Abstract
Resource utilization and energy need to be carefully handled for achieving virtualization in the cloud environment. An important aspect to be considered is that of load balancing, where the workload is distributed so that a particular node does not become overburdened with tasks. Improper load balancing will lead to losses in terms of both memory as well as energy consumption. The resources should be scheduled in a cloud in such a way that users obtain access at any time and with possibly less energy wastage. The proposed algorithm uses an improved Genetic Algorithm that helps reduce overall power consumption as well as performs scheduling of virtual machines so that the nodes are not loaded below or above their capacity. In this case, each chromosome in the population is considered to be a node. Each virtual machine is allocated to a node. The virtual machines on every node correspond to the genes of a chromosome. Crossover and mutation operations have been performed after which optimization techniques have been used to obtain the resulting allocation of tasks. The proposed approach has proved to be competent with respect to earlier approaches in terms of load balancing and resource utilization.
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References
Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J. Internet Serv. Appl. 1(1), 7–18 (2010)
Albers, S., Fujiwara, H.: Energy-efficient algorithms for flow time minimization. ACM Trans. Algorithms (TALG) 3(4), 49 (2007)
Cardosa, M., Korupolu, M.R., Singh, A.: Shares and utilities based power consolidation in virtualized server environments. In: IM’09. IFIP/IEEE International Symposium on Integrated Network Management, pp. 327–334. IEEE, Piscataway (2009)
Grit, L., Irwin, D., Yumerefendi, A., Chase, J.: Virtual machine hosting for networked clusters: Building the foundations for “autonomic” orchestration. In First International Workshop on Virtualization Technology in Distributed Computing VTDC 2006, p. 7. IEEE, Piscataway (2006)
Chaisiri, S., Lee, B.S., Niyato, D.: Optimal virtual machine placement across multiple cloud providers. In: Services Computing Conference, 2009. APSCC 2009. IEEE Asia-Pacific, pp. 103–110. IEEE, Piscataway (2009)
Bichler, M., Setzer, T. and Speitkamp, B.: Capacity Planning for Virtualized Servers (2006)
Speitkamp, B., Bichler, M.: A mathematical programming approach for server consolidation problems in virtualized data centers. IEEE Trans. Serv. Comput. 3(4), 266–278 (2010)
Van, H.N., Tran, F.D., Menaud, J.M.: July. Performance and power management for cloud infrastructures. In: 2010 IEEE 3rd International Conference on Cloud Computing (CLOUD), pp. 329–336. IEEE, Piscataway (2010)
Hermenier, F., Lorca, X., Menaud, J.M., Muller, G., Lawall, J.: Entropy: a consolidation manager for clusters. In: Proceedings of the 2009 ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, pp. 41–50. ACM, New York City (2009)
Goiri, I., Julia, F., Nou, R., Berral, J.L., Guitart, J., Torres, J.: Energy-aware scheduling in virtualized datacenters. In: 2010 IEEE International Conference on Cluster Computing (CLUSTER), (pp. 58–67). IEEE, Piscataway (2010)
Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012)
Quang-Hung, N., Nien, P.D., Nam, N.H., Tuong, N.H., Thoai, N.: A genetic algorithm for power-aware virtual machine allocation in private cloud. In: Information and Communication Technology-EurAsia Conference, pp. 183–191. Springer, Berlin, Heidelberg (2013)
Mi, H., Wang, H., Yin, G., Zhou, Y., Shi, D., Yuan, L.: Online self-reconfiguration with performance guarantee for energy-efficient large-scale cloud computing data centers. In: 2010 IEEE International Conference on Services Computing (SCC), pp. 514–521. IEEE, Piscataway (2010)
Xu, J., Fortes, J.A.: Multi-objective virtual machine placement in virtualized data center environments. In: Proceedings of the 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber, Physical and Social Computing, pp. 179–188. IEEE Computer Society (2010)
Gao, Y., Guan, H., Qi, Z., Hou, Y., Liu, L.: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J. Comput. Syst. Sci. 79(8), 1230–1242 (2013)
Feller, E., Rilling, L., Morin, C.: Energy-aware ant colony based workload placement in clouds. In: Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing, pp. 26–33. IEEE Computer Society (2011)
Gao, C., Wang, H., Zhai, L., Gao, Y., Yi, S.: An energy-aware ant colony algorithm for network-aware virtual machine placement in cloud computing. In: 2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS), pp. 669–676. IEEE (2016)
Kansal, N.J., Chana, I.: Energy-aware virtual machine migration for cloud computing-a firefly optimization approach. J. Grid Comput. 14(2), 327–345 (2016)
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Basu, S., Kannayaram, G., Ramasubbareddy, S., Venkatasubbaiah, C. (2019). Improved Genetic Algorithm for Monitoring of Virtual Machines in Cloud Environment. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 105. Springer, Singapore. https://doi.org/10.1007/978-981-13-1927-3_34
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DOI: https://doi.org/10.1007/978-981-13-1927-3_34
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