Energy optimization techniques for green cognitive infrastructures

ENergy efficient DEsign of COmmunications Networks (ENDECON)

Research Team D

Telecommunication Networks and integrated Services Laboratory – TNS

Department of Digital SystemsUniversity of Piraeus

TNS - ENDECON

Outline

Our team

Research area and technical approach

Contribution to WPs and tasks

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TNS - ENDECON

University of Piraeus - Research Team - D

Department of Digital Systems

Telecommunication Networks and integrated Services Laboratory – TNS

Main research team consists of 3 faculty members Panagiotis Demestichas, Professor, head of TNS

Angelos Rouskas, Assistant Professor, head of ENDECON team

Angeliki Alexiou, Assistant Professor

External research team consists of 4 members George Dimitrakopoulos, Lecturer at HUA, Diploma and PhD from NTUA ECE

dept

Dimitrios Komnakos, Postdoc, Diploma and PhD from NTUA ECE dept

Marios Logothetis, PhD candidate,TNS lab

+1 PhD candidate

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Profile [1|6]: Institution, Department

The University of Piraeus comprises nine academic departments

The University of Piraeus Research Center (UPRC) provides administrative assistance to basic and applied research activities, conducted by the personnel of the University of Piraeus

The Department of Digital Systems was founded in 1999

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Profile [2|6]: Laboratory

Telecommunication Networks and integrated Services (TNS) Laboratory

Objective - Short Description The Laboratory of Telecommunication Networks and integrated Services (TNS)

is framed within the Department of Digital Systems, of the University of Piraeus.

The main objective of the TNS Laboratory is to conduct research and development in all areas related to telecommunication networks and services. Through its research, development and educational activities, the Laboratory will contribute to the realization and sustainable development of a human-centric Information and Communication Society.

Personnel 3 members of faculty

4 Senior Research Engineers (PhD)

5 Research Engineers – PhD students

25 Research/Software Engineers – Thesis at postgraduate or undergraduate level

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Profile [3|6]: Laboratory

The TNS Laboratory conducts applied and basic research on: High-speed, fixed-access, broadband networks

High-speed, wireless-access, infrastructures (2G, 3G, 4G, B3G)

Core networks

Services and respective platforms in heterogeneous networks

Internet and Web technologies

Design, management and performance evaluation of communication networks

Software Engineering, Service-oriented platforms

Optimisation techniques, algorithm and complexity theory, queuing theory

Machine learning techniques

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Profile [4|6]: Legacy assets

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TNS Research, Standardization Activities: Infrastructure

FP7/IST E3 (End-to-End Efficiency) Cognitive networks and systems (Technical

Management) ENISA

Ontology for modelling resilience stakeholders and associated concepts

FP6/IST E2R (End-to-End Reconfigurability)

B3G Infrastructures, Reconfigurable, Software Adaptable, SDR

FP6/IST ACE (Antenna Centre of Excellence)

4G systems FP5/IST

MONASIDRE, CREDO, SHUFFLE B3G Infrastructures, Cooperative

FP4/IST STORMS Design of 3G Infrastructures

ARIADNE (Ministry of Development, General Secretariat for Research and Technology): Dynamic Spectrum Management and Planning of 4G Wireless Access Networks and Terminals.

Consultancy (Ministry of Finance, Ministry of Education, Private sector related to 4G systems and WiMAX)

TNS Research, Standardization Activities: Services

INTEL Collaboration Platform development: Quality of Experience

enhancement, lower costs and green decisions EUREKA/CELTIC IMPULSE (Integrated

Multimodal Platform for Ubiquitous Multimedia Service Execution)

IMS platforms EUREKA/CELTIC WIN-HPN (Wireless

Intelligent Hospital Premises Network) Digital Health

FP5/IST Moebius E-Business and Digital Health over 2.5G and 3G

Infrastructures FP4/IST

Screen, Montage Service Engineering, Accounting, Personal

Mobility DIOSKOUROI

Training and consultancy on modern telecommunication infrastructures and services for Military personnel

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Profile [5|6]: Emerging assets

Contributions to EU-funded projects and initiatives

FP7/ICT ONEFIT (Opportunistic networks and Cognitive Management Systems for Efficient Application Provision in the Future Internet) – STREP – Project coordination

Networking schemes for wireless access to the Future Internet

ONs, CMSs & Control Channels for the Cooperation

FP7/ICT UniverSelf (Self-management in the FI) – IP – WP leadership

Autonomic management of Future Internet infrastructure

FP7/ICT iCore (Internet Connected Objects for Reconfigurable Eco-systems) – IP

FP7/ICT ACROPOLIS (Advanced coexistence technologies for Radio Optimisation in Licensed and Unlicensed Spectrum) - NoE

COST ICT Action IC0902 on Cognitive radio and networking for cooperative coexistence of heterogeneous wireless networks – National representative

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Profile [6|6]: Activities – Memberships

Active participation to standardization bodies, research fora and organisations

ETSI-RRS (Reconfigurable Radio Systems) AFI (Autonomic network engineering for the self-managing Future Internet) IEEE SCC41 (Dynamic Spectrum Access Networks)/1900.4 WG on

