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Research Team D

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 Systems University of Piraeus. Outline. Our team

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Research Team D

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  1. 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

  2. Outline Our team Research area and technical approach Contribution to WPs and tasks 2

  3. 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

  4. 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

  5. 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

  6. 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

  7. Profile [4|6]: Legacy assets • 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) • IMSplatforms • 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 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)

  8. 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

  9. 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)

  10. Outline Our team Research area and technical approach Contribution to WPs and tasks

  11. 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 11

  12. 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 www.smart2020.org

  13. 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 13

  14. 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

  15. 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

  16. 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

  17. Outline Our team Research area and technical approach Contribution to WPs and tasks

  18. Research Team D Tasks • 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

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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

  24. Contributions to Tasks 2.1 and 2.3 (Alexiou) Small Cell Networks (SCNs) for LTE-Advanced and Beyond (Task 2.1) Cluster-Head Selection Algorithm (Tasks2.1, 2.3) Energy Efficient Hybrid TDMA/CSMA technique (Tasks2.1, 2.3) Energy-aware Relay Transmission Strategy (Tasks2.1, 2.3)

  25. Small Cell Networks (SCNs) for LTE-Advanced and Beyond: Future Work (Task 2.1) • Increased wireless data demand • Increased number of interconnected devices SCNs Challenges • Interference Management complexity • Increased overhead for facilitating coordination among base stations • 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

  26. Cluster-Head Selection Algorithm (Tasks 2.1,2.3) • 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 limitations • Hindered direct link to the cellular infrastructure • Power consumption • Hardware complexity • Reliable solution for proving coverage extension  Cluster-head based networks • Objective: enhance • Energy efficiency • Reliability • 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

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

  28. Energy-aware Relay Transmission Strategy (Tasks 2.1,2.3) • Relays in wireless networks • extend coverage • provide higher network throughput • improve energy efficiency. • Objectives • Enhance energy efficiency • Provide quality of service assurance to the receiver • Understand fundamental aspects of cooperation and sleep-wake mechanisms • Contribution • compute the minimum required energy for achieving a specific bit error performance • combine sleep-wake mechanisms and cooperative communications to prolong network lifetime [G. Abou Elkheir, Α. Lioumpas, and A. Alexiou, “Energy Efficient Cooperative Scheduling based on Sleep-Wake Mechanisms“, WCNC, April 2012] • FUTURE WORK • Extension to multiple-antenna relays • Examine energy efficient schemes taking into account cooperation overhead

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