanisms addressing dynamic network

functions deployments, combining the information available at virtual network

functions level with administrative information and with the virtual network

fabric. WP5 addressed these items by investigating and developing a

distributed analytics framework for prophylactic preparation against various

security threats.

Objective 2

Machine Learning for dynamic network functions placement from the

perspective of providing a reliable end-to-end network service including the

provisioning of a distributed mechanism for ensuring high availability of the

functional processing, though this providing the means for automated service

administration.

Objective 3

Machine Learning and Real-time analytics for dynamic establishment and

adaptation of the network towards the momentary resilience requirements

ensuring the high availability of the network connections between dynamic and

elastic scaling components.

Objective 4:

Integration and harmonization of the end-to-end reliability mechanism by

integration of the local analytics (i.e. in each of the network tenants), end-to-

end service analytics according to the service specific device usage patterns

through this providing a predictable and replicable end-to-end reliability

mechanism

The WP5 work was split into three distinctive phases, each enabling the maturation of the

developed technologies throughout the project. Each of the phases was reflected in the form of a

deliverable as well as when the case in the form of prototypes, implementations and evaluations,

according to the initially proposed work plan:

1) Initial conceptualization – as reflected in D5.1, the main goal was to provide the initial

design of an integrated SDN/NFV ecosystem with machine learning algorithms through

this providing the means for a more robust network ecosystem for security and reliability

compared to the existing systems, which do not include the machine learning. The work in

this phase was aligned and influenced the architectural developments of WP2. As proposed

by the description of work, this deliverable achieved the initial system design from the

perspective of integrating machine learning functionalities within the SDN/NFV ecosystem

in the precise use cases that are addressed in WP5. The deliverable followed a holistic

overview of all the possible issues for security, resilience and long duration services aiming

to provide a pallet of possible options where machine learning is supposed to provide a

better performance than the current solutions.

In this initial conceptualization phase a large number of mechanisms for a very large

number of security and resilience problems were proposed with the aim of covering the

specific technologies and being able to offer towards the interesting parties different

directions of development.

2) First phase and second phase of implementation – as reflected in the D5.2 and D5.3 a set

of prototypes were implemented following the initial conceptualization phase. Albeit a very



large number of security and resilience scenarios may have been considered within the

project, only the most significant ones from the perspective of the partners, especially the

operator as well as the third party industry customers of the work package members, were

developed and assessed. This allowed for the concentration of the resources towards

significant results with larger impact as well as towards underlining where the machine

learning is producing major advantages compared to the other policy based only solutions.

Although the prototypes were not specifically designed to be integrated into the common

infrastructure of CogNet an initial alignment with the WP6 activities as the integration

process was followed.

Furthermore, as WP5 mainly concentrated on the development of end-to-end prototypes

for showcasing the advantages brought by machine learning for network management, a

dual approach was taken:

i) the adoption of simple existing machine learning prototypes, through this bringing

the first understanding of machine learning techniques to partners specialized in

network management

ii) adoption of WP3 algorithms and adaptations from WP4 of these algorithms

enabling the machine learning specialized partners to understand and to adapt

their developments to the needs of network management.

Two separate implementation phases were considered in order to be able to have a middle

check-point on the developments as well as a comprehensive report on them, with this

minimizing the risk of having a reduced number of results within the work package.

3) The evaluation phase – in the evaluation phase (as reflected in D5.4) the prototypes were

measured and assessed towards underlining the key added value of the machine learning

techniques within the network management for network security and resilience. As

expected in this phase, some additional developments were needed within the prototypes

to be able to properly respond to the evaluation tests and requirements, adaptations which

could not be visible before the evaluation campaigns are started.

The approach taken enabled the proper integration of the specialists from two very distinctive

domains: network management and machine learning to the direction of security and resilience.

From this perspective, the work package achieved its goal and further developed research

relationships which will enable the further development of the prototypes towards trials as well as

the further development of the research and innovation area of network management for software

networks.

2.4.2. Achievements of the work package

The work package achieved its goals specifically by the development of a comprehensive approach

towards the security and resilience within the software network environments and by implementing

and evaluating selected testbeds which evolved during the projects towards the most interesting

directions of research in the domain.



The testbeds showcase the advantages of machine learning for network management in the

directions of subscriber communication security, network security and network reliability and

performance. The list of the testbeds and components described in this deliverable is illustrated in

Figure 4.

Figure 4 – Testbeds of WP5

The end-to-end processing is split into three parts: monitoring, detection and actuation. For some

of the added value management features included in WP5, the same machine learning or the same

actuation is used. This is properly marked through the deliverables.

The following table introduces shortly the description of the testbeds and the value added

demonstrated using the machine learning techniques.

Testbeds Description and added value summary

Distributed

Security

Enablement

The distributed security enablement testbed, albeit addressing a firewall at the

entry of the network has a further actuation implementation of security zones

using SFC based routing, showcasing additionally to the large benefits of using

machine learning for detection of attacks the benefit of dynamic networking

solutions within the cloud infrastructures which allow with a minimal functionality

addition to create different security zones.

This is the main feature of interest of private network administrators which are

still trying to discover how their current security zones are provided by the cloud



infrastructures, one of the main limiting factors of adopting cloud infrastructures

on a very large scale in enterprises.

Honeynet The honeynet testbed considered a classic approach to security, addressing mainly

the functionality of a firewall which is placed at the entry of the network and is able

to detect different attacks.

The provided evaluation results prove that the implementation of machine

learning based firewalls is feasible for a carrier-grade network and that it should

be considered in the next generation of products.

With this evaluation, operators are able to transmit to the vendors of software

firewall components the message that firewalls could be improved by more

dynamic data processing mechanisms and through this to reduce the final cost of

such components.

NFV

Security

Anomaly

Detection

The security anomaly detection testbed made a further step into the direction of

innovative features for security and to deploy a distributed security solution

directly on the forwarding plane of the cloud, through this liberating the firewall

functionality for the initial location into the infrastructure and enabling its

placement between any two connected virtual machines.

With this, security can be dynamically placed within the cloud environment and

could address in a distributed manner (i.e. gradually) different types of attacks.

Instead of concentrating on the placement of the network functions in different

network locations to implement the security zones, the project proved that it is

possible to place the security functionality at data path level and to use machine

learning techniques to address the detection of only specific attacks in a specific

location represents a new approach and new means to implement security in the

cloud/NFV environment using SDN mechanisms.

Resilience

testbed

The performance degradation testbed proved that machine learning represents a

cost-effective means to determine when the performance of a network function is

degrading and through this to predict the performance degradation of the

network service. The proposed mechanism also covers abnormal behaviour (not

only watchdog functionality) and proved that the anomalies can be detected and

mitigated in due time before their effect is noticed by the users of the service. This

represents a new approach to high availability where the specific machine learning

helps to solve the problem of component functionality degradation on the long

term.

Media SLA The Media SLA testbed addresses the performance degradation from the

perspective of the SLA of the subscriber, proving that the same anomaly detection

mechanisms should be used for the detection of the possible SLA degradation and

to mitigate in real time such situations.



Added to this, an initial consideration of the development of an “Experience” framework for

network management was considered, where the machine learning will play a major role to

accumulate the proper insight for the more adequate network management decisions, based on

the proper interpretation of past situations and past action results. Albeit the “Experience”

framework remained only at the conceptual level enabling only the uniformity of the evaluations

of the testbeds towards underlining the machine learning added values, it provides a next step on

how the network management should be considered in highly dynamic and complex environments.