Architectural Building Blocks Enabling Network-Device Distributed Decision Making for Optimized Radio Resource Usage in Heterogeneous Wireless Access Networks

Future Internet Initiatives Wireless World Research Forum

(WWRF) (2004 – today) European Networks of Living Labs

(ENoLL) (2011 – today) Cognitive Communications WUN (2010 – today) TM Forum (2012 – today) GreenTouch (2012 – today) Next Generation Mobile Networks

(NGMN) (2012 – today)

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Outline

Our team

Research area and technical approach

Contribution to WPs and tasks

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TNS - ENDECON

ICT energy consumption and Mobile Communications share

ICT infrastructure 3% of worldwide energy

2% of global CO2 emissions – 530 Mts of CO2 in 2002 and 830 Mts in 2007

Mobile Communication Networks 2002: 12% of ICT emissions (64 Mts of CO2)

Increase in data rates expected and significant rollout of 4G BS (LTE)

2020: Share expected to grow to 21% (absolute number expected to grow 3

times to 178 Mts of CO2)

Besides environmental aspects, energy is a significant portion of operators OPEX

www.smart2020.org

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Energy consumption in Mobile Communications Networks

Largest fraction of energy consumption in the wireless access network

3 million Base Stations 4.5 GW power consumption

approximately 20 Mts of CO2

Energy OPEX 3000$/y for grid connected BS – 10 times higher for off-grid diesel powered BSs

3 billion Mobile Stations (MSs) 0.2–0.4 GW power consumption

approximately 1.5 Mts of CO2 / year

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www.smart2020.org

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BS typical power consumption

Alcatel-Lucent data Major inefficiency contributing factors Component level: power amplifier, air conditioning, general power supply etc. Link level: synchronization, reference signals Network level: network planning to meet full load requirements

Every BS has a non negligible inherent production cost and an installation cost

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Typical datacenter power consumption

Largest consumers of energy are supporting facilities (cooling, etc)

Servers and networking equipment consumes a smaller proportion of datacenter energy

Major inefficiency factor is that the devices are not energy proportional

Infotech research group

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Optimizing subsystems locally may be inefficient

Mobile communication networks Small cell deployment decreases the power consumption per cell but

requires many operating cells which may result in higher network consumption especially under low load conditions

Efficient multiuser scheduling reduces power transmissions but on the receiving end increases complexity and computations for detection

Datacenters energy proportional servers and network devices may not minimize total

datacenter power consumption: consider the case of underutilized components under low load conditions

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Our technical approach

Optimizing energy efficiency in mobile communication networks at the network level requires a holistic approach regarding the system and its constituent parts as a whole

Address diverse scenarios and high system complexity multilayer BS architectures

multi RAT operation

non-uniform user density

varying traffic patterns

Cognitive networks can support such an approach for dynamic learning, adaptation and reconfiguration of systems

Every possible parameter measurable should be taken into account so that the network intelligently modifies its functionality to meet a certain objective which in our case is power saving

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TNS - ENDECON

Outline

Our team

Research area and technical approach

Contribution to WPs and tasks

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TNS - ENDECON

Research Team D Tasks

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WP5 - Energy optimization techniques for green cognitive infrastructures (Leader) Task 5.1: Green cognitive wireless network planning

Task 5.2: Management techniques for wireless green cognitive network devices

Task 5.3: Design of energy-efficient datacenters, servers and base stations

WP2 - Cross-layer design of wireless communication networks with energy-optimum consumption Task 2.1: Cooperative and energy-efficient beamforming techniques

Task 2.3: Cross-layer routing techniques for different degrees of channel state information

WP3 - Network planning and operation techniques for optimal energy consumption Task 3.2: Energy-aware RWA algorithms

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Green cognitive wireless network planning [WP5 Task 5.1]

Networks are dimensioned mainly for busy hour conditions: most of the time the network is underutilized and energy resources are wasted

The task will address network planning for energy efficiency in an area where no network exists (case 1)

an existing operator wishes to extend its network with new RAT technologies to support additional demand and new services (case 2)

The design objective will include minimization of power consumption and

cost in terms of OPEX/CAPEX

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Green cognitive wireless network planning [WP5 Task 5.1]

Issues/Parameters under consideration Coexistence of complementing RAT technologies

Coexistence of multi-layered architectures (macro-micro BSs)

Optimization will include BS characteristics like position, RF out power, antenna tilt, gain, height

QoS guarantees

Population densities

Varying traffic load patterns

Two ways to approach this optimization planning task Formulation of the optimization problem and introduction of heuristics whose

behavior will be compared with the optimal solution

Planning with stochastic and evolutionary optimization techniques

Assessment through energy-related metrics area spectral efficiency (rural areas), bit per joule efficiency (dense areas),

dBe

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Management techniques for wireless green cognitive network devices [WP5 Task 5.2 ]

Power management in current networks focus mainly on interference Cell breathing is employed in WCDMA networks and cell size is reduced by

lowering cell transmission power

TRXs in GSM networks are switched on/off as traffic increases/decreases

To cope with the increasing data traffic demands Small cell deployment (microcell, picocell, femtocell) to absorb traffic

