A Genetic Based Resource Management Algorithm Considering Energy Efficiency in Cloud Computing Systems

Subject Areas : Cloud, Cluster, Grid and P2P Computing

Marzieh Bozorgi Elize ¹ (Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Ahmad KhademZadeh ² (Department of Education and International Cooperation, Iran Telecommunication Research Center, Tehran, Iran)

Received: 2017-03-17 Accepted : 2017-09-12 Published : 2017-11-01

Keywords:

References:

[1] Zhang, Q., Cheng, L. and Boutaba, R., 2010. Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications, Springer, pp. 7-18.
[2] Calheiros, R. N. Ranjan, R. and Buyya, R., 2011, Virtual machine provisioning based on analytical performance and qos in cloud computing environments, International Conference, (pp. 295-304). IEEE.
[3] Chen, J. L. Larosa, Y. T. and Yang, P. J., 2012, “Optimal QoS Load Balancing Mechanism,” IEEE for 2nd baltic congress on future internet communication, (pp. 214-221).
[4] Beloglazov, A. and Buyya, R., 2010. Energy efficient resource management in virtualized cloud data centers. In Proceedings of the 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, IEEE Computer Society, (pp. 826-831). IEEE/ACM.
[5] Laszewski, G. V., Wang, L., Younge, A. J. and He, X., 2009. Power-aware scheduling of virtual machines in dvfs-enabled clusters. In Cluster Computing and Workshops, CLUSTER'09. IEEE International Conference on (pp. 1-10). IEEE.
[6] Buyya, R., Beloglazov, A. and Abawajy, J., 2010. Energy-efficient management of data center resources for cloud computing: A vision, architectural elements, and open challenges. arXiv preprint arXiv: 1006.0308.
[7] Lin, M., Wierman, A., Andrew, L. L. H. and Thereska, E., 2011. Dynamic right-sizing for power-proportional data centers. IEEE INFOCOM, Proceedings, pp. 1098-1106.
[8] Pakbaznia, E. and Pedram, M., 2009. Minimizing data center cooling and server power costs. In Proceedings of the 14th ACM/IEEE international symposium on Low power electronics and design, (pp 145-150). ACM/IEEE.
[9] Lee, Y. C. and Zomaya, A. Y., 2012. Energy efficient utilization of resources in cloud computing systems. The Journal of Supercomputing, pp. 268-280.
[10] Beloglazov, A. and Buyya, R., 2012. Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers. Published online in Wiley InterScience.
[11] Beloglazov, A., Buyya, R., Lee, Y. C. and Zomaya, A., 2011. A taxonomy and survey of energy-efficient data centers and cloud computing systems. Advances in Computers., vol. 82, Advances in Computers, pp. 47-111.
[12] Mesbahi, M. R, Rahmani, A. M., Hosseinzadeh, M., 2017. Highly reliable architecture using the 80/20 rule in cloud computing datacenters, Future Generation Computer Systems, PP. 77-86.
[13] Chen, G., He, W., Liu, J., Nath, S., Rigas, L., Xiao, L. and Zhao, F., 2008. Energy-aware server provisioning and load dispatching for connection-intensive internet services. In Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation, (pp. 337-350).
[14] Chase, J. S., Anderson, D. C., P Thakar,. N. A. Vahdat, M. and Doyle, R. P., 2001. Managing energy and server resources in hosting centers. In ACM SIGOPS Operating Systems Review, pp. 103-116.
[15] Zhang, L. and Ardagna, D., 2004. SLA based profit optimization in autonomic computing systems. ACM In Proceedings of the 2nd international conference on Service oriented computing, (pp. 173-182). ACM.
[16] Chen, Y., Das, A., Qin, W., Sivasubramaniam, A., Wang, Q. and Gautam, N., 2005. Managing server energy and operational costs in hosting centers. In ACM SIGMETRICS Performance Evaluation Review., pp. 303-314.
[17] Kim, K. H., Beloglazov, A. and Buyya, R.,2009. Power-aware provisioning of cloud resources for real-time services. In Proceedings of the 7th International Workshop on Middleware for Grids, Clouds and e-Science, (pp. 1). ACM.