All the testbeds profited on the flexibility provided by the underlying cloud substrate of the

SDN/NFV environment and are using the new provided mitigation mechanisms (e.g. re-routing,

scaling, rebooting of network functions, etc.) to modify the network and the processing of the

specific services according to the decisions taken by the machine learning algorithms. With the

evaluation of the testbeds we have proven that machine learning techniques are feasible to be

used as advanced means for the network management decisions in real-time as needed to be able

to properly use the mitigation mechanisms proposed.

Furthermore, the proposed mechanisms were tested with local analytics, where the machine

learning mechanisms were placed in close proximity to the network functions themselves. With

this, a first evaluation of the algorithms was obtained providing to the reader the means to

understand that the specific features are highly beneficial when placed locally into the network.

However, this may be too complex as costs for real-life network deployments, as the distribution

of such functionality would require large support from the analytics functionality providers. For

assessing a more effective solution towards real-life deployments, most of the testbed algorithms

will be also deployed in the framework of WP6 as part of the common infrastructure, which from a

testbed perspective represents the centralized approach towards the network management. With

this, a second evaluation of the mechanisms is provided as well as the means to integrate them

into a single machine learning network management solution.

Since the start of the project, a very large momentum was seen for the machine learning usage in

performance and security management. From this perspective, WP5 managed to provide

innovation just in time for the needs of the software components providers, cloud providers and

operators to be able to assess in an initial form the benefits of machine learning in network

management and to make their decisions and roadmaps on the further integration towards the

later product integration.

Considering that WP5 has reached its major objectives, that the proposed testbeds showcase a

large number of performance and security related network management directions, all applied to

cloud/NFV infrastructures and considering the research and the development community interest

into the developed solutions, we consider WP5 as being successful, albeit being only an initial step

into the integration of machine learning,

2.5. WP6 – Validation & Integration

This work package has carried out activities pivoting around the following technical results:



Demonstrators

The final goal of WP6 is to provide evidences of the successful results and benefits of using CogNet

results to specific Network Management problems present in 5G networks. To capture all the

expected features from CogNet, WP6 has implemented a set of different demonstrators which

meet the requirements and specific 5G challenges, explored in WP4 and WP5. The demonstrators,

selected from real business plans from partners, generate or use representative datasets to meet

target 5G challenges.

The WP2 established a set of target scenarios accompanied with intrinsic 5G challenges. The

scenarios have been formulated for showing real impact of cognitive network management. Thus,

these scenarios bring specific research questions and illustrate a significant impact in a real-life

context. Hence, the demonstrators are defined on top specific scenarios and named employing the

scenario they target.

The demonstrators and their setup are described in “D63 - Final release of the integrated platform

and performance reports”. The next figure depicts the current WP6 demonstrators of CogNet and

the different partners involved in each of them:

Figure 5 Demonstrators and participants around common scenarios

The network management situations supported by the tools applied on the demos are:

Demo

Network

Management

Domain

Short Description

Follow the Sun Self-healing The tools are able to identify virtual machines

performance degradation when being hosted by a

common physical machine, producing degradation

on the co-located service.



MMCC - Real

Media SLA

Self-healing The tools are able to identify SLA breaches and to

re-scale/reroute VNFs the traffic accordingly to

guarantee a QoS.

MMCC – Traffic

Classification

Self-configuration The tools are able to classify the type of service of

each data flow without inspection of the payload or

the specific ports.

Dense Urban Area Self-healing The tools are able to identify performance

degradation situations on NFVs.

Detection and

Reparation of

Network Threats

Self-healing The tools are able to identify threats and apply new

rules to put threat’s origins in quarantine by NFV

setup.

Connected Cars Self-optimization The tools forecast the cars’ distribution on cells and

apply antenna setup to improve the signal coverage.

Urban Mobility

Awareness

Self-optimization The tools forecast the density of users in a city based

on data aggregation of network utilization and

social networks activity.

Table 2-3 Short Description of Demonstrators

Datasets

The employed Machine Leaning algorithms in the different demonstrators have been tailored to

process specific features from different datasets. The datasets have been extracted from a

representative and realistic setup or retrieved from real connectivity records. In the case of the geo-

binned dataset from WeFi1 , it includes network performance statistics from people surfing on the

Internet in the Manhattan area along 6 months. The datasets and the applicable licensing terms

are described in “D63 - Final release of the integrated platform and performance reports” and “D64

- Final evaluation and impact assessment results”.

Common Infrastructure

A major result of CogNet is the Common Infrastructure. A set of scripts and Ansible playbooks

which allows any network manager to deploy the full CogNet infrastructure ready to capture data,

analyse specific key features and provide actuation suggestion according to the identified or

predicted networking issues and the defined actuation. To host such shared WP6 testbed for all

the demonstrators, we decided to use a commercial Rackspace2 cloud infrastructure, based on

OpenStack technology. This decision aims to guarantee scalability and get independence from

specific setups of the facilities from the partners. The APIs and the set of instructions for its

deployment is being declared initially in “D62 - First release of the integrated platform and

performance reports” and its final specification in “D63 - Final release of the integrated platform

and performance reports”.

Furthermore, to check the flexibility to deploy the common infrastructure on top of different

facilities from different partners, the common infrastructure has been adapted to decouple from

Rackspace technology and APIs, and to enable private network addressing. Thus, the common

1 https://wefi.com/ 2 https://www.rackspace.com/

https://wefi.com/

https://www.rackspace.com/



demonstrator infrastructure can be cast over different virtualized infrastructures where different

use cases and challenges are addressed. The set of instructions for its deployment is being declared

in “D63 - Final release of the integrated platform and performance reports”.

This common infrastructure brings the following objectives: replicability, with a solution which can

be instantiated and the same experiment should be executed over different platforms/test-beds;

integration, shipping a common set of elements, dataflow, SW stack and APIs to be considered and

adopted; and pedagogical, providing a best-practice reference platform for developing cognitive

management solutions with machine learning.

Policy Engine

CogNet project provides the ability to self-heal in reconfigurable dynamic networks by use of policy

based network management actuation for correction and prevention, and for the reconfiguration

of these policies based on the updated knowledge of the Machine Learning about the network

being monitored.

The Policy Engine evaluates the Machine Learning outputs to identify if any violations have

occurred. This evaluation is based solely on the policy document that is directly associated with the

Machine Learning algorithm being executed. If the Machine Learning detects that a particular

situation has occurred the corrective action(s) as specified within the associated policy is deemed

the most appropriate actions to be executed.

From a high level perspective, the Policy Engine has a Kafka interface which takes the pushed JSON

events (with a SUPA ECA document structure), checks a condition on an instance of a policy and

triggers an action message if the condition is satisfied.

Figure 6 High level Policy Engine workflow

SUPA ECA policies will define the behaviour of the Policy Instance for each policy that will be

triggered for each demonstrator application. That programmed actions and behaviours from Policy

Instance have several parameters that can be static or dynamic.

Integration and Testing

Furthermore, in order to get a consistent integration, evaluation and interoperability of the

developed tools in WP6 based on Machine Learning techniques, we have defined a procedure for

all the demonstrators on top of the common infrastructure. To this end, an encapsulation

mechanism has been defined to turn the Machine Learning systems and environments into

modules to be used in the continuous integration and testing system, Jenkins.



