Optimized Energy Aware Resource Allocation Algorithm Using Software Defined Network Technology
DOI:
https://doi.org/10.26636/jtit.2019.129418Keywords:
computer network management, network servers, software defined networking, virtual machinesAbstract
The number of data centers (DCs) used for storing and processing data has evolved rapidly in recent years. However, the operations held by DCs may relate to a number of disadvantages, primarily presuming in excessive energy and power consumption due to the poor management standards applied. This may lead to a situation in which many devices within the DC operate at full capacity without any tasks assigned for actual execution. A Software Defined Network (SDN) is a network architecture where the control plane is an independent entity from the data plane, yielding to a higher controllability and flexibility over the network. Through the utilization of SDN architecture, a highly functional energy aware network may be established. In this paper, we propose a heuristic algorithm that monitors the current status of an SDN network (in addition to all ingoing and outgoing traffic), in order to dynamically and efficiently allocate network resources by ensuring that only the necessary network devices are active and by turning the idle ones off. The results show that the proposed algorithm reduces energy consumption of the network compared to existing solutions.
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[1] C. Mastroianni, M. Meo, and G. Papuzzo, “Analysis of a selforganizing algorithm for energy saving in data centers”, in Proc. 2013 IEEE Int. Symp. on Parallel Distrib. Process., Worksh. and Phd Forum, Cambridge, MA, USA, 2013, pp. 907–914. DOI: https://doi.org/10.1109/IPDPSW.2013.184
View in Google Scholar
[2] D. A. Alboaneen, B. Pranggono, and H. Tianfield, “Energy-aware virtual machine consolidation for cloud data centers”, in Proc. 2014 IEEE/ACM 7th Int. Conf. on Util. and Cloud Comput., London, UK., 2014, pp. 1010–1015. DOI: https://doi.org/10.1109/UCC.2014.166
View in Google Scholar
[3] I. Widjaja, A. Walid, Y. Luo, Y. Xu, and H. J. Chao, “Small versus large: Switch sizing in topology design of energy-efficient data centers”, in Proc. 2013 IEEE/ACM 21st Int. Symp. on Qual. of Serv. IWQoS 2013, Montreal, QC, Canada, 2013, pp. 51–56. DOI: https://doi.org/10.1109/IWQoS.2013.6550264
View in Google Scholar
[4] J. Perel et al., “All-optical packet/circuit switching-based data center network for enhanced scalability, latency, and throughput”, IEEE Network, vol. 27, no. 6, pp. 14–22, 2013. DOI: https://doi.org/10.1109/MNET.2013.6678922
View in Google Scholar
[5] M. Seymour, “Is energy efficiency enough? Filling the engineering gap in data center design and operation”, in Proc. 15th IEEE Intersoc. Conf. on Thermal and Thermomech. Phenom. in Electron. Syst. ITherm 2016, Las Vegas, NV, USA, 2016, pp. 702–709. DOI: https://doi.org/10.1109/ITHERM.2016.7517616
View in Google Scholar
[6] A. Amokrane, M. F. Zhani, R. Langar, R. Boutaba, and G. Pujolle, “Greenhead: Virtual data center embedding across distributed infrastructures”, IEEE Trans. on Cloud Comput., vol. 1, no. 1, pp. 36–49, 2013. DOI: https://doi.org/10.1109/TCC.2013.5
View in Google Scholar
[7] B. Yu, Y. Han, X. Wen, X. Chen, and Z. Xu, “An energy-aware algorithm for optimizing resource allocation in software defined network”, in Proc. IEEE Global Commun. Conf. GLOBECOM 2016, Washington, DC, USA, 2016, pp. 1–7. DOI: https://doi.org/10.1109/GLOCOM.2016.7841589