[18] Abbasi, Z., Mukherjee, T., Varsamopoulos, G. and KS Gupta, S., 2012. A Green and Dynamic Web Application Hosting Manager Across Geographically Distributed Data Centers. ACM Journal on Emerging echnologies in Computing Systems (JETC), Volume 8 Issue 4.
[19] Buchbinder, N., Jain, N. and Menache, I., 2011. Online job migration for reducing the electricity bill in the cloud. Networking 2011, pp. 172-185.
[20] Adnan, M. A., Sugihara, R. and Gupta, R. K., 2012. Energy Efficient Geographical Load Balancing via Dynamic Deferral of Workload. In Cloud Computing (CLOUD), 5th International Conference, 2012, (pp. 188-195). IEEE
[21] Ghoreyshi, S. M., 2013 .Energy-Efficient Resource Management of Cloud Datacenters under Fault Tolerance Constraints. (pp. 1-6). IEEE.
[22] Bala, A. and Chana, I., 2012. Fault Tolerance-Challenges, Techniques and Implementation in Cloud Computing. IJCSI International Journal of Computer Science Issues., vol. 9, no. 1.
[23] Wu, Y., Yuan, Y., Yang, G. and Zheng, W., 2010. An adaptive task-level fault-tolerant approach to Grid. The Journal of Supercomputing 51, no. 2 (2010), pp. 97-114.
[24] Buyya R. and Murshed, M., 2002. GridSim: A Toolkit for the Modeling and Simulation of Distributed Resource Management and Scheduling for Grid Computing. Concurrency and Computation Practice and Experience, 14(15-13), Wiley Press.
[25] Kim, K. H., Buyya, R. and Kim, J., 2007. Power aware scheduling of bag-of-tasks applications with deadline constraints on DVS-enabled clusters. IEEE Computer Society, In Proceedings of the seventh international symposium on cluster computing and the grid, (pp. 541-548), IEEE.
[26] Calheiros, R., Ranjan, R., Beloglazov, A., De Rose, F. and Buyya, R., 2011. CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms. IEEE, pp.1-24.
[27] Zamani, A. R., Zoua, M., Montesa, J. D., Petrib, I., Rana, O., Parashara, M., 2017. A computational model to support in-network data analysis in federated ecosystems, Future Generation Computer Systems, PP. 1-13.
[28] Garg, S. K., Yeo, C. S., Anandasivam, A. and Buyya, R., 2011. Environment-conscious scheduling of HPC applications on distributed cloud-oriented data centers. Journal of Parallel and Distributed Computing, pp.732-749.
[29] Petrucci, V., Loques, O. and Mossé, D., 2010. Dynamic optimization of power and performance for virtualized server clusters. In Proceedings of the ACM Symposium on Applied Computing, pp. 263-264.
[30] Ranganathan, P., Leech, P., Irwin, D. and Chase, J., 2006. Ensemble-level power management for dense blade servers. IEEE Computer Society, In ACM SIGARCH Computer Architecture News, pp. 66-77.
[31] Pering, T. and Brodersen, R., 1998. Energy efficient voltage scheduling for real-time operating systems. In Proceedings of the 4th Real-Time Technology and Applications Symposium RTAS’98, Work in Progress Session. IEEE.
[32] Cokey, D. B., Brown, A. N., Caravaca-Aguirre, A. M. and Piestun, R., 2012. Genetic algorithm optimization for focusing through turbid media in noisy environments. Optical society of America.
[33] Sivanandam S.N. and Deepa, S. N., 2007. Introduction to Genetic Algorithms, Springer, ISBN 9783540731894.
[34] Soni, N., Kumar,T., 2014. Study of Various Mutation Operators in Genetic Algorithm, International Journal of Computer Science and Information Technologies, pp. 4519-4521.
[35] Jalali Varnamkhasti, M. L., Lee,S. Abu Bakar, M. R., and Leong, W. J., 2012. A Genetic Algorithm with Fuzzy Crossover Operator and Probability. Advance in Operation Research, pp. 1-16.
[36] Shresths, A. and Mahmood, A., 2016. Improving Genetic Algorithm with Fine – Tuned Crossover and Scaled Architecture. Journal of Mathematics, pp. 1-10.