Figure 7 [L]CSE Integration and Validation procedure

Performance and Validation Reports

The validation is based on scenarios bringing metrics that can be evaluated to conclude

performance reports. This validation is performed on top of a demonstrator that conducts the

integration and validation activities. This way, the system employed to process signals and data is

common while the monitored network and the services and traffic issues in place are specific to

the demonstrators. This ensures a uniform mechanism for the integration and validation activities

across all the demonstrators. Going further, a significant result is the work done to make the

CogNet Common Infrastructure generic to be deployed on top infrastructures based other cloud

technologies and on private setups.

Summary

The main results from WP6 are compiled in the next table.

Topic Technical Result

Common

Infrastructure

Deployment documented to be replicated in another private or public

OpenStack-based infrastructure. This way the Common Infrastructure can be

instantiated as a commodity in private setups.

Policy Engine The Policy Engine runs SUPA ECA policies with actions that can include

parameters obtained in a direct way from the metadata or retrieved from the

connection to the network topology.

Policy Engine

Monitor

The Policy Engine includes an activity monitor to track the inputs from the

[L]CSE and the eventually triggered actions, being expanded to be used as a

record for testing and validation purposes.

Policy

Actuation

Verbs

A set of actuation verbs and the typical required parameters have been defined

and agreed to cover all the possible actions considered in the different demos.

Docker

Monitor

The different ML algorithms encapsulated and hosted in a Docker Machine are

monitored by Google cAdvisor to track resource usage and performance

characteristics of their running containers.

Demos

Integration

All the demos are integrated with the Common Infrastructure and deploying

them automatically by means of a Jenkins job.

Demos

Testing

All the demos are reporting performance and evaluation tests obtained in the

deployment on the common infrastructure by means of a Jenkins job.

Machine Learning

Common Infrastructure

Jenkins

Testing dataset to be

replayed

Docker Encapsulation &

Kafka/InfluxDB connection Demo logs sent by email



Demos

Actuation

All the demos apply the action to the monitored network, modifying the network

behaviour, or push the action to a dashboard system to provide awareness to

the network manager of an identified or forecasted issue.

GitHub GitHub includes:

- the final design of the common infrastructure for deploying on top of a public

Rackspace infrastructure and on top of a private OpenStack infrastructure,

- the policy engine for dynamic actions,

- a tutorial to deploy a CSE by encapsulating the ML algorithms in a Docker

and connecting appropriately to Kafka queues with compliant SUPA ECA

policies and messages.

- a tutorial to inject data to the Inbound API and to consume the Outbound

API of CogNet Common Infrastructure and generate a log report for validation

purposes

- licensing terms for all the demos

Table 2-4 Summary of technical results


The main objectives considered and meet by the WP6 are:


Objective 1 The implemented common infrastructure includes components for data

collection from network nodes by means of the Inbound API based on Monasca

messaging.

In order to classify and identify relevant features from traffic and network

metrics in a scalable manner, the common infrastructure provides a Docker-

based mechanism to integrate and deploy Machine Learning techniques and

running environment into scalable cloud infrastructures.

Furthermore, the common infrastructure includes two processing modes, Kafka-

based live data flows for real-time processing and InfluxDB-based on-demand

access for batch and training processing.

Objective 2 The policy engine, as a major component of the common infrastructure,

provides policy management and eventually escalates required actions

according to SUPA ECA defined policies.

Moreover, the policies and new data can be dynamically updated by means of

a Kafka bus. Furthermore, the SUPA ECA syntax is flexible enough to define

dynamic parameters.

Objective 3 The different demonstrators from WP6 are intrinsically related to the application

of Machine Learning algorithms to specific network management issues. Some

of them such as Massive Multimedia Content Consumption- Media SLA meets

service demand prediction and provisioning.



of them such as Follow the Sun, Dense Urban Area and Urban Mobility

Awareness target to identify network faults or degradation or conditions such

as congestion at both a network wide and a local level.





of them such as Massive Multimedia Content Consumption- Traffic

Classification and Detection and Reparation of Network Threats explore and

address security issues applying policies to shield from them.

Objective 6 The employed Machine Leaning algorithms in the different demonstrators have

been tailored to process specific features from different datasets. The datasets

have been extracted from a representative and realistic setup or retrieved from

real connectivity records. In the case of the geo-binned dataset from WeFi3 , it

includes network performance statistics from people surfing on the Internet in

the Manhattan area along 6 months.

Table 2-5 Coverage of CogNet Objectives in WP6

3 https://wefi.com/

https://wefi.com/



3. Risks and mitigation actions

During the course of the CogNet project it was vital that any risks were identified as early as

possible and that mitigation actions were put in place in order to address the risks and minimise

any impact on the project. At the end of each quarter the work package leaders submitted a report

that contained the progress of the work package for the last quarter along with the plans for the

following quarter. This report also included any risks that the work packages may have identified.

These were all collated and discussed during the work package leaders phone conference (occurred

every Thursday) and were tracked throughout the life span of the risk. All of the reports are available

and a sample of the risk identified are captured here in Table 6.

Risk No. Short Description WP(s)

affected

Contingency Owner

1 Migration of demos to

common infrastructure is

a large unknown in terms

of effort

WP6 Identify potential

bottlenecks as early as

possible and contact

coordinator re additional

resources

Vicomtech

2 Use Case has not obtained

a Proof of Concept

showing some

improvement

WP4,

WP5

Re-examine the scope of

the proof of concept; that

is, are we being realistic in

what we’re trying to

demonstrate

UPM,

Fokus

3 Applied Machine Learning

techniques are not

adequate

WP3 Identify preliminary results

and explore related but

still appropriate

techniques

UNITN

4 Flexibility of CI to

incorporate: 1. required

architecture elements, 2.

capacity to get

performance or be

representative, 3. docking

for testing frameworks

WP6 Resulting design and

definitions has been

concluded from the

requirements of the

different demos defining a

representative middle

point. These will evolve as

new inputs are tracked

twice per month with

Vicomtech



updates in a specific slot of

the WP6 telco

5 A Use Case has not

obtained a Proof of

Concept showing some

improvement.

WP3,

WP4

Reduce the scope of the

proof of concept

UPM

6 Sudden changes of

personnel

WP5 New personnel were

integrated. An extension

of the WP was granted.

Fokus

7 Versatile Continuous

Integration and testing

system: The current

testbed employed for

experimentation and by

the demos inside CogNet

can be too tailored to the

Rackspace APIs and the

ability to be generalized

and instantiated in other

infrastructures must be

checked

WP4,

WP6

From the deployment

code on GitHub Orange

will perform a deep

analysis of the Common

Infrastructure deploying it

on other infrastructures

from the partners. The

ability to operate with

different infrastructures

will be explored by Orange

Vic

8 Policy Engine applicability:

Universal applicability of

dynamic SUPA ECA

documents to all the

actions considered in the

demos

WP2,

WP6

The final iteration on the

design and definition of

the Policy Engine will be

concluded from the

collection of planned

actions, including a set of

actuation verbs and

required parameters,

coming from the inputs of

the different demos.

