محاسبات ابری سبز با کاهش مصرف انرژی در مهاجرت زنده اولویت‌دار سرویس‌ها
الموضوعات :
1 - دانشگاه کاشان
2 - دانشگاه کاشان
الکلمات المفتاحية: رایانش ابری سرویس‌های همکارکاهش مصرف انرژی مهاجرت زنده سرویس‌ها,
ملخص المقالة :
امروزه رشد سریع تقاضا برای استفاده از منابع محاسباتی ابری، موجب افزایش مصرف انرژی در مراکز داده شده است. محاسبات ابری سبز برای جلوگیری از افزایش مصرف انرژی مراکز داده بیان شده است. یکی از راهکارهای کاهش مصرف انرژی در سیستم‌های پردازش ابری، استفاده از روش‌های تجمیع سرویس‌ها است. روش‌های تجمیع موجود با مهاجرت‌های غیر ضروری، عدم تعادل بار کاری میزبان‌ها و نادیده‌گرفتن ارتباط بین سرویس‌ها ممکن است باعث کاهش کیفیت سرویس و افزایش مصرف انرژی شود. لذا در این تحقیق با مهاجرت‌دادن سرویس‌های ضروری بر اساس اولویت (شامل تعداد فرزندان، سطح و هزینه ارتباطی هر سرویس)، از میزبان‌هایی که بارکاری خیلی زیاد و یا خیلی کم دارند (که موجب مصرف انرژی زیادی می‌شوند) به میزبان‌هایی که حاوی سرویس‌های همکار هستند، بهره‌وری منابع موجود مرکز داده بهبود یافته و مصرف انرژی کاهش می‌یابد. مهاجرت زنده سرویس‌ها بر اساس اولویت و به حداقل رساندن تعداد مهاجرت‌ها با هدف کاهش مصرف انرژی، کاهش زمان پاسخ و افزایش کارایی سيستم می‌باشد. با بررسی و مقایسه روش پیشنهادی با روش‌های دیگر، بهبود 37/10 درصدی در کاهش مصرف انرژی در مراکز داده ابری، 18/11 درصدی در کاهش تعداد مهاجرت سرویس‌ها و 46/1 درصدی در افزایش تعداد میزبان‌هایی که خاموش شده‌اند، مشاهده می‌شود.
[1] X. Ye, Y. Yin, and L. Lan, "Energy-efficient many-objective virtual machine placement optimization in a cloud computing environment," IEEE Access, vol. 5, pp. 16006-16020, 2017.
[2] P. Bezerra, G. Martins, R. Gomes, F. Cavalcante and A. Costa, "Evaluating live virtual machine migration overhead on client's application perspective," in Proc. Int. Conf. on Information Networking, ICOIN'17, pp. 503-508, Da Nang, Vietnam, 11-13 Jan. 2017.
[3] A. Beloglazov and R. Buyya, "Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers," Concurrency and Computation: Practice and Experience, vol. 24, no. 13, pp. 1397-1420, Sept. 2012.
[4] P. G. J. Leelipushpam, J. Sharmila, "Live VM migration techniques in cloud environment - a survey," in Proc. IEEE Conf. on Information & Communication Technologies, pp. 408-413, Thuckalay, India, 11-12 Apr. 2013
[5] W. S. Cleveland, "Robust locally weighted regression and smoothing scatterplots," J. of the American Statistical Association, vol. 74, no. 368, pp. 829-836, Dec. 1979.
[6] A. Verma, G. Dasgupta, T. K. Nayak, P. De, and R. Kothari, "Server workload analysis for power minimization using consolidation," in Proc. of the Conf. on USENIX Annual Technical Conf., pp. 1-28, San Diego, CA, USA, 14-19 Jun. 2009.
[7] X. Fu and C. Zhou, "Virtual machine selection and placement for dynamic consolidation in cloud computing environment," Frontiers of Computer Science, vol. 9, no. 2, pp. 322-330, Feb. 2015.
[8] S. Esfandiarpoor, A. Pahlavan, and M. Goudarzi, "Structure-aware online virtual machine consolidation for datacenter energy improvement in cloud computing," Computers & Electrical Engineering, vol. 42, no. 1, pp. 74-89, Feb. 2015.
[9] D. Patel, R. K. Gupta, and R. Pateriya, "Energy-aware prediction-based load balancing approach with VM migration for the cloud environment," Data, Engineering and Applications, vol. 2, no. 1, pp. 59-74, Apr. 2019.
[10] J. Gao and G. Tang, "Virtual machine placement strategy research," in Proc. IEEE Int. Conf. on Cyber-Enabled Distributed Computing and Knowledge Discovery, pp 294-297, Beijing, China, 10-12 Oct. 2013.
[11] J. Wan, F. Pan, and C. Jiang, "Placement strategy of virtual machines based on workload characteristics," in Proc. IEEE 26th Int. Parallel and Distributed Processing Symp. Workshops & PhD Forum, pp. 2140-2145, Shanghai, China, 21-25 May 2012.
