آمار
| تعداد نشریات | 50 |
| تعداد شمارهها | 2,232 |
| تعداد مقالات | 20,475 |
| تعداد مشاهده مقاله | 25,230,348 |
| تعداد دریافت فایل اصل مقاله | 22,863,003 |
A Genetic Based Resource Management Algorithm Considering Energy Efficiency in Cloud Computing Systems | ||
| Journal of Advances in Computer Engineering and Technology | ||
| مقاله 4، دوره 3، شماره 4 - شماره پیاپی 12، بهمن 2017، صفحه 203-212 اصل مقاله (462.54 K) | ||
| نوع مقاله: Original Research Paper | ||
| نویسندگان | ||
Marzieh Bozorgi Elize1؛ Ahmad KhademZadeh  2
| ||
| 1Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 2Department of Education and International Cooperation, Iran Telecommunication Research Center, Tehran, Iran | ||
| چکیده | ||
| Cloud computing is a result of the continuing progress made in the areas of hardware, technologies related to the Internet, distributed computing and automated management. The Increasing demand has led to an increase in services resulting in the establishment of large-scale computing and data centers, in addition to high operating costs and huge amounts of electrical power consumption. Insufficient cooling systems and inefficient, causing overheating sources, shortening the life of the machine and too much carbon dioxide is produced. In this paper, we aim to improve system performance; Cloud Computing based on a decrease in migration of among virtual machines (VM), and reduce energy consumption to be able to manage resources to achieve optimal energy efficiency. For this reason, various techniques such as genetic algorithms (GAs), virtual machine migration and ways Dynamic voltage and frequency scaling (DVFS), and resize virtual machines to reduce energy consumption and fault tolerance are used. The main purpose of this article, the allocation of resources with the aim of reducing energy consumption in cloud computing. The results show that reduced energy consumption and hold down the rate of virtual machines breach of contract, reduces migration as well. | ||
| کلیدواژهها | ||
| cloud computing؛ Energy efficiency؛ Genetic algorithm | ||
| مراجع | ||
|
[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. | ||
|
آمار تعداد مشاهده مقاله: 907 تعداد دریافت فایل اصل مقاله: 733 |
||