Vic

9 Testing coverage: Testing

report not only covering

scientific results but D2.1

CogNet Validation metrics

and D2.2 Technical and

WP2,

WP4,

WP5,

WP6

Generate a full picture

with the covered metrics

to span the most

representative metrics that

guarantee a complete

Vic



non-technical

requirements

validation of the CogNet

goals.

10 Automated testing:

Framework to inject

realistic traffic to the

infrastructure and a tool

to benchmark tool

CogNet Evaluation

Metrics (KPIs)

WP6 The next step around

testing is to adopt the

Jenkins system to perform

the tests, this way the

demo could be exercised

in a programmed way.

Vic

11 Apply actuation: Uniform

integration with

endpoints for the set of

actuation verbs

WP6 Poll the intention and

track the progress on

integration with

SDN/MANO/OSS/BSS

systems

Vic

12 Easiness and suitability of

mechanism to inject

realistic traffic to the

infrastructure and a

reporting tool to compile

result for the evaluation of

CogNet Evaluation

Metrics (KPIs)

WP6 Special session is

scheduled in November

2017 in Orange Gardens

to check integration,

evaluation and

connectivity aspects of the

WP6 demos. Here the

partners will receive guide

and support for the

adoption of the Jenkins

system to perform the

tests. This way the demo

could be exercised in a

programmed way.

Vic

Table 6 Sample risks identified within the CogNet project



4. Potential Impacts

The CogNet consortium is focused on guaranteeing a strong impact of the project achievements

in the most relevant research and industrial communities, spanning across several categories of

stakeholders in the cloud service provider, 5G and Machine learning areas by the se?? of the

following mediums:

Website and Social Media channels: aimed at promoting to a general and wide public our

research and development activity.

Communication and Dissemination: development of a converged strategy aiming at

ensuring a broad impact of our dissemination strategy through the participation to several

industrial and academic conferences and events and the publication of papers, journal

articles, white papers, brochures and posters.

Standardization: continuous monitoring and contribution to standards in the relevant

standardization bodies and working groups aims at improving the technical acceptability

of our results.

Business exploitation: as a final outcome of the research activity, the exploitation of the

Project’s results or foreground constitutes an essential first step towards the potential

commercialization of the CogNet solution.

Since the beginning of the project the dissemination and exploitation strategy has been considered

one of the key goals of CogNet. In order to establish a shared and efficient process helping to

identify, develop, review and make available content which communicates the objectives and

results of the activities in the project. The impact of the project is also crucial, exposing key

audiences, potential customers and relevant academic and research stakeholders to the CogNet

solution was essential in maximizing impact.

The dissemination plan of the CogNet partners consists of the following activities:

Published high-quality papers in major international conferences and journals in the area

of networking, security, autonomic systems, big data, data mining, data analysis and closely

related fields, to promote new ideas and concepts stemming from project activities and

outcomes.

For the academic partners, gaining significant skills in the area of big data computing, data

mining and analysis, machine learning and distributed computing. Such skills and

knowledge is currently leading to new courses to be held to undergraduate, graduate and

PhD students, summer schools, seminars and informal presentations to research partners.

For the industrial partners, the dissemination of results and prototypal implementations,

both internally and to other partner companies for industrial exploitation.

The dissemination plan also includes:

Press releases and Whitepapers; introducing project vision and key aspects of CogNet

research

Posters/brochures which was used for project dissemination by consortium partners in

networking events at conferences and within the 5GPPP coordination activities.



4.1. Main Dissemination Activities

CogNet had chosen to set up a number of communication means, ranging from a project dedicated

web site, up to social networking channels in Twitter, LinkedIn, Facebook and Youtube. All those

means are intended to report on CogNet progresses and convey our research findings to the

public.

The CogNet website has been designed and implemented in order to provide brief yet

comprehensive information on project goals and scope, publications, results and other similar

relevant material. The design of the website is compliant with standard practices for improving

usability for the navigation and clarity over different type of fixed and mobile devices.

Key goal of the communication and dissemination activities in CogNet was to establish an efficient

and consortium-wide process to publish and get validation from the wide research community on

the major findings and results of our technical activities.

CogNet dissemination consisted of activities for project promotion as a whole, and dissemination

of specific and innovative results (e.g., scientific papers). These include scientific papers, journals

and conferences of interest, press releases, and a list of relevant industrial associations that are

interested in the project activities and outcomes. Both future and current activities are presented,

targeting different academic and industrial communities, students, stakeholders and decision

makers. Below are listed the CogNet communication channels and their respective URLs.

Communication channel URL

CogNet website http://www.cognet.5g-ppp.eu

CogNet Twitter account https://twitter.com/5GPPPCogNet

CogNet LinkedIn group https://www.linkedin.com/groups/8353951

CogNet Facebook

account

https://www.facebook.com/5GPPPCogNet/

CogNet YouTube

channel

https://www.youtube.com/channel/UCv3BjdE2XedmnSOOYLq6E_w

4.2. Publications and events participation

This section lists all the dissemination items occurred during the whole Project duration.

Publication Title Authors Event

“The Application of Machine

Learning and Data Analytics

to Network Management for

Large Scale Networks”

(invited talk)

Robert Mullins Fokus Fuseco Forum Nov, 2015



“Deep Natural Language

Processing for Cognitive

Dialog Systems” (invited talk)

Alessandro Moschitti Deep Natural Language Processing

for Cognitive Dialog Systems Nov,

2015

“CogNet: An NFV/SDN based

architecture for Autonomic

5G Network Management

using Machine Learning”

(poster)

D. Gallico, M. Biancani,

H. Assem, D. Lopez

5G: From Myth to Reality (ETSI) Apr,

2016

“NFV Service Orchestration

and Lifecycle Management

based on Open Source

MANO” (invited talk)

Diego Lopez TMForumLive! May, 2016

"Deep Natural Language

Processing for Fact

Verification and User

Interaction” (presentation)

Alessandro Moschitti Google NLP Workshop 2016 May,

2016

“The future of 5G with

Cognitive Computing”

Haytham Assem IBM Technical Leadership Exchange

(TLE) May, 2016

“CogNet: A new architecture

featuring cognitive features”

Teodora Sandra Buda IBM Technical Leadership Exchange

(TLE) May, 2016

"Applying Machine Learning

to Intent-Based Networking "

(presentation)

Diego R. Lopez Open Platform for NFV (OPNFV)

Summit Jun, 2016

1st International Workshop

on Network Management,

Quality of Service and

Security for 5G Networks”

(organization of industrial

events)

Robert Mullins Conference Workshop hosted at

25th EuCNC 2016 Jun, 2016

“Distributional Neural

Networks for Automatic

Crossword Puzzles”

Severyn, M. Nicosia,

G.Barlacchi, A. Moschitti

53rd annual meeting of the

Association for Computational

Linguistics (ACL) Jul, 2015

“SACRY: Syntax-based

automatic crossword puzzle

resolution system”

G. Barlacchi, M.Nicosia,

A.Moschitti

53rd annual meeting of the

Association for Computational

Linguistics (ACL) Jul, 2015

“Learning to Rank Short Text

Pairs with Convolutional

Deep Neural Networks”

Severyn, A. Moschitti 38th International ACM SIGIR

Conference on Research and

Development in Information

Retrieval Aug, 2015

“Assessing the Impact of

Syntactic and Semantic

Structures for Answer

Passages Reranking” (paper)

K. Tymoshenko,

A.Moschitti

24th CIKM Oct, 2015

“Deep Neural Networks for

Named Entity Recognition in

Italian”

D. Bonadiman, A.

Severyn, A. Moschitti

2nd Italian Conference on

Computational Linguistics Dec,

2015



"Intent-Based Networking -

And How Machine Learning

Can Bring Convergence”

P. A. Aranda Networld 2020 Strategic Research

Agenda Mar, 2016

"Parallelized unsupervised

feature selection for large-

scale network traffic analysis"

Ordozgoiti, S.Gómez

Canaval A.Mozo

24th ESANN Apr, 2016

"PSCEG: An unbiased Parallel

Subspace Clustering

algorithm using Exact Grids"

Zhu, B.Ordozgoiti,

A.Mozo

24th ESANN Apr, 2016

“Can Machine Learning aid in

delivering new Use cases and

Scenarios in 5G?”

