Telecommunication Networks and integrated Services (TNS) Laboratory Department of Digital Systems University of Piraeus Research Center (UPRC) University of Piraeus Green Footprint Prof. P, Demestichas, Assist. Prof. A Rouskas, M. Logothetis {pdemest, arouskas, arouskas,

TNS – Green Footprint Outline  Introduction - Research Areas - Motivation  Energy efficient Resource Allocation to femtocells  Problem Statement  Proposed Solution  Indicative Results  Conclusion – Future Work  Operator-driven Traffic Engineering in Core Networks  Problem Formulation  Proposed Solution  Indicative Results  Conclusion – Future Work  Disseminations 2

TNS – Green Footprint Introduction / Research Areas 3 Research Areas  Wireless Access  High-speed, wireless-access, infrastructures (2G, 3G, B3G, 4G).  Fixed Access – Core Network  Optical Networks (WDM, SONET) Fixed access networks (xDSL, FTTx,) Emerging wireless world

TNS – Green Footprint Motivation  The estimation for 2020 : mobile communication infrastructures will represent more than 50% of network CO 2 emissions.  Need for reduction of transmission powers and energy consumption in Wireless and Fixed Access 4 Global telecoms footprint [2002 & 2020]

TNS – Green Footprint Problem Statement 5 Problematic situation  All terminals are served through the BS  Congestion issues arise  Inadequate QoS (delivery probability, delay, etc.) to the terminals Femtocells are the opportunity that is exploited  They offer their resources for the relief of the congested BS Opportunistic Network Creation  Terminals are offloaded to femtocells  BS is no longer congested  Terminals experience higher QoS Energy efficiency  Femtocells are configured to operate at the minimum possible power level required to cover the terminals  Switch off femtocells that have not acquired traffic Opportunistic Networks are operator governed extensions of the infrastructure

TNS – Green Footprint 6 Process: 1. Selection of femtocells which are nearest to the terminals that will participate in the ON 2. Initial configuration of femtocells to the max power level 3. Assignment of traffic to femtocells 4. Selection of femtocells that can decrease their power level 5. Gradually decrease the power level of each femtocell to the minimum level that the constraints (coverage and capacity) are not violated Solution - Energy efficient Resource Allocation to femtocells Input:  The congested BS and its capabilities: RAT, Capacity, Coverage  Set of deployed femtocells and their capabilities : RAT, Capacity, Set of possible transmission powers  Terminals information: RAT, Location, Mobility level, Sensitivity Output:  The allocation of transmission powers to the femtocells  The assignment of terminals to femtocells

TNS – Green Footprint Indicative Results [1/4] 7  The delivery probability  Increases after the solution enforcement  Increases as more terminals are offloaded to the femtocells  Decreases as the terminals’ mobility level increases  The delay  Decreases after the solution enforcement  Decreases as more terminals are offloaded to the femtocells  Increases as the terminals’ mobility level increases

TNS – Green Footprint Indicative Results [2/4] 8 Power and traffic allocation to the femtocells - For central user distribution many femtocells remain without traffic and are switched off - For sparse user distribution more terminals need to remain active to cover the traffic Output of Algorithm

TNS – Green Footprint Indicative Results [3/4] 9  BS energy consumption in relation with the number of femtocells  Energy consumption decreases as more femtocells are deployed  BS energy consumption in relation with the number of serving terminals  Energy consumption rises while more terminals are served through the BS

TNS – Green Footprint Indicative Results [1/4] 10  Femto-terminals need low transmission power to communicate with the femtocells  Increased battery lifetime (25% in average)  Battery’s residual capacity drops at lower rate

TNS – Green Footprint Conclusions – Future Work 11  Conclusions  The algorithm  Allocates the minimum possible transmission power to femtocells that is needed to cover the terminals that are suitable to be offloaded to femtocells  Switches off the femtocells that remain without traffic  Femtocells are an energy efficient solution  Decreased BS power consumption due to the redirection of a proportion of the terminals  Increased battery lifetime of femto-terminals due to the small distance between terminals and femtocells  Future Work  Frequency allocation by taking into account interferences from neighboring BSs in a general sense  Taking into account QoS requirements

TNS – Green Footprint Operator-driven Traffic Engineering in Core Networks 12 Computation of optimum routing configuration Monitoring Setting LSPs Policy RAN requests Video Servers Ingress LSRs Egress LSRs Base Stations Operator  Problem Statement: find the most suitable routing configuration to accommodate traffic demands, satisfying operator’s policies  Proposed Solution:  CORE - Multilayer Traffic Engineering: IP/MPLS over DWDM (for optical core networks)

TNS – Green Footprint Multi-layer Traffic Engineering (MLTE): IP/MPLS over DWDM Core Optical Networks 13  Problem Statement: find the most energy-efficient lightpath to accommodate the new traffic demand, while respecting the capacity of fibers and wavelengths.  Proposed Solution (CORE - Multilayer Traffic Engineering: IP/MPLS over DWDM)  Energy efficiency is achieved through the allocation of traffic to dedicated lightpaths, which are restricted at the optical layer only (optical bypass), when this is possible. Our proposed heuristic algorithm (ETAL) activates and exploits more network elements in order to find the necessary portions for establishing lightpaths without aggregating them.

TNS – Green Footprint Multi-layer Traffic Engineering (MLTE): IP/MPLS over DWDM Core Optical Networks 14  Find all paths  Order Paths  Find optimal lightpath  Minimum conversions  Dedicated lightpath  Optical bypassing  Enforce decision  GMPLS signaling  Update network’s status Heuristic Algorithm: Energy-aware allocation of traffic to lightpaths (ETAL)

TNS – Green Footprint Multi-layer Traffic Engineering (MLTE): IP/MPLS over DWDM Core Optical Networks 15  Evaluation: comparisons with energy-efficient routing schemes  Metrics: number of conversions, consumed power, number of activated fibers, number of activated wavelengths, number of activated paths, average length of activated paths Future Work Develop an updated cost function which will include proactive approach

TNS – Green Footprint Disseminations D. Karvounas, A. Georgakopoulos, D. Panagiotou, V. Stavroulaki, K. Tsagkaris, P. Demestichas, “Achieving energy efficiency through the opportunistic exploitation of resources of infrastructures comprising cells of various sizes”, Journal of Green Engineering, vol.2, issue 3, River Publishers, 2012  D. Karvounas, A. Georgakopoulos, V. Stavroulaki, N. Koutsouris, K. Tsagkaris, P. Demestichas, “Resource Allocation to Femtocells for Coordinated Capacity Expansion of Wireless Access Infrastructures”, accepted for publication at EURASIP Journal on Wireless Communications and Networking, Special Issue on Femtocells in 4G Systems, 2012  V. Foteinos, K. Tsagkaris, P. Peloso, L. Ciavaglia and P. Demestichas, “Energy Savings with Multilayer Traffic Engineering in Future Core Networks”, Journal of Green Engineering,  V. Foteinos, K. Tsagkaris, P. Peloso, L. Ciavaglia and P. Demestichas, “Energy-Aware Allocation of Traffic to Optical Lightpaths in Multilayer Core Networks”, submitted for publication to IEEE/OSA Journal of Lightwave Technology,  V. Foteinos, K. Tsagkaris, P. Peloso, L. Ciavaglia and P. Demestichas,” Energy Savings in Multilayer Core Networks”,submitted for publication to IEEE International Conference on Communications,