View in Google Scholar
[8] E. Grigoriou, A. A. Barakabitze, L. Atzori, L. Sun, and V. Pilloni, “An SDN-approach for QoE management of multimedia services using resource allocation”, in Proc. IEEE Int. Conf. on Commun. ICC 2017, Paris, France, 2017, pp. 1–7. DOI: https://doi.org/10.1109/ICC.2017.7997261
View in Google Scholar
[9] B. Pavithra and R. Ranjana, “Energy efficient resource provisioning with dynamic VM placement using energy aware load balancer in cloud”, in Proc. Int. Conf. on Inform. Commun. and Embedded Syst. ICICES 2016, Chennai, India, 2016, pp. 1–6. DOI: https://doi.org/10.1109/ICICES.2016.7518919
View in Google Scholar
[10] S. Subbiah and V. Perumal, “Energy-aware network resource allocation in SDN”, in Proc. Int. Conf. on Wirel. Commun., Sig. Process. and Netw. WiSPNET 2016, Chennai, India, 2016, pp. 2071–2075. DOI: https://doi.org/10.1109/WiSPNET.2016.7566506
View in Google Scholar
[11] X. Wen, Y. Han, H. Yuan, X. Zhou, and Z. Xu, “An efficient resource embedding algorithm in software defined virtualized data center”, in Proc. IEEE Global Commun. Conf. GLOBECOM 2015, San Diego, CA, USA, 2015, pp. 1–7. DOI: https://doi.org/10.1109/GLOCOM.2015.7417556
View in Google Scholar
[12] Q. Zhang, Q. Zhu, M. F. Zhani, and R. Boutaba, “Dynamic service placement in geographically distributed clouds”, in Proc. IEEE 32nd Int. Conf. on Distrib. Comput. Syst., Macau, China, 2012, pp. 526–535. DOI: https://doi.org/10.1109/ICDCS.2012.74
View in Google Scholar
[13] S. Padma, K. Vijayalakshmi, and G. Sangameshwaran, “Power generation using hybrid renewable energy resources for domestic applications”, in Proc. Int. Conf. on Wirel. Commun., Sig. Process. and Netw. WiSPNET 2016, Chennai, India, 2016, pp. 1993–1998. DOI: https://doi.org/10.1109/WiSPNET.2016.7566491
View in Google Scholar
[14] A. Qureshi, R. Weber, H. Balakrishnan, J. Guttag, and B. Maggs, “Cutting the electric bill for internet-scale systems”, SIGCOMM Comput. Commun. Rev., vol. 39, no. 4, pp. 123–134, 2009. DOI: https://doi.org/10.1145/1594977.1592584
View in Google Scholar
[15] M. Rahnamay-Naeini, S. S. Baidya, E. Siavashi, and N. Ghani, “A traffic and resource-aware energy-saving mechanism in software defined networks”, in Proc. Int. Conf. on Comput., Netw. and Commun. ICNC 2016, Kauai, HI, USA, 2016, pp. 1–5. DOI: https://doi.org/10.1109/ICCNC.2016.7440553
View in Google Scholar
[16] T. Yang, Y. C. Lee, and A. Y. Zomaya, “Energy-efficient data center networks planning with virtual machine placement and traffic configuration”, in Proc. IEEE 6th Int. Conf. on Cloud Comput. Technol. and Sci., Singapore, 2014, pp. 284–291. DOI: https://doi.org/10.1109/CloudCom.2014.135
View in Google Scholar
[17] K. Zhang, T. Wu, S. Chen, L. Cai, and C. Peng, “A new energy efficient VM scheduling algorithm for cloud computing based on dynamic programming”, in Proc. IEEE 4th Int. Conf. on Cyber Secur. and Cloud Comput. CSCloud 2017, New York, NY, USA, 2017, pp. 249–254. DOI: https://doi.org/10.1109/CSCloud.2017.46
View in Google Scholar
[18] M. K. Patterson, “The effect of data center temperature on energy efficiency”, in Proc. 11th Intersoc. Conf. on Thermal and Thermomech. Phenom. in Electron. Syst., Orlando, FL, USA, 2008, pp. 1167–1174. DOI: https://doi.org/10.1109/ITHERM.2008.4544393