[12] A. J. Younge, et al., "Efficient resource management for cloud computing environments," in Proc. IEEE Int. Conf. on Green Computing, pp. 357-364, Chicago, IL, USA, 15-18 Aug. 2010.
[13] M. B. Nagpure, P. Dahiwale, and P. Marbate, "An efficient dynamic resource allocation strategy for VM environment in cloud," in Proc. IEEE Int. Conf. on Pervasive Computing, ICPC'15, 5 pp., Pune, India, 8-10 Jan. 2015.
[14] X. Zheng and Y. Cai, "Dynamic virtual machine placement for cloud computing environments," in Proc. IEEE 43rd Int. Conf. on Parallel Processing Workshops, pp. 121-128, Minneapolis, MN, USA, 9-12 Sept. 2014.
[15] E. Asyabi and M. Sharifi, "A new approach for dynamic virtual machine consolidation in cloud data centers," International J. of Modern Education and Computer Science, vol. 7, no. 4, pp. 61-66, Apr. 2015.
[16] D. Gmach, et al., "Capacity management and demand prediction for next generation data centers," in Proc. IEEE Int. Conf. on Web Services, ICWS'07, pp. 43-50, Salt Lake City, UT, USA, 9-13 Jul. 2007.
[17] T. Wood, et al., "Profiling and modeling resource usage of virtualized applications," in Proc. ACM/IFIP/USENIX Int. Conf. on Distributed Systems Platforms and Open Distributed Processing, pp. 366-387, Berlin, Germany, 2008.
[18] J. Sonnek, et al., "Starling: minimizing communication overhead in virtualized computing platforms using decentralized affinity-aware migration," in Proc. IEEE 39th Int. Conf. on Parallel Processing, pp. 228-237, San Diego, CA, USA, 13-16 Sept. 2010.
[19] L. Hu, et al., "Net-cohort: detecting and managing vm ensembles in virtualized data centers," in Proc. of the 9th Int. Conf. on Autonomic Computing, pp. 3-12, San Jose, CA, USA, 18-21 Sept. 2012.
[20] J. T. Piao and J. Yan, "A network-aware virtual machine placement and migration approach in cloud computing," in Proc. IEEE 9th Int. Conf. on Grid and Cloud Computing, pp. 87-92, Nanjing, China, 1-5 Nov. 2010.
[21] K. Tsakalozos, M. Roussopoulos, and A. Delis, "Hint-based execution of workloads in clouds with nefeli," IEEE Trans. on Parallel and Distributed Systems, vol. 24, no. 7, pp. 1331-1340, Jul. 2012.
[22] Z. Xiao, Q. Chen, and H. Luo, "Automatic scaling of internet applications for cloud computing services," IEEE Trans. on Computers, vol. 63, no. 5, pp. 1111-1123, Nov. 2012.
[23] S. Srikantaiah, A. Kansal, and F. Zhao, "Energy aware consolidation for cloud computing," in Proc. of the Conf. on Power Aware Computing and Systems, vol. 10, pp. 46-56, Berkeley, CA, USA, 7-10 Dec. 2008.
[24] M. Cardosa, M. R. Korupolu, and A. Singh, "Shares and utilities based power consolidation in virtualized server environments," in Proc. IFIP/IEEE Int. Symp. on Integrated Network Management, pp. 327-334, New York, NY, USA, 1-5 Jun. 2009.
[25] M. Noshy, A. Ibrahim, and H. A. Ali, "Optimization of live virtual machine migration in cloud computing: a survey and future directions," J. of Network and Computer Applications, vol. 110, no. 1, pp. 1-10, May 2018.
[26] A. Alarifi, et al., "Energy-efficient hybrid framework for green cloud computing," IEEE Access, vol. 8, pp. 115356-115369, Jun. 2020.
[27] P. Geetha and C. R. R. Robin, "Power conserving resource allocation scheme with improved QoS to promote green cloud computing," J. of Ambient Intelligence and Humanized Computing, vol. 12, no. 1, pp. 7153–7164, Jul. 2020.
[28] N. J. Kansal and I. Chana, "Energy-aware virtual machine migration for cloud computing-a firefly optimization approach," J. of Grid Computing, vol. 14, no. 2, pp. 327-345, Feb. 2016.
[29] J. Meshkati and F. Safi-Esfahani, "Energy-aware resource utilization based on particle swarm optimization and artificial bee colony algorithms in cloud computing," The J. of Supercomputing, vol. 75, no. 5, pp. 2455-2496, May 2019.
[30] G. Singh, M. Malhotra, and A. Sharma, "A comprehensive study on virtual machine migration techniques of cloud computing," Applications of Computing, Automation and Wireless Systems in Electrical Engineering, vol. 553, no. 1, pp. 591-603, Jun. 2019.
[31] K. Park and V. S. Pai, "CoMon: a mostly-scalable monitoring system for PlanetLab," ACM SIGOPS Operating Systems Review, vol. 40, no. 1, pp. 65-74, Jun. 2006.