Macro-layer cells to provide coverage

From an energy efficient point of view small cells are more power efficient than macro BS

Traffic fluctuations in small cells can be very significant Under high load conditions traffic should be served by small cells

Under low load conditions traffic should be aggregated and cells should power off

=>BS coordination – cooperation is necessary for dynamic power management

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Management techniques for wireless green cognitive network devices [WP5 Task 5.2 ]

The task will address self organization of cellular networks to reduce the energy consumption of the BSs under time varying traffic loads Disperse load for load balancing and aggregate load for energy efficiency

Power management techniques to alter the power state of the base station to meet the actual demand cell zooming in/out

binary on/off

cooperation strategies

Challenging problems to cope with are tracing traffic load fluctuations

determining switching thresholds

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Design of energy-efficient datacenters, servers and base stations [WP5 Task 5.3]

Current IT equipment (servers, storage and network) power consumption not proportional to the load

Operating IT equipment requires facility power (cooling, UPS)

Current design focus on load balancing for short response times and availability

Our approach in this task is to lower power consumption through load aggregation without compromising availability Will propose routing and load aggregation/balancing algorithms to minimize the

number of underutilized equipments and stations in the IT and facility infrastructure and thus achieve high energy efficiency

Assessment will be based on metrics like Data Centers Infrastructure efficiency (DCiE) and Data Center Productivity efficiency (DCP)

The task will investigate the possibility of a real measurement procedure in a local data center and use sensor network for real time power consumption monitoring

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Contributions to Tasks 2.1 and 2.3 (Alexiou)

1. Small Cell Networks (SCNs) for LTE-Advanced and Beyond (Task 2.1)

2. Cluster-Head Selection Algorithm (Tasks 2.1, 2.3)

3. Energy Efficient Hybrid TDMA/CSMA technique (Tasks 2.1, 2.3)

4. Energy-aware Relay Transmission Strategy (Tasks 2.1, 2.3)

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Small Cell Networks (SCNs) for LTE-Advanced and Beyond: Future Work (Task 2.1)

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Increased wireless data demand Increased number of interconnected devices

Interference Management complexity Increased overhead for facilitating coordination among base stations

SCNs Challenges

Densify wireless access networks

Energy-Efficiency Aspects

Reduce energy consumption by minimizing signaling overhead Examine the QoS-Energy Efficiency trade-off for coordinated cooperation schemes

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Cluster-Head Selection Algorithm (Tasks 2.1,2.3)

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• The vision of the future Wireless Communication Systems is associated with some critical requirements such as reliable connectivity in ad-hoc and energy-limited scenarios

• Many of the connected devices are subject to physical limitationso Hindered direct link to the cellular infrastructureo Power consumptiono Hardware complexity

• Reliable solution for proving coverage extension Cluster-head based networks

Objective: enhanceo Energy efficiencyo Reliabilityo Performance

Proposed Technique:Τhe nodes are able to switch between different CH

[P. S. Bithas, Α. Lioumpas, and A. Alexiou, "Enhancing the Efficiency of Cluster-based Networks through MISO Techniques," in Proc. Wireless Communication and Networking Conference (WCNC 2012), Apr. 2012.]

FUTURE WORK

Investigate the case where a node may select a CH to connect to among L available.

Simpler diversity reception techniques will be investigated

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Energy Efficient Hybrid TDMA/CSMA technique (Tasks 2.1,2.3)

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In a typical Wireless Sensor Network (WSN) Scenario•Events travel hop-by-hop in a many-to-one traffic pattern towards the sink•Significant packet collision, congestion and loss may occur•The sensors nearest to the sink consume more energy than sensors further away from it•The operational lifetime of the overall system is considerably shortened

Objective: Mitigating the negative consequences of this bottleneck effect

Contribution• we provide an analytical framework for evaluating the performance of contention-based and contention-free access schemes, and• we propose a hybrid access scheme that incorporates the advantages of both approaches.

[P. S. Bithas, Α. Lioumpas, and A. Alexiou, "A Hybrid Contention/Reservation Medium Access Protocol for Wireless Sensor Networks," accepted for publication in Proc. GLOBECOM 2012, Dec. 2012.]

FUTURE WORK

•Extension to the multihop case will be investigated

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Energy-aware Relay Transmission Strategy (Tasks 2.1,2.3)

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Relays in wireless networks • extend coverage• provide higher network throughput• improve energy efficiency.

Contribution•compute the minimum required energy for achieving a specific bit error performance•combine sleep-wake mechanisms and cooperative communications to prolong network lifetime

ObjectivesEnhance energy efficiency Provide quality of service assurance to the receiverUnderstand fundamental aspects of cooperation and sleep-wake mechanisms

FUTURE WORK•Extension to multiple-antenna relays•Examine energy efficient schemes taking into account cooperation overhead

FUTURE WORK•Extension to multiple-antenna relays•Examine energy efficient schemes taking into account cooperation overhead

[G. Abou Elkheir, Α. Lioumpas, and A. Alexiou, “Energy Efficient Cooperative Scheduling based on Sleep-Wake Mechanisms“, WCNC, April 2012]