T.S.Buda, H.Assem,

D.Lopez, M. I. Corici,

D.Raz, O.Uryupina,

R.Mullins, I.G. Ben Yahia

IEEE 5GMan May, 2016

“Agile Service Manager for

5G”

M. Mechtri, I. G. Ben

Yahia, D. Zeghlache


“Emerging Management

Challenges for the 5G era:

Multi-Service Provision

through Optimal End-to-End

Resource Slicing in Virtualized

Infrastructures”

K. Tsagkaris, I. G. Ben

Yahia, A.

Georgakopoulos, P.

Demestichas


“Crossword Puzzle Resolution

in Italian using Distributional

Models for Clue Similarity”

M. Nicosia, A. Moschitti 7th IIR May, 2016

“ARRAU: Linguistically-

Motivated Annotation of

Anaphoric Description”

O. Uryupina, R. Artstein,

A. Bristot, F. Cavicchio,

K.J. Rodriguez, M. Poesio

10th LREC May, 2016

“Machine Learning for

Autonomic Network

Management in a Connected

Cars Scenario”

G. Velez, M. Quartulli, A.

Martin, O. Otaegui, H.

Assem

NETS4CARS Jun, 2016

“Convolutional Neural

Networks vs. Convolution

Kernels: Feature Engineering

for Question Answering”

K. Tymoshenko, D.

Bonadiman, A. Moschitti

NAACL Jun, 2016

“KeLP at SemEval-2016 Task

3: Learning Semantic

Relations between Questions

and Answers”

S. Filice, D. Croce, A.

Moschitti, R. Basili

SemEval Jun, 2016

ConvKN at SemEval-2016

Task 3: Answer and Question

Selection for Question

Answering on Arabic and

English Fora"

A. Barron-Cedeno, D.

Bonadiman, G. Da San

Martino, S. Joty, A.

Moschitti, F. A. Al

Obaidli, S. Romeo, K.

Tymoshenko, A. Uva

SemEval Jun, 2016



“An Energy Efficient

Architecture for 5G Network

Management”

K. Sullivan, M. Barros, A.

Martin

EuCNC 2016 Jun, 2016

“CogNet: A Network

Management Architecture

Featuring Cognitive

Capabilities”

L. Xu, H. Assem, T. S.

Buda, D. R. López, I. G.

Ben Yahia, M. Smirnov,

D. Raz, O. Uryupina, A.

Martin, A. Mozo, D.

Gallico, R. Mullins


“Cooperative Caching in C-

RAN using Bayesian

Classification and Greedy

Placement”

B. Azpiazu, M. Quartulli,

A. Martín, I. Golaizola, B.

Sierra


"Using Machine Learning to

Detect Noisy Neighbors in 5G

Networks"

U. Margolin, A. Mozo, B.

Ordozgoiti, D. Raz, E.

Rosensweig, I. Segall


“LiMoSINe pipeline:

Multilingual UIMA-based NLP

platform”

O. Uryupina, B. Plank, G.

Barlacchi, F. Valverde

Albacete, M. Tsagkias,

A.Uva, A. Moschitti

54th ACL Aug, 2016

“The Rhythms of Italian Cities:

Estimating Presence Patterns

from Mobile Phone Data”

G. Barlacchi, P. Bosetti, Q.

Zhang, M. Chinazzi, S,

Bernaola, A. Vespignani,

B. Lepri

IC2S2 Jun, 2016

Machine Learning as a

Service for enabling Internet

of Things and People

Haytham Assem, Lei Xu,

Teodora Sandra Buda,

Declan O’Sullivan

Personal and Ubiquitous

Computing (PUC) Journal

Spatio-Temporal Clustering

Approach for Detecting

Functional Regions in Cities

. Haytham Assem, Lei Xu,


Declan O’Sullivan

28th IEEE International Conference

on Tools with Artificial Intelligence

(ICTAI) 2016 Nov, 2016

ADE: An ensemble approach

for early anomaly detection


Haytham Assem, Lei Xu

IFIP/IEEE International Symposium

on Integrated Network

Management (IM) 2017 May, 2017

5G Architecture White Paper 5G Architecture Working

group

Jul, 2016

A Fast Iterative Algorithm for

Improved Unsupervised

Feature Selection

Ordozgoiti, Bruno,

Sandra Gómez Canaval,

and Alberto Mozo

Data Mining (ICDM), 2016 IEEE 16th

International Conference on. IEEE

Dec, 2016

Deep convolutional neural

networks for detecting noisy

neighbours in cloud

infrastructure

Ordozgoiti, Bruno,


Alberto Mozo, Udi

Margolin, Elisha

Rosensweig, Itai Segall

Proc. ESANN. Vol. 2017 Apr, 2017



Feature Ranking and

Selection for Big Data Sets

Ordozgoiti, Bruno,


and Alberto Mozo

East European Conference on

Advances in Databases and

Information Systems Aug, 2016

Using Machine Learning to

Detect Noisy Neighbors in 5G

Networks

Margolin, Udi, Alberto

Mozo, Bruno Ordozgoiti,

Danny Raz, Elisha

Rosensweig, and Itai

Segall

Networking and Internet

Architecture Oct, 2016

Spark2Fires: A New Parallel

Approximate Subspace

Clustering Algorithm

Zhu, Bo, and Alberto

Mozo

East European Conference on

Advances in Databases and

Information Systems Aug, 2016

Learning to Rank Non-

Factoid Answers: Comment

Selection in Web Forums

Kateryna Tymoshenko,

Daniele Bonadiman,

Alessandro Moschitti

CIKM 2016 (25th ACM International

on Conference on Information and

Knowledge Management) Oct,

2016

A Practical Perspective on

Latent Structured Prediction

for Coreference Resolution

Iryna Haponchyk,


EACL 2017 (European Chapter of

the Association for Computational

Linguistics ) Apr, 2017

Effective Shared

Representations with

Multitask Learning for

Community Question

Answering

Daniele Bonadiman,

Antonio Uva, Alessandro

Moschitti

EACL 2017 (European Chapter of

the Association for Computational

Linguistics ) Apr, 2017

AI for SLA Management in

Programmable Networks

Imen Grida Ben Yahia,

Jaafar Bendriss, Prosper

Chemouil, Djamal

Zeghlache

International Conference on Design

of Reliable Communication

Networks 2017 Mar, 2017

Forecasting and Anticipating

SLO Breaches in

Programmable Networks


Jaafar Bendriss, Djamal

Zeghlache

2017 20th Conference on

Innovations in Clouds, Internet and

Networks (ICIN) Mar, 2017

CogNitive 5G Networks:

Comprehensive Operator Use

Cases with Machine Learning

for Management Operations


Jaafar Bendriss, Alassane

Samba, Philippe Dooze

2017 20th Conference on

Innovations in Clouds, Internet and

Networks (ICIN) Mar, 2017

Log-based behavioral

differencing

Maayan Goldstein,

Danny Raz, Itai Segall

ISSTA 2017 Jul, 2017

Integrated Terahertz

Communication with

Reflectors for 5G Small Cell

Networks

Michael T. Barros,

Sasitharanand

Balasubramaniam, and R.

