استفاده از یک الگوریتم بهینه‌سازی چند هدفه برای تخصیص کارها در سیستمهای مبتنی بر ابر با هدف کاهش انرژی مصرفی
محورهای موضوعی : عمومىسارا طبقچی میلان 1 , نیما جعفری نویمی پور 2
1 - گروه مهندسی کامپیوتر، واحد تبریز، دانشگاه آزاد اسلامی، تبریز، ایران
2 - گروه مهندسی کامپیوتر، واحد تبریز، دانشگاه آزاد اسلامی، تبریز، ایران
کلید واژه: رایانش ابری, انرژی مصرفی, تخصیص کار, الگوریتم بهینه‌سازی چند هدفه,
چکیده مقاله :
افزایش تقاضا منجر به افزایش تنوع، تعداد خدمات و درنتیجه ایجاد مراکز داده رایانش با مقیاس بزرگ‌شده است که علاوه بر هزینههای عملیاتی بالا، مقادیر عظیمی از توان الکتریکی را مصرف میکند. از طرفی سیستمهای خنک‌کننده ناکافی و ناکارآمد، نه‌تنها باعث گرم شدن بیش‌ازحد منابع و کاهش عمر کاری دستگاه‌ها می‌شود، بلکه باعث تولید کربن شده که در وضعیت آب‌وهوا نقش مهمی دارد. ازاین‌رو، در این پژوهش، یک روش مؤثر مدیریت منابع انرژی در مراکز داده ابری مجازی شده ارائه‌شده که علاوه بر کاهش مصرف انرژی و هزینههای عملیاتی، باعث افزایش کیفیت خدمات نیز شده است. این پژوهش، به ارائه یک استراتژی تخصیص منبع در سیستمهای ابری باهدف کاهش انرژی و هزینه اجرا پرداخته و کاربرد آن را در محیط رایانش ابری بررسی میکند. نتایج حاصل از شبیهسازی نشان میدهد که روش پیشنهادی میتواند نسبت به روشهای NPA[1]، [2]DVFS، [3]ST و [4]MM ، میانگین انرژی مصرفی را تا 0.626 کیلووات ساعت کاهش دهد، همچنین نیاز به مهاجرت و موارد نقض SLA نیز به ترتیب به 186 و 30.91% کاهش پیدا نمود.
Nowadays, new technologies have increased the demand for business in the web environment.Increasing demand will increase the variety and number of services. As a result, the creation of large-scale computing data centers has high operating costs and consumes huge amounts of electrical power. On the other hand, inadequate and inadequate cooling systems not only cause excessive heating of resources and shorten the life of the machines. It also produces carbon that plays an important role in the weather. Therefore, they should reduce the total energy consumption of these systems with proper methods. In this research, an efficient energy management approach is provided in virtual cloud data centers, which reduces energy consumption and operational costs, and brings about an increase in the quality of services. It aims to provide a resource allocation strategy for cloud systems with the goal of reducing energy, cost of implementation and examining its use in cloud computing. The results of the simulation show that the proposed method in comaprision to NPA, DVFS, ST and MM methods can reduce the average energy consumption up to 0.626 kWh, also the need to immigration and SLA violation declined up to 186 and 30.91% respectively.
[1] N. Jafari Navimipour and B. Zareie, "A model for assessing the impact of e-learning systems on employees’ satisfaction," Computers in Human Behavior, vol. 53, pp. 475-485, 2015/12/01/ 2015.
[2] M. Chiregi and N. J. Navimipour, "A new method for trust and reputation evaluation in the cloud environments using the recommendations of opinion leaders' entities and removing the effect of troll entities," Computers in Human Behavior, vol. 60, pp. 280-292, 7// 2016.
[3] A. Souri and N. Jafari Navimipour, "Behavioral modeling and formal verification of a resource discovery approach in Grid computing," Expert Systems with Applications, vol. 41, no. 8, pp. 3831-3849, 6/15/ 2014.
[4] Y. Ding, X. Qin, L. Liu, and T. Wang, "Energy efficient scheduling of virtual machines in cloud with deadline constraint," Future Generation Computer Systems, vol. 50, pp. 62-74, 9// 2015.
[5] N. Jafari Navimipour, A. M. Rahmani, A. Habibizad Navin, and M. Hosseinzadeh, "Expert Cloud: A Cloud-based framework to share the knowledge and skills of human resources," Computers in Human Behavior, vol. 46, pp. 57-74, 5// 2015.
[6] P. Xiao, Z.-G. Hu, and Y.-P. Zhang, "An Energy-Aware Heuristic Scheduling for Data-Intensive Workflows in Virtualized Datacenters," Journal of Computer Science and Technology, journal article vol. 28, no. 6, pp. 948-961, 2013.
[7] J. Song, T. Li, Z. Wang, and Z. Zhu, "Study on energy-consumption regularities of cloud computing systems by a novel evaluation model," Computing, journal article vol. 95, no. 4, pp. 269-287, 2013.
