An Initial Load-Based Green Software Defined Network
Source abstract
Abstract
Software defined network (SDN) is a new network architecture in which the control function is decoupled from the data forwarding plane, that is attracting wide attentions from both research and industry sectors. However, SDN still faces the energy waste problem as do traditional networks. At present, research on energy saving in SDN is mainly focused on the static optimization of the network with zero load when new traffic arrives, changing the transmission path of the uncompleted traffic which arrived before the optimization, possibly resulting in route oscillation and other deleterious effects. To avoid this, a dynamical energy saving optimization scheme in which the paths of the uncompleted flows will not be changed when new traffic arrives is designed. To find the optimal solution for energy saving, the problem is modeled as a mixed integer linear programming (MILP) problem. As the high complexity of the problem prohibits the optimal solution, an improved heuristic routing algorithm called improved constant weight greedy algorithm (ICWGA) is proposed to find a sub-optimal solution. Simulation results show that the energy saving capacity of ICWGA is close to that of the optimal solution, offering desirable improvement in the energy efficiency of the network.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Software-Defined Networks and 5G · Advanced Optical Network Technologies · Advanced MIMO Systems Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Beijing Key Laboratory of Advanced Information Networks, Beijing University of Posts and Telecommunications, Beijing 100876, China
References (24)
Show source-provided references
1. OpenFlow: Enabling innovation in campus networks10.1145/1355734.1355746 ↗
2. Pickavet, M., Vereecken, W., Demeyer, S., Audenaert, P., Vermeulen, B., Develder, C., Colle, D., Dhoedt, B., and Demeester, P. (2008, January 15–17). Worldwide energy needs for ICT: The rise of power-aware networking. Proceedings of the 2008 2nd International Symposium on Advanced Networks and Telecommunication Systems, Mumbai, India.10.1109/ANTS.2008.4937762 ↗
3. Jain, S., Kumar, A., Mandal, S., Ong, J., Poutievski, L., Singh, A., Venkata, S., Wanderer, J., Zhou, J., and Zhu, M. (2013, January 12–16). B4: Experience with a globally-deployed software defined wan. Proceedings of the ACM SIGCOMM 2013 Conference on SIGCOMM, Hong Kong, China.10.1145/2486001.2486019 ↗
4. Chabarek, J., Sommers, J., Barford, P., Estan, C., Tsiang, D., and Wright, S. (2008, January 13–18). Power awareness in network design and routing. Proceedings of the 27th IEEE Conference on Computer Communications (INFOCOM 2008), Phoenix, AZ, USA.10.1109/INFOCOM.2008.93 ↗
5. Bousia, A., Antonopoulos, A., Alonso, L., and Verikoukis, C. (2012, January 10–15). “Green” distance-aware base station sleeping algorithm in LTE-Advanced. Proceedings of the IEEE International Conference on Communications, Ottawa, ON, Canada.10.1109/ICC.2012.6364240 ↗
6. Bousia, A., Kartsakli, E., Alonso, L., and Verikoukis, C. (2012, January 3–7). Dynamic energy efficient distance-aware Base Station switch on/off scheme for LTE-advanced. Proceedings of the 2012 IEEE Global Communications Conference (GLOBECOM), Anaheim, CA, USA.10.1109/GLOCOM.2012.6503331 ↗
7. Dynamic Base Station Switching-on/off Strategies for Green Cellular Networks10.1109/TWC.2013.032013.120494 ↗
8. Cell zooming for cost-efficient green cellular networks10.1109/MCOM.2010.5621970 ↗
9. Multiobjective Auction-Based Switching-off Scheme in Heterogeneous Networks: To Bid or Not to Bid?10.1109/TVT.2016.2517698 ↗
10. Gupta, M., and Singh, S. (2003, January 25–29). Greening of the Internet. Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, ACM, Karlsruhe, Germany.10.1145/863955.863959 ↗
11. Energy management through optimized routing and device powering for greener communication networks10.1109/TNET.2013.2249667 ↗
12. Energy-minimized design for IP over WDM networks10.1364/JOCN.1.000176 ↗
13. ElasticTree: Saving Energy in Data Center Networks
14. Hu, Y., Luo, T., Wang, W., and Deng, C. (2016, January 5–7). GreSDN: Toward a green software defined network. Proceedings of the 2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS), Kanazawa, Japan.
15. Hu, Y., Luo, T., Beaulieu, N., and Deng, C. (2016). The Energy-Aware Controller Placement Problem in Software Defined Networks. IEEE Commun. Lett.10.1109/LCOMM.2016.2645558 ↗
16. GreCo: An Energy Aware Controller Association Algorithm for Software Defined Networks10.1109/LCOMM.2015.2394457 ↗
17. Energy-saving model for SDN data centers10.1007/s11227-014-1237-3 ↗
18. Giroire, F., Moulierac, J., and Phan, T.K. (2014, January 8–12). Optimizing Rule Placement in Software-Defined Networks for Energy-aware Routing. Proceedings of the IEEE GLOBECOM, Austin, TX, USA.10.1109/GLOCOM.2014.7037187 ↗
19. A note on the complexity of Dijkstra’s algorithm for graphs with weighted vertices10.1109/12.663776 ↗
20. (2014, August 15). SNDlib: Library of Test Instance for Survivable Fixed Telecommunication Network Design. Available online: http://sndlib.zib.de/home.action.
21. (2015, May 21). Brite: Boston University Representative Internet Topology Generator. Available online: http://www.cs.bu.edu/brite/.
22. Generating realistic ISP-level network topologies10.1109/LCOMM.2003.814708 ↗
23. The problem of synthetically generating IP traffic matrices: Initial recommendations10.1145/1070873.1070876 ↗
24. (2014, October 21). CPLEX: IBM’s Linear Programming Solver. Available online: http://www.ilog.com/product/cplex/.