Mullins.

IEEE Transactions on Vehicular

Technology , 2017 Dec, 2016

CogNet: An NFV/SDN based

architecture for Autonomic

5G Network Management

using Machine Learning

(poster)

Domenico Gallico (IRT),

Matteo Biancani (IRT),

Haytham Assem (IBM),

Diego Lopez (TID)

2nd Global 5G Event – “Enabling

the 5G EcoSphere” Nov, 2016

SLA enforcement Imen Grida Ben Yahia

and Jaafar Bendriss

(Orange)

Orange Exhibition days Dec, 2016



Cognitive Services Portfolio

for 5G Network Management

Bora Caglayan, Teodora

Sandra Buda, Haytham

Assem, Imen Grida Ben

Yahia, Jaafar Bendriss,

Angel Martin, Gorka

Velez, Udi Margolin, Itai

Segall, Antonio Pastor,

Diego Lopez, Alberto

Mozo, Bruno Ordozgoiti,

Marius-Iulian Corici,

Mikhail Smirnov,

Kateryna Timoshenko,

Olga Uryupina, Joe

Tynan, Martin Tolan

2nd Workshop on Network

Management, Quality of Service

and Security for 5G Networks

colocated with EUCNC 2017 Jul,

2016

5G PPP – 5G Architecture

White Paper

5G PPP Architecture

Working Group

Jul, 2016

Dynamic Policy Based

Actuation for Autonomic

Management of Telecoms

Networks

Martin Tolan, Joe Tynan,

Angel Martin, Felipe

Mogollon

2nd Workshop on Network

Management, Quality of Service

and Security for 5G Networks

colocated with EUCNC 2017 Jul,

2016

Book chapter for 5G

European Vision

Haytham Assem, Jaafar

Bendriss, Teodora

Sandra Buda, Imen Grida

Ben Yahia, Diego Lopez,

Udi Margolin, Angel

Martin, Alberto Mozo,

Marouane Mechteri,

Kieran Sullivan, Martin

Tolan

RCMC: Recognizing Crowd

Mobility Patterns in Cities

based on Location Based

Social Networks Data

Haytham Assem,


Declan O’Sullivan

Journal: ACM Transactions on

Intelligent Systems and Technology

(TIST)

Discovering New Socio-

demographic Regional

Patterns in Cities



Declan O’Sullivan

LBSN Workshop, SIGSPATIAL 2016,

ACM 9thInternational Conference

Machine Learning as a

Service for enabling Internet

of Things and People

Haytham Assem,


Declan O’Sullivan

Journal: Personal and Ubiquitous

Computing (PUC) Journal

RCMC: Recognizing Crowd

Mobility Patterns in Cities

based on Location Based

Social Networks Data

Haytham Assem,


Declan O’Sullivan

Journal: ACM Transactions on

Intelligent Systems and Technology

(TIST)



Spatio-Temporal Clustering

Approach for Detecting

Functional Regions in Cities



Declan O’Sullivan

IEEE ICTAI (International

Conference on Tools for Artificial

Intelligence) 2016 Nov, 2016

Cognitive Applications and

Their Supporting Architecture

for Smart Cities



Declan O’Sullivan

Journal: Big Data Analytics for

Sensor-Network Collected

Intelligence

ADE: An ensemble approach

for early anomaly detection


Haytham Assem, Lei Xu



Management (IM) 2017 May, 2017

Instantaneous Throughput

Prediction in Cellular

Networks: Which Information

Is Needed?

Alassane Samba (Orange

Labs, France), Gwendal

Simon (Telecom

Bretagne, France),

Philippe Dooze (Orange

Labs, France), Yann

Busnel (Crest (Ensai) /

Inria Rennes, France),

Alberto Blanc (Telecom

Bretagne, France)



Management May, 2017

Don't you understand a

measure? Learning it:

structured prediction for

Coreference Resolution using

its evaluation measure as a

loss function

Iryna Haponchyk

(UNITN), Alessandro

Moschitti (UNITN)

ACL (Association for Computational

Linguistics) 2017 Aug, 2017

RelTextRank: An Open Source

Framework for Building

Relational Syntactic-Semantic

Text Pair Representations

Kateryna Tymoshenko

(UNITN), Alessandro

Moschitti (UNITN),

Massimo Nicosia

(UNITN) and Aliaksei

Severyn (Google)



Self-Crowdsourcing Training

for Relation Extraction

Azad Abad(UNITN),

Moin Nabi (UNITN) and


(UNITN)



Annotating a broader range

of anaphoric phenomena, in a

variety of genres: the ARRAU

Corpus

Olga Uryupina (UNITN),

Ron Artstein, Antonella

Bristot, Federica

Cavicchio, Francesca

Delogu, Kepa J.

Rodriguez, Massimo

Poesio

Journal: Journal of Natural

Language Engineering



A fast iterative algorithm for

improved unsupervised

feature selection

Bruno Ordozgoiti (UPM),

Sandra Gómez Canaval

(UPM), Alberto Mozo

(UPM)

2016 IEEE 16th International

Conference on Data Mining (ICDM)

Dec, 2017

Deep convolutional neural

networks for detecting noisy

neighbours in cloud

infrastructure



(UPM), Alberto Mozo

(UPM), Udi Margolin

(Nokia), Elisha

Rosensweig (Nokia), Itai

Segall (Nokia)

25th European Symposium on

Artificial Neural Networks,

Computational Intelligence and

Machine Learning Apr, 2016

Probabilistic Leverage Scores

for Parallelized Unsupervised

Feature Selection



(UPM), Alberto Mozo

(UPM)

14th International Work-

Conference on Artificial Neural

Networks 2017 (IWANN) Jun, 2017

CogNet: Network

Management Architecture

Featuring Cognitive

Capabilities

Lei Xu, Haytham Assem,



Angel Martin, Domenico

Gallico, Matteo Biancani,

Antonio Pastor, Pedro A.

Aranda, Mikhail Smirnov,

Danny Raz, Olga

Uryupina, Alberto Mozo,

Bruno Ordozgoiti,

Marius-Iulian Corici, Pat

O’Sullivan, Robert

Mullins.