[8] D. B. L.D and P. Venkata Krishna, "Honey bee behavior inspired load balancing of tasks in cloud computing environments," Applied Soft Computing, vol. 13, no. 5, pp. 2292-2303, 5// 2013.
[9] B. Alami Milani and N. Jafari Navimipour, "A comprehensive review of the data replication techniques in the cloud environments: Major trends and future directions," Journal of Network and Computer Applications, vol. 64, pp. 229-238, 4// 2016.
[10] F. Lombardi and R. Di Pietro, "Secure virtualization for cloud computing," Journal of Network and Computer Applications, vol. 34, no. 4, pp. 1113-1122, 7// 2011.
[11] N. Jafari Navimipour, A. Habibizad Navin, A. M. Rahmani, and M. Hosseinzadeh, "Behavioral modeling and automated verification of a Cloud-based framework to share the knowledge and skills of human resources," Computers in Industry, vol. 68, pp. 65-77, 4// 2015.
[12] M. Almorsy, J. Grundy, and A. S. Ibrahim, "Adaptable, model-driven security engineering for SaaS cloud-based applications," Automated Software Engineering, journal article vol. 21, no. 2, pp. 187-224, 2014.
[13] L. Wu, S. Kumar Garg, and R. Buyya, "SLA-based admission control for a Software-as-a-Service provider in Cloud computing environments," Journal of Computer and System Sciences, vol. 78, no. 5, pp. 1280-1299, 9// 2012.
[14] S. Ramamoorthy and S. Rajalakshmi, "A Preventive Method for Host Level Security in Cloud Infrastructure," in Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC – 16’), V. Vijayakumar and V. Neelanarayanan, Eds. Cham: Springer International Publishing, 2016, pp. 3-12.
[15] S. Dam, G. Mandal, K. Dasgupta, and P. Dutta, "An Ant Colony Based Load Balancing Strategy in Cloud Computing," in Advanced Computing, Networking and Informatics- Volume 2: Wireless Networks and Security Proceedings of the Second International Conference on Advanced Computing, Networking and Informatics (ICACNI-2014), M. Kumar Kundu, P. D. Mohapatra, A. Konar, and A. Chakraborty, Eds. Cham: Springer International Publishing, 2014, pp. 403-413.
[16] J. Anselmi, D. Ardagna, and M. Passacantando, "Generalized Nash equilibria for SaaS/PaaS Clouds," European Journal of Operational Research, vol. 236, no. 1, pp. 326-339, 7/1/ 2014.
[17] A. Yousafzai et al., "Cloud resource allocation schemes: review, taxonomy, and opportunities," Knowledge and Information Systems, journal article vol. 50, no. 2, pp. 347-381, February 01 2017.
[18] A. Hameed et al., "A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems," Computing, journal article vol. 98, no. 7, pp. 751-774, July 01 2016.
[19] G. Wei, A. V. Vasilakos, Y. Zheng, and N. Xiong, "A game-theoretic method of fair resource allocation for cloud computing services," The Journal of Supercomputing, journal article vol. 54, no. 2, pp. 252-269, November 01 2010.
[20] W. Shu, W. Wang, and Y. Wang, "A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing," EURASIP Journal on Wireless Communications and Networking, journal article vol. 2014, no. 1, p. 64, April 23 2014.
[21] Z. Xiao, W. Song, and Q. Chen, "Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment," IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 6, pp. 1107-1117, 2013.
[22] C. Li, Y. C. Liu, and X. Yan, "Optimization-based resource allocation for software as a service application in cloud computing," Journal of Scheduling, journal article vol. 20, no. 1, pp. 103-113, February 01 2017.
[23] S. Son, G. Jung, and S. C. Jun, "An SLA-based cloud computing that facilitates resource allocation in the distributed data centers of a cloud provider," The Journal of Supercomputing, journal article vol. 64, no. 2, pp. 606-637, May 01 2013.
[24] H. Y. K. Lau and Y. Zhao, "Integrated scheduling of handling equipment at automated container terminals," International Journal of Production Economics, vol. 112, no. 2, pp. 665-682, 2008/04/01/ 2008.
[25] J. H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, 1992.
[26] C. A. C. Coello, "Evolutionary multiobjective optimization," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 1, no. 5, pp. 444-447, 2011.
[27] A. Abraham and L. Jain, "Evolutionary multiobjective optimization," Evolutionary Multiobjective Optimization, pp. 1-6, 2005.
[28] F. Sheikholeslami and N. J. Navimipour, "Service allocation in the cloud environments using multi-objective particle swarm optimization algorithm based on crowding distance," Swarm and Evolutionary Computation, vol. 35, pp. 53-64, 2017/08/01/ 2017.
[29] M. Clerc, "Standard particle swarm optimisation," 2012.
[30] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, "CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms," Software: Practice and Experience, vol. 41, no. 1, pp. 23-50, 2011.
[31] A. Beloglazov, J. Abawajy, and R. Buyya, "Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing," Future Generation Computer Systems, vol. 28, no. 5, pp. 755-768, 2012/05/01/ 2012.
