sinc – an information-centric approach for end-to-end iot cloud resource provisioning

SINC – An Information-Centric Approach

for End-to-End IoT Cloud Resource

Provisioning

Hong-Linh Truong and Nanjangud Narenda

Distributed Systems Group, TU Wien

[email protected]

dsg.tuwien.ac.at/staff/truong

Ericsson Research, Bangalore, India

[email protected]

ICCCRI2016@CloudAsia2016, 4th May 2016 1

Outline

Motivation

Challenges

The SINC conceptual framework

Overall architecture

API management and integration

Naming, slicing, and routing

Slice management and adaptation

Towards the implementation of SINC

Conclusions and future work


State-of-the art IoT Clouds/Cyber-

Physical Systems Complex infrastructures of IoT elements (sensors,

gateways, networks), micro data centers, network

services, cloud VM, storage, etc.


Cloud

(big, centralized

data centers)

Cloud

(big, centralized

data centers)

Edge

(IoT devices, micro

data centers)

Edge

(IoT devices, micro

data centers)

Edge

(IoT devices, micro

data centers)

Edge

(IoT devices, micro

data centers)

Network functions

(network services

+ micro

datacenter

On-demand resources provisioning across IoT networks

(the edge), network functions (the middle) and the

clouds (the back-end)

Motivation (1)

Application scenarios: emergency responses, on-demand

crowd sensing, Geo Sports monitoring, cyber-physical

systems testing, etc.


Geo Sports: Picture courtesy

Future Position X, Sweden

Indian Overfly collapses

figure source: http://timesofindia.indiatimes.com

Need to have an end-to-end provisioning of resources

E.g., sensors, network function services, storage, virtual machines

Short, crucial and heavily workload; elasticity and uncertainties.

Motivation (2)

Problems

Virtual resources are provided by different providers

Often there is no coordination among them inadequate

support for elasticity and uncertainties for the application

Host-centric information is too low level to represent

“slice” view

It is very hard, if not impossible, to establish end-to end

view on resources

lack of tools, too complex, time-consuming, & error-

prone effort for application users and developers

Our contribution

A conceptual framework for slicing IoT, network

functions and cloud resourcesICCCRI2016@CloudAsia2016, 4th May 2016 5

Challenges

Modeling distributed IoT, network functions and cloud

capabilities in an integrated view

Slicing end-to-end network of resources

Composing resources in slices of IoT, network

functions and clouds

(Re-)configuring composed resources


End-to end

Resource slice

Applications/Virtual

infrastructures

SINC conceptual framework


Integrating diverse types of resources

Make a Resource Grid ready for slice creation

How to harmonize and gather IoT, network functions and cloud

resources

API Integration and Communication

Use REST API for obtaining metadata and control of resources

Sensoring data can be transferred through different

middleware

Work with existing metamodel (IoTivity, OpenHAB, IoTDM,

ETSI MANO, OCCI, CIMI, etc.)

Rely on scalable cloud middleware (e.g., AMQP & MQTT)ICCCRI2016@CloudAsia2016, 4th May 2016 8

IoT networks Network Function Services Clouds

Resource Grid

Naming, Slicing and Routing

From Resource Grid to information-centric description

of Partitions of Resources for slices

Information-centric description of resources from IoT, network

and clouds; modeling partitions of resources

Slicing

Leveraging network slicing techniques (e.g., 5G)

Leveraging IoT and cloud virtualization to provision on-demand

dedicated resources with elasticity capabilities

Routing

Utilize concepts of Forwarding Information Base (FIB) and

Pending Interest Table (PIT) for routing control commands and

data queries to underlying resources

Separate control commands and data queries from sensoring

data transportation


Resource Management,

Configuration and Adaptation (1)

Creating slices, each slice includes a set of partitions of

resources

Modeling and capturing user requirements for slices

Creation and Management

Develop new algorithms for creating slices by leveraging

existing works for IoT, networks, and services

Integrate with NFV orchestrators, virtual sensors, gateways,

cloud APIs and SDN controllers.

Deal with different resource provisioning models imposed by

underlying infrastructures

Configuration by leveraging different deployment tools for IoT,

network functions and clouds


Resource Management,

Configuration and Adaptation (2)

Monitoring and Management

Develop end to end metrics for slices

Integrate monitoring capabilities from different

providers and correlating monitoring data

Runtime slice adaptation

Performance as well as uncertainties at

infrastructures, applications and their integration

levels

Adaptation capabilities across IoT, network functions

and clouds

Multiple level of adaptations based on end-to-end

problems and partition problems


Towards the Implementation

Using REST API to integrate resource management

capabilities from different providers

Distributed communication middleware, e.g., based on

AMQP/MQTT, for querying resource information and

propaging controls

TOSCA or other topology description tools for modeling

topologies for supporting configuration and deployment

Leveraging existing deployment techniques for IoT and

clouds

Testbed established with open sources: Dockers,

OpenStack, Weave, OpenDayLight, etc. by utilizing

cloud, network and IoT devices


Towards the Implementation - HINC

Implement API Integration and

Communication

http://sincconcept.github.io/HINC/

High level information models for

Resource Grid

Middleware and adaptors for

integrating different providers

API for querying and configuring

resources

Leveraging SALSA for IoT,

network functions and cloud

configuration

http://tuwiendsg.github.io/SALSA/ICCCRI2016@CloudAsia2016, 4th May 2016 13

Conclusions and Outlook

Slicing IoT, network functions and clouds

Important for various types of applications

Key to the coordination of diverse types of resources in

distributed edge and cloud systems

SINC: a conceptual framework and steps to achieving end-to-

end resources provisioning

Ongoing work

Slice requirement modeling and composition algorithms

APIs for programming resource queries and controls

Configuration tools (http://tuwiendsg.github.io/SALSA/)

Uncertainty testing and analytics (www.u-test.eu)

Testbed (Vienna, Bangalore, Hanoi, and public clouds)

Check http://sincconcept.github.io for new update


http://tuwiendsg.github.io/SALSA/

http://www.u-test.eu/

Thanks for your

attention!

Questions?

Hong-Linh Truong

Distributed Systems GroupTU Wien

dsg.tuwien.ac.at/staff/truong


sinc – an information-centric approach for end-to-end iot cloud resource provisioning

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