EUCNC 2016 Jun, 2016

Virtualization and 5G

(Tutorial)

Fabrizio Granelli (UNITN) European Wireless 2017 May, 2017

Enabling 5G architectures

through Software Defined

Networking (Tutorial)

Fabrizio Granelli (UNITN) IEEE CAMAD 2017 Jun, 2017

Deep Learning and Structural

Kernels for Semantic

Inference on Web Data

(Invited talk)


(UNITN)

SLTC 2016 Nov, 2016

Machine Learning for 5G

applications

Olga Uryupina (UNITN) NetCla: ECML-PKDD 2016 Network

Classification Challenge Sep, 2016

Sep, 2016

Cognitive modules

supporting Network

Management

Teodora Sandra Buda

(IBM)

SAP Industrial Innovation Event 27

July 2017



Urban Mobility Awareness for

Network Demand Prediction

in Smart Cities

Haytham Assem (IBM) All Ireland Smart Cities Forum 12

September 2017

Urban Mobility Awareness for

Network Management

Haytham Assem (IBM) Irish Management Institute

Postgrad Event 5th October 2017

Urban Mobility Awareness

and Dense Urban Area demos

Haytham Assem,


Bora Caglayan, Jason

Lloyd (IBM)

H2020 SELIS GA 25th October 2017

Urban Mobility Awareness

and Performance Anomaly

Detection for Network

Management

Teodora Sandra Buda

(IBM), Imen Grida Ben

Yahia (Orange)

Orange Annual Exhibition 5-7

December 2017

Cognitive Network

Management

Imen Grida Ben Yahia

(Orange)

9th FUSECO FORUM 09-10,

November 2017

Efficient Weighted Model

Integration via SMT-Based

Predicate Abstraction

Paolo Morettin (UNITN),

Andrea Passerini

(UNITN), and Roberto

Sebastiani (UNITN)

IJCAI 2017 August 19-25, 2017

Learning Contextual

Embeddings for Structural

Semantic Similarity using

Categorical Information

Massimo Nicosia

(UNITN) and Alessandro

Moschitti (UNITN)

CoNLL August 3-4, 2017

Accurate Sentence Matching

with Hybrid Siamese

Networks

Massimo Nicosia

(UNITN) and Alessandro

Moschitti (UNITN)

CIKM November 6-10, 2017

Neural Sentiment Analysis for

a Real-World Application

Daniele Bonadiman

(UNITN), Giuseppe

Castellucci (Almawave),

Andrea Favalli

(Almawave), Raniero

Romagnoli (Almawave)

and Alessandro

Moschitti (UNITN)

Clic-it December 11-13, 2017

Predicting Land Use of Italian

Cities using Structural

Semantic Models

Gianni Barlacchi (UNITN),

Bruno Lepri and


(UNITN)

Clic-it December 11-13, 2017

Structural Semantic Features

for Land Use Classification

(Submitted)

Gianni Barlacchi (UNITN),

Bruno Lepri(FBK) and


(UNTIN)

WWW2018 April 23-27, 2018



Syntactic and Semantic

Structures for Answer

Passages Reranking (UNDER

REVIEW)

Kateryna Tymoshenko

(UNITN), Alessandro

Moschitti (UNITN)

Journal: TOIS (ACM Transactions on

Information Systems)

Autonomous Crowdsourcing

through Human-Machine

Collaborative Learning

Azad Abad (UNITN),

Moin Nabi (UNITN) and


(UNITN)

SIGIR 2017 August 7-11, 2017

Collaborative Partitioning for

Coreference Resolution

Olga Uryupina (UNITN)

and Alessandro

Moschitti (UNITN)

CoNLL August 3-4, 2017

ST-DenNesFus: Deep Spatio-

Temporal Dense Networks for

Network Demand Prediction

(Under Review)

Haytham Assem, Bora

Caglayan, Teodora

Sandra Buda, Declan

O'Sullivan (IBM)

Journal: IEEE Transactions on

Knowledge and Data Engineering

(TKDE) 2017

DeepAD: A Generic

Framework based on Deep

Learning for Time Series

Anomaly Detection (Under

Review)


Bora Caglayan, Haytham

Assem (IBM)

PAKDD 2018 June 3-6, 2018

5G PPP – 5G Architecture

White Paper version 2


Bora Caglayan, Haytham

Assem (IBM)

LAMB-DASH: A DASH-HEVC

adaptive streaming algorithm

in a sharing bandwidth

environment for

heterogeneous contents and

dynamic connections in

practice

Angel Martin, Roberto

Viola, Josu Gorostegui,

Mikel Zorrilla, Julian

Florez and Jon

Montalban

(VICOMTECH)

Journal: Springer Journal of Real-

Time Image Processing 23 October

2017

SaW: Video Analysis in Social

Media with Web-based

Mobile Grid Computing

Mikel Zorrilla, Julián

Flórez, Alberto Lafuente,

Angel Martin, Jon

Montalbán, Igor G.

Olaizola, Iñigo Tamayo

(VICOMTECH)

IEEE Transactions on Mobile

Computing 26 October 2017

Hybrid MEC and Client

Adaptation for Fair and

Efficient Media Streaming in

SDR Mobile Networks

(Submitted)

Angel Martin, Roberto

Viola, Josu Gorostegui,

Mikel Zorrilla, Julian

Florez and Jon

Montalban

(VICOMTECH)

IEEE Transactions on Circuits and

Systems for Video Technology 20

November 2017



Cognitive Management

Architecture for QoE-aware

delivery of media services in

5G Networks (Submitted)

Angel Martin, Jon Egaña,

Julian Florez, Jon

Montalban, Marco

Quartulli, Roberto Viola

and Mikel Zorrilla.

(VICOMTECH)

IEEE Transactions on Broadcasting

22 December 2017

Iterative column subset

selection



(UPM), Alberto Mozo

(UPM)

Journal: Knowledge and

Information Systems (KAIS)

A distributed and quiescent

max-min fair algorithm for

network congestion control

(PUBLISHED).

Alberto Mozo (UPM),

José Luis López-Presa

(UPM), Antonio

Fernández Anta (IMDEA

Networks)

Journal: Expert Systems with

Applications

Forecasting short-term data

center network traffic

dynamics with convolutional

neural networks (to be

published in 2018)

Alberto Mozo (UPM),



(UPM)

Journal: PLOS ONE

Mining logical theories in

feature space

Andrea Passerini

(UNITN)

SMiLee February 3-4, 2016

Where are you going? An

overview on machine learning

models for human mobility

Gianni Barlacchi (UNITN) PyData Italy 2016 April 19, 2016

CIKM Data Mobility

Challenge

Gianni Barlacchi (UNITN) CIKM 2017 November 6, 2017

Master course: Massively

Parallel Machine Learning

Alberto Mozo (UPM) September 2017 – January 2018

Introduction to deep learning Bruno Ordozgoiti,

Alberto Mozo (UPM)

Workshop at Universidad

Politécnica de Madrid 4 - 11

October 2017

Applying Cognitive

Techniques to Enable Intent-

Based Networking

Diego R. Lopez IEEE 5G Summit at UNET 2017 11

May 2017

On the Dialectics of Intent,

and how it applies to next-

generation network

management

Diego R. Lopez EUCNC 2017 12-15 June 2017

Network Service

Management in the Days of

the Software Network

Diego R. Lopez SDN World Congress 9-13 October

2017



4.3. Collaboration with other EU Groups and Projects

This section reports all of the collaborations with other 5G-PPP Projects. Throughout the life of

CogNet one of the main focuses was the dissemination to a wider audience. A particular emphasis

was on interactions with other 5G-PPP projects which included presentations, networking events,

published papers and workshops. Below is a list of the main activities carried out during the whole

Project duration.

Event Activity

Steering Board meeting, September

2016, Brussels

Presentation about the progress of CogNet and the

Network Management & QoS Working Group.

