A Hybrid Method for Reduction of Energy Consumption in Cloud Networks
Mehran Tarahomi, Mohammad Izadi Kish International Campus Sharif University of Technology Tehran, Iran, Department of Computer Engineering Sharif University of Technology Tehran, Iran
Abstract: Cloud computing is consisted of physical data centers each of which including hundreds or thousands of
computers. A key technology which has enabled cloud computing feasible, is virtualization. Virtualization allows us to
separate virtual machines in a way that each of these so-called virtualized machines can be configured on a number of hosts
according to the type of application of the user. It is also possible to dynamically alter the allocated resources of a virtual
machine. Different methods of energy saving in data centers can be divided to three general categories: 1) energy saving
methods based on balancing the loads of resources; 2) scheduling by the help of hardware facilities; 3) reducing the consumption
of energy through being aware of thermal characteristics of the environment.
One of the most important challenges in terms of cloud computing is to maintain an optimized load balance in cloud environment
on servers because this issue is critical for energy consumption. Since load balance algorithms depend on the current
behavior of the system, they act dynamically. Important factors which should be taken into account while developing such
types of algorithms include load estimation, load ratio, adaptability of different systems, performance of the system, interaction
between nods, scheduling, nature of the transferred service, selecting nodes and etc.
By taking a detailed look on previous methods and challenges of energy saving in cloud networks, we provide a hybrid method
which enables us to save energy through finding a suitable configuration for placement of virtual machines and through being
aware of special features of virtual environments for scheduling and balancing their dynamic loads by the help of live
migration method.
Keywords: Cloud Networks, Virtual Machines, Energy Saving, Scheduling, Load Balance, Live Migration, Cloudsim, DVFS A Hybrid Method for Reduction of Energy Consumption in Cloud Networks
References: [1] Wang, L., Ranjan, R., Chen, J., Benatallah, B. (2011). Cloud Computing: Methodology, Systems, and Applications. CRC Press,2011. [2] Chandrasekaran, K . (2014). Essentials of Cloud Computing. Taylor & Francis, 2014. [3] Schaefer, D. (2014). Cloud-Based Design and Manufacturing (CBDM): A Service-Oriented Product Development Paradigm for the 21st Century. Springer International Publishing, 2014. [4] Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., Brandic, I. (2009).Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility, Futur. Gener. Comput. Syst., 25 (6) 17, 2009. [5] Rittinghouse, J. W., Ransome, J. F. (2009). Cloud Computing: Implementation, Management, and Security. CRC Press. [6] Keahey, K., Tsugawa, M., Matsunaga, A., and Fortes, J. (2009). Sky computing, IEEE Internet Comput., 13, no. October, p. 43–51. [7] Khalil, I., Khreishah, A., Azeem, M. (2014). Cloud Computing Security: A Survey, Computers, 3 (1) 1–35, Feb. [8] Beloglazov, A., Buyya, R. (2012). Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers, Concurr. Comput. Pract. Exp., V. 24, p. 1397–1420. [9] Ding, Y., Qin, X., Liu, L., and Wang, T. (2015). Energy efficient scheduling of virtual machines in cloud with deadline constraint, Futur. Gener. Comput. Syst., vol. 50, p. 62–74, 2015. [10] Aslazandeh, S., Chaczko, Z., Chiu, C. (2014). Cloud computing #x2014; The effect of generalized spring tensor algorithm on load balancing, In: Computer Aided System Engineering (APCASE), 2014 Asia-Pacific Conference on, 2014, p. 5–8. [11] Chen, L., Shen, H., Sapra, K. (2014). RIAL: Resource Intensity Aware Load balancing in clouds, In: INFOCOM, 2014 Proceedings IEEE, 2014, p 1294–1302. [12] Domanal, S. G., and Reddy, G. R. M. (2014). Optimal load balancing in cloud computing by efficient utilization of virtual machines, In: Communication Systems and Networks (COMSNETS), 2014 Sixth International Conference on, 2014, p1–4. [13] Mashaly, M., and Kuhn, P. J. (2012). Load balancing in cloud-based content delivery networks using adaptive server activation/deactivation, In: Engineering and Technology (ICET), 2012 International Conference on, 2012, p. 1–6. [14] Ajit, M., Vidya, G. (2013). VM level load balancing in cloud environment, In: Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on, 2013, p. 1–5. [15] Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T., Ho, A., Neugebauer, R., Pratt, I., Warfield, A. (2003). Xen and the art of