View in Google Scholar
[19] N. T. Hieu, M. D. Francesco, and A. Yl-Jski, “Virtual machine consolidation with usage prediction for energy-efficient cloud data centers”, in Proc. IEEE 8th Int. Conf. on Cloud Comput., New York, NY, USA, 2015, pp. 750–757. DOI: https://doi.org/10.1109/CLOUD.2015.104
View in Google Scholar
[20] A. Markiewicz, P. N. Tran, and A. Timm-Giel, “Energy consumption optimization for software defined networks considering dynamic traffic”, in Proc. IEEE 3rd Int. Conf. on Cloud Netw. CloudNet 2014, Luxembourg, 2014, pp. 155–160. DOI: https://doi.org/10.1109/CloudNet.2014.6968985
View in Google Scholar
[21] D. Henni, Y. Hadjaj-Aoul, and A. Ghomari, “Probe-SDN: A smart monitoring framework for SDN-based networks”, in Proc. Global Inform. Infrastruc. and Netw. Symp. GIIS 2016, Porto, Portugal, 2016, pp. 1–6. DOI: https://doi.org/10.1109/GIIS.2016.7814940
View in Google Scholar
[22] C. Zhang, X. Huang, G. Ma, and X. Han, “A dynamic scheduling algorithm for bandwidth reservation requests in software-defined networks”, in Proc. 10th Int. Conf. on Informa., Commun. and Sig. Process. ICICS 2015, Singapore, 2015, pp. 1–5. DOI: https://doi.org/10.1109/ICICS.2015.7459856
View in Google Scholar
[23] K. Liu, Y. Cao, Y. Liu, G. Xie, and C. Wu, “A novel min-cost QoS routing algorithm for SDN-based wireless mesh network”, in Proc. 2nd IEEE Int. Conf. on Comp. and Commun. ICCC 2016, Chengdu, China, 2016, pp. 1998–2003. DOI: https://doi.org/10.1109/CompComm.2016.7925051
View in Google Scholar
[24] S. Tomovic, I. Radusinovic, and N. Prasad, “Performance comparison of QoS routing algorithms applicable to large-scale SDN networks”, in Proc. IEEE EUROCON 2015 – Int. Conf. on Comp. as a Tool EUROCON 2015, Salamanca, Spain, 2015, pp. 1–6. DOI: https://doi.org/10.1109/EUROCON.2015.7313698
View in Google Scholar
[25] Y. Guo, Z. Wang, X. Yin, X. Shi, and J. Wu, “Optimize routing in hybrid SDN network with changing traffic”, in Proc. 26th Int. Conf. on Comp. Commun. and Netw. ICCCN 2017, Vancouver, BC, Canada, 2017, pp. 1–8. DOI: https://doi.org/10.1109/ICCCN.2017.8038397
View in Google Scholar
[26] Y. Han, J. Li, J.-Y. Chung, J.-H. Yoo, and J. W. Hong, “SAVE: Energy-aware virtual data center embedding and traffic engineering using SDN”, in Proc. of the 2015 1st IEEE Conf. on Netw. Softwariz. NetSoft 2015, London, UK, 2015, pp. 1–9. DOI: https://doi.org/10.1109/NETSOFT.2015.7116142
View in Google Scholar
[27] A. Ishimori, F. Farias, E. Cerqueira, and A. Abelm, “Control of multiple packet schedulers for improving QoS on OpenFlow/SDN networking”, in Proc. 2nd Eur. Worksh. on Software Defined Netw., Berlin, Germany, 2013, pp. 81–86. DOI: https://doi.org/10.1109/EWSDN.2013.20
View in Google Scholar
[28] J. Pang, G. Xu, and X. Fu, “SDN-based data center networking with collaboration of multipath TCP and segment routing”, IEEE Access, vol. 5, pp. 9764–9773, 2017. DOI: https://doi.org/10.1109/ACCESS.2017.2700867
View in Google Scholar
[29] A. Bentaleb, A. C. Begen, R. Zimmermann, and S. Harous, “SDNHAS: An SDN-enabled architecture to optimize QoE in HTTP adaptive streaming”, IEEE Trans. on Multimed., vol. 19, no. 10, pp. 2136–2151, 2017. DOI: https://doi.org/10.1109/TMM.2017.2733344
View in Google Scholar
[30] P. Desmond, “5 contributors to data center energy inefficiency”, 2011.
View in Google Scholar
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