Steering Board meeting, December

2016, London

Presentation on project updates and the project’s

position was conveyed on various issues related to

5G-PPP.

7th FUSECO Forum, November 2016,

Berlin

Panel on open source approaches to NFV

orchestration, where CogNet results were

mentioned.

5GMan 2017, May, 2017 Workshop organized with createNet which are part

of Coherent and Sesame project.

Steering Board and Technological Board

meetings

Contributions to Phase-2 cartography and related

schematics

Chair of Working Group on 'Network

Management & QoS’

Updated Terms of Reference, identified Targeted

Outputs, regular updates to WG members (Phase-

1 and Phase-2 projects)

Discussions with 5G-Media project

(Phase-2) on using CogNet’s smart

engine

Email exchanges and telcos.

ICT Proposers Day in Budapest Informal updates/discussions with various 5G-PPP

projects during ICT Proposers Day in Budapest.

Table 4-1 Collaboration and liaisons with other 5Gppp projects

CogNet is also chairing the Network Management Working Group and hosted a Network

Management Workshop at EuCNC 2017 in Oulu, Finland. This was held in conjunction with the

SelfNet 5G-PPP project (https://selfnet-5g.eu/tag/5g-ppp/) and the title of the workshop was “2nd

Network Management, Quality of Service and Security for 5G Networks”. The reason for the

workshop was to show case the work of the Network Management, Quality of Service and Security

Working Group of the EU 5G-PPP and to present the newly developed whitepaper on these same

topics as developed by the projects involved in the Working Group.

The workshop brought together the various contributing projects within the 5G-PPP that are

involved in this working group and other interested parties (projects and/or organisations) which

have a common interest in the development and progression of the following:



• Network Management

• Security

• Quality of Service

As a result of this workshop CogNet and Selfnet created a joint interest on these topics, and have

also established a first contact with the Charisma project (http://www.charisma5g.eu/) that has

been further explored afterwards.

In the summer of 2017 CogNet had an article published in the EURESCOM message ‘Smarter

networks through machine learning’ this article disseminated the results of the project to date. Also

in the publication was an interview with Dr. Michael Barros on Network management in 5G. Within

the later end of the project, CogNet was given the opportunity to publish a chapter in the book

title a 5G European Vision. Most of the consortium contributed to the chapter showcasing some of

the work carried out by the CogNet project.

CogNet is currently collaborating with the 5G-PPP Phase II project “5GMEDIA”

(http://www.5gmedia.eu/). Through the work carried out by CogNet and shared through the

various working groups (Steering Board, Technical Board and the Network Management and QoS

WG) a relationship has sprung up where the outputs of CogNet may be able to provide a spring

board for the 5GMedia project. This collaboration is currently ongoing, and it is envisioned that it

will continue past the finish date of CogNet so that we can provide as much assistance as possible.

CogNet is part of the Architecture Working Group and as such has contributed to the 1st and 2nd

release of the 5G Architecture White Paper. The first release has been published on July 2016, while

the second release will be published in Q4 2017 (https://5g-ppp.eu/white-papers/) and to the

“European 5G Annual Journal 2017” that presents the achievements of 5G PPP phase 1 projects

after two years after their launch and has been released on September 2017 (https://5g-

ppp.eu/annual-journal/).

4.4. Exploitation of Project’s foreground

The consortium final exploitation strategy has been periodically captured throughout the life time

of the project and has the final strategy documented in the deliverable “D7.9 - Final Business

Exploitation Plan” where each partner has provided a plan to exploit the achievements of the

CogNet project taking into account the latest development brought in the different use cases and

scenarios. This deliverable not only covers the intensions of the industrial partners where they are

planning on introducing components of CogNet directly into their portfolios but also the academic

partners who are planning on updated/creating new modules at both under and post graduate

level based on the outcomes of CogNet.



5. Project Details

Project Website http://www.cognet.5g-ppp.eu/

Open source Repository https://github.com/CogNet-5GPPP

GitHub Repository https://github.com/CogNet-5GPPP

Twitter https://twitter.com/5GPPPCogNet

Facebook https://www.facebook.com/5GPPPCogNet/

YouTube https://www.youtube.com/channel/UCv3BjdE2XedmnSOOYLq6E_w

Coordinating Partner Telecommunications Software & Systems Group (TSSG),

Waterford Institute of Technology West Campus,

Carriganore,

Co. Waterford,

X91 P20H,

Ireland.

Email lists [email protected]

[email protected]

[email protected]

[email protected]

[email protected]

[email protected]

[email protected]

Conferences bridges GoTo Meeting

Cisco WebEx Meeting Center

Skype

Document Repositories

(private, requires

activation)

http://redmine.cognet.5g-ppp.eu/

http://www.cognet.5g-ppp.eu/

https://github.com/CogNet-5GPPP

https://github.com/CogNet-5GPPP

https://twitter.com/5GPPPCogNet

https://www.facebook.com/5GPPPCogNet/

https://www.youtube.com/channel/UCv3BjdE2XedmnSOOYLq6E_w

mailto:[email protected]










5.1. Meeting Metrics

Kick off meeting 16th July 2015

Face to face technical & plenary meetings 9 (all partners involved)

Workshops 8 (not all partners involved in all workshops)

WP1 weekly meetings 87 up to the 12/2017

WP2 meetings 44 up to the 04/2017





Final Review 14th March 2018

Meeting Minutes Repositories http://redmine.cognet.5g-ppp.eu/




6. References

[1] T. S. Buda, H. Assem, L. Xu, D. Raz, U. Margolin, E. Rosensweig, D. R. Lopez, M.-I. Corici, M.

Smirnov, R. Mullins, O. Uryupina, A. Mozo, B. Ordozgoiti, A. Martin, A. Alloush, P. O'Sullivan and

I. G. B. Yahia, Can Machine Learning aid in delivering new Use cases and Scenarios in 5G?,

5GMAN Workshop, 2016 IEEE/IFIP Network Operations and Management Symposium (NOMS),

2016.

[2] M. Sanchez, A. Asadi, M. Draxler, R. Gupta, V. Mancuso, A. Morelli, A. De La Oliva and V.

Sciancalepore, Tackling the Increased Density of 5G Networks: The CROWD Approach, IEEE

81st Vehicular Technology Conference (VTC Spring), 2015.

[3] L. Jiang, G. Feng and S. Qin, Cooperative content distribution for 5G systems based on

distributed cloud service network, IEEE International Conference on Communication Workshop

(ICCW), 2015.

[4] S. Jeon, D. Corujo and R. L. Aguiar, Virtualised EPC for on-demand mobile traffic offloading in

5G environments, IEEE Conference on Standards for Communications and Networking (CSCN),

2015.

[5] L. Xu, H. Assem, I. G. B. Yahia, T. S. Buda, A. Martin, D. Gallico and M. B. e. al., CogNet: A network

management architecture featuring cognitive capabilities., IEEE European Conference on

Networks and Communications (EuCNC), 2016.

[6] ETSI, Network Functions Virtualisation, An Introduction, Benefits, Enablers, Challenges and Call

for Action. https://portal.etsi.org/nfv/nfv_white_paper.pdf, 2012.

[7] A. Kejariwal, Introducing practical and robust anomaly detection in a time series, Twitter

Engineering Blog. Web, vol. 15., 2015.

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