Reliable and Energy-Efficient Routing for Green Software Defined Networking

Improvement of energy efficiency in Software Defined Networking (SDN) is a challenging problem. In order to achieve higher energy efficiency, the routing and scheduling in software defined networks should be planned robustly based on the topological specifications of the SDN. The topological specifications of the SDN depends on the size of the network graph which changes rapidly as the virtualized network devices in the network turned on and off by the controller. Therefore, modelling the SDN graph is an important issue in designing a reliability aware energy efficient SDN. In this paper we propose a new algorithm for reliable energy efficient traffic engineering in SDN. In the proposed algorithm the SDN structure is modelled according to complex network definitions. The experimental results show that the proposed algorithm can optimize the traffic flow in SDN and reduce the energy consumption by 73%, while maintain reliability in the network.

[1]  Hannu Tenhunen,et al.  Using Ant Colony System to Consolidate VMs for Green Cloud Computing , 2015, IEEE Transactions on Services Computing.

[2]  Xiaodong Wang,et al.  Correlation-Aware Traffic Consolidation for Power Optimization of Data Center Networks , 2016, IEEE Transactions on Parallel and Distributed Systems.

[3]  Luca Prete,et al.  Energy Efficient Minimum Spanning Tree in OpenFlow Networks , 2012, 2012 European Workshop on Software Defined Networking.

[4]  Luca Caviglione,et al.  Predictive Control for Energy-Aware Consolidation in Cloud Datacenters , 2016, IEEE Transactions on Control Systems Technology.

[5]  Sujata Banerjee,et al.  ElasticTree: Saving Energy in Data Center Networks , 2010, NSDI.

[6]  MengChu Zhou,et al.  A Stochastic Approach to Analysis of Energy-Aware DVS-Enabled Cloud Datacenters , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[7]  Xuyun Zhang,et al.  EnReal: An Energy-Aware Resource Allocation Method for Scientific Workflow Executions in Cloud Environment , 2016, IEEE Transactions on Cloud Computing.

[8]  Jie Wu,et al.  Saving Energy in Partially Deployed Software Defined Networks , 2016, IEEE Transactions on Computers.

[9]  U. Brandes.  A faster algorithm for betweenness centrality , 2001 .

[10]  Massoud Pedram,et al.  Hierarchical SLA-Driven Resource Management for Peak Power-Aware and Energy-Efficient Operation of a Cloud Datacenter , 2016, IEEE Transactions on Cloud Computing.

[11]  Marta C. González,et al.  Cycles and clustering in bipartite networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Martin Suchara,et al.  Greening backbone networks: reducing energy consumption by shutting off cables in bundled links , 2010, Green Networking '10.

[13]  David A. Maltz,et al.  Network traffic characteristics of data centers in the wild , 2010, IMC '10.

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

[15]  Di Wu,et al.  Eco-Aware Online Power Management and Load Scheduling for Green Cloud Datacenters , 2016, IEEE Systems Journal.

[16]  David Hausheer,et al.  Measuring and modeling the power consumption of OpenFlow switches , 2014, 10th International Conference on Network and Service Management (CNSM) and Workshop.

[17]  Liesbet Van der Perre,et al.  Challenges and enabling technologies for energy aware mobile radio networks , 2010, IEEE Communications Magazine.

[18]  L. Chiaraviglio,et al.  Optimal Energy Savings in Cellular Access Networks , 2009, 2009 IEEE International Conference on Communications Workshops.

[19]  Xiaoning Zhang,et al.  Power-Efficient Provisioning for Online Virtual Network Requests in Cloud-Based Data Centers , 2015, IEEE Systems Journal.