virtualization, ACM SIGOPS Oper. Syst. Rev., vol. 37, p. 164, 2003. [16] Calheiros, R. N. (2009). Automated Emulation of Distributed Systems through System Management and Virtualization, PhD Thesis, Pontif. Cathol. Univ. Rio Gd. do Sul Porto Alegre, Brazil, 2009. [17] Shaw, S. B., Singh, A. K. (2014). A survey on scheduling and load balancing techniques in cloud computing environment, In: Computer and Communication Technology (ICCCT), In: 2014 International Conference on, 2014, p. 87–95. [18] Abrahamsson, P., Helmer, S., Phaphoom, N., Nicolodi, L., Preda, N., Miori, L., Angriman, M., Rikkila, J., Wang, X., Hamily, K., Bugoloni, S. (2013).Affordable and Energy-Efficient Cloud Computing Clusters: The Bolzano Raspberry Pi Cloud Cluster Experiment,” Cloud Computing Technology and Science (CloudCom), In: 2013 IEEE 5th International Conference on, vol. 2. p 170–175, 2013. [19] Devadas, S., Malik, S. (1995). A survey of optimization techniques targeting lowpower VLSI circuits, In: 32nd Annual ACM/ IEEE Design AutomationConference, p. 242-247, Spain. [20] Petrucci, V., Loques, O., Mosse, D. (2010).Dynamic optimization of power andperformance for virtualized server clusters, In: Proceedings of the 2010 ACMSymposium on Applied Computing, p. 263-264, USA, 2010. [21] von Laszewski, G., Wang, L., Younge, A. J., He, X. (2009). Power-awarescheduling of virtual machines in dvfs-enabled clusters, In: ClusterComputing and Workshops, IEEE International Conference on ClusterComputing, p. 1-10, 2009. [22] Rotem, E., Naveh, A., Moffie, M., Mendelson, A. (2004). Analysis of thermalmonitor features of the Intel Pentium processor, TACS Workshop,Hongkong,. [23] Kusic, D., Kephart, J. O., Hanson, J. E., Kandasamy, N., and Jiang, G. (2009). Powerand performance management of virtualized computing environments vialookahead control, Cluster computing, vol. 12, p. 1-15, 2009. [24] Elnozahy, E., Kistler, M., Rajamony, R. (2003). Energy-efficient server clusters, In: Workshop on Power-Aware Computer Systems, p. 179-197, Italy, 2003. [25] Beloglazov, A., Buyya, R. (2010). Energy efficient resource management invirtualized cloud data centers, IEEE/ACM International Conference onCluster, Cloud and Grid Computing, p. 826-831, Sydney, 2010. [26] Tang, Q., Gupta, S., and Varsamopoulos, G. (2007). Thermal-aware task schedulingfor data centers through minimizing heat recirculation, IEEE InternationalConference on Cluster Computing, p. 129-138, USA, 2007. [27] Tang, Q.,Gupta, S. K. S., Varsamopoulos, G. (2008). Energy-efficient thermalawaretask scheduling for homogeneous highperformance computing datacenters: A cyber-physical approach, Parallel and Distributed Systems, IEEETransactions on, vol. 19, p. 1458-1472. [28] Banerjee, S., Adhikari, M., Biswas, U. (2014). Development of a smart job allocation model for a Cloud Service Provider, In: Business and Information Management (ICBIM), In: 2014 2nd International Conference on, 2014, p. 114–119. [29] Chang, F., Viswanathan, R., Wood, T. L. (2012). Placement in Clouds for Application-Level Latency Requirements, In: Cloud Computing (CLOUD), In: 2012 IEEE 5th International Conference on, 2012, p. 327–335. [30] Alakeel, A. M. (2010). A Guide to Dynamic Load Balancing in Distributed Computer Systems, Int. J. Comput. Sci. Inf. Secur., 10 ( 6) 153–160, 2010. [31] Yan, S., Wang, X., Razo, M., Tacca, M., and Fumagalli, A. (2014). Data center selection: A probability based approach, Transparent Optical Networks (ICTON), In:2014 16th International Conference on. p. 1–5, 2014. [32] Wang, H., Bergman, K. (2012). Optically interconnected data center architecture for bandwidth intensive energy efficient networking, Transparent Optical Networks (ICTON), 2012 14th International Conference on. p. 1–4, 2012. [33] Beloglazov, A., and Buyya, R. (2010). Energy Efficient Resource Management in Virtualized Cloud Data Centers, Cluster, Cloud and Grid Computing (CCGrid), In: 2010 10th IEEE/ACM International Conference on. p. 826–831, 2010. [34] Ghosh, T. K., Goswami, R., Bera S., and Barman, S.(2012). Load balanced static grid scheduling using Max-Min heuristic,” In:Parallel Distributed and Grid Computing (PDGC), 2012 2nd IEEE International Conference on, 2012, p. 419–423. [35] Urgaonkar, R., Kozat, U. C., Igarashi, K., Neely, M. J. (2010). Dynamic resource allocation and power management in virtualized data centers, Netw. Oper. Manag. Symp. NOMS 2010 IEEE, no. Vm, p. 479–486, 2010. [36] Adhikari, J., Patil, S. (2013). Double threshold energy aware load balancing in cloud computing, in Computing, In: Communications and Networking Technologies (ICCCNT),2013 Fourth International Conference on, 2013, p 1–6.