User-Network Cooperation-Based Sleep Scheduling for Communication Networks

The redundant design and dynamic nature of traffic raise an energy inefficiency issue in communication networks. We exploit the selfishness of both users and the network to schedule cooperatively the idle links and nodes into sleep to save energy. We first formulate the sleep scheduling problem from a perspective of routing, and then propose a greedy algorithm to solve the problem. To reduce the complexity of centralized computation, we further propose a user-network cooperation-based mechanism, where the network publishes a proportionally weighted cost-sharing rule related to energy consumption, while the users selfishly choose their routes with the least cost accordingly. The proposed cooperation mechanism attracts users to aggregate their traffic on fewer links and nodes. The network then simply puts the idle links and nodes into sleep. Selfish routing behaviors are modeled by an α-approximate routing game, where the α factor is adopted to consider the energy consumption, packet losses, and delay during re-routing. We prove the equilibrium existence, convergence, and convergence speed of the best responses, and evaluate the lower bound performance in terms of price of anarchy with further improvement by an advertisement method. Distributed algorithms based on the best responses are also developed to implement the cooperative mechanism. Simulation results over network instants from SNDlib show that our game-based algorithms outperform the greedy and heuristic centralized algorithms in saving energy.

[1]  Suresh Singh,et al.  The potential impact of green technologies in next-generation wireline networks: Is there room for energy saving optimization? , 2011, IEEE Communications Magazine.

[2]  Sujata Banerjee,et al.  Energy Aware Network Operations , 2009, IEEE INFOCOM Workshops 2009.

[3]  Shengming Jiang,et al.  QoS provisioning performance of IntServ, DiffServ and DQS with multiclass self-similar traffic , 2013, Trans. Emerg. Telecommun. Technol..

[4]  Suresh Singh,et al.  Greening of the internet , 2003, SIGCOMM '03.

[5]  L. Shapley,et al.  Potential Games , 1994 .

[6]  Vincent K. N. Lau,et al.  Efficient Energy-Aware Routing With Redundancy Elimination , 2015, IEEE Journal on Selected Areas in Communications.

[7]  R. Rosenthal A class of games possessing pure-strategy Nash equilibria , 1973 .

[8]  Franco Davoli,et al.  Energy Efficiency in the Future Internet: A Survey of Existing Approaches and Trends in Energy-Aware Fixed Network Infrastructures , 2011, IEEE Communications Surveys & Tutorials.

[9]  Michael Franz,et al.  Power reduction techniques for microprocessor systems , 2005, CSUR.

[10]  Marco Listanti,et al.  Implementing energy-aware algorithms in backbone networks: A transient analysis , 2015, 2015 IEEE International Conference on Communications (ICC).

[11]  L. Shapley,et al.  REGULAR ARTICLEPotential Games , 1996 .

[12]  Marco Mellia,et al.  Reducing Power Consumption in Backbone Networks , 2009, 2009 IEEE International Conference on Communications.

[13]  Sergiu Nedevschi,et al.  Reducing Network Energy Consumption via Sleeping and Rate-Adaptation , 2008, NSDI.

[14]  T. Roughgarden Potential functions and the inefficiency of equilibria , 2006 .

[15]  Marco Listanti,et al.  An Energy Saving Routing Algorithm for a Green OSPF Protocol , 2010, 2010 INFOCOM IEEE Conference on Computer Communications Workshops.

[16]  Allen B. MacKenzie,et al.  Effect of Selfish Node Behavior on Efficient Topology Design , 2008, IEEE Transactions on Mobile Computing.

[17]  Jing Wang,et al.  Green 5G Heterogeneous Networks Through Dynamic Small-Cell Operation , 2016, IEEE Journal on Selected Areas in Communications.

[18]  Tim Roughgarden,et al.  The price of stability for network design with fair cost allocation , 2004, 45th Annual IEEE Symposium on Foundations of Computer Science.

[19]  B. Dhoedt,et al.  Worldwide energy needs for ICT: The rise of power-aware networking , 2008, 2008 2nd International Symposium on Advanced Networks and Telecommunication Systems.

[20]  David Coudert,et al.  Robust energy-aware routing with redundancy elimination , 2015, Comput. Oper. Res..

[21]  Michal Pioro,et al.  SNDlib 1.0—Survivable Network Design Library , 2010 .

[22]  J. A. Tomlin,et al.  Minimum-Cost Multicommodity Network Flows , 1966, Oper. Res..

[23]  Jie Xu,et al.  Energy Group Buying With Loading Sharing for Green Cellular Networks , 2015, IEEE Journal on Selected Areas in Communications.

[24]  Dzmitry Kliazovich,et al.  DENS: Data Center Energy-Efficient Network-Aware Scheduling , 2010, GreenCom/CPSCom.

[25]  Rene L. Cruz,et al.  Optimal routing, link scheduling and power control in multihop wireless networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[26]  Stephen J. Wright,et al.  Power Awareness in Network Design and Routing , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[27]  Tim Roughgarden,et al.  Network Design with Weighted Players , 2006, SPAA '06.

[28]  Dario Rossi,et al.  A Survey of Green Networking Research , 2010, IEEE Communications Surveys & Tutorials.

[29]  Marco Mellia,et al.  Minimizing ISP Network Energy Cost: Formulation and Solutions , 2012, IEEE/ACM Transactions on Networking.

[30]  Min Zhu,et al.  B4: experience with a globally-deployed software defined wan , 2013, SIGCOMM.