فصلنامه تخصصی فناوری اطلاعات و ارتباطات

نوع مقاله : مقاله مروری

نویسندگان

1 گروه مهندسی کامپیوتر،دانشگاه آزاد اسلامی واحد مهاباد،ایران

2 گروه مهندسی کامپیوتر، دانشگاه ازاد اسلامی واحد مهاباد، دانشکده فنی و مهندسی

3 شرکت توزیع نیروی برق تبریز، تبریز، ایران.

4 گروه مهندسی عمران، دانشکده مهندسی عمران، دانشگاه آزاد اسلامی، واحد مهاباد، مهاباد، ایران

چکیده

افزایش لحظه‌ای کاربران و نیاز آن‌ها به خدمات اینترنتی باعث شد که در اندک زمانی شرکت‌هایی که این گونه از خدمات را ارائه می‌دادند با مشکلاتی نظیر عدم توانایی در پاسخگویی سریع به کاربران و افزایش هزینه‌هایشان روبرو شوند .از این‌رو بسیاری از این شرکت‌ها با سرمایه‌گذاری‌های فراوان در زمینه‌های تحقیقاتی به فکر شیوه‌های موثر و مقرون به صرفه، برای سرویس‌دهی به حجم بالایی از کاربران افتادند و به این ترتیب فناوری نوین و کارآمدی به نام رایانش ابری به‌ وجود آمد .با افزایش کاربران استفاده کننده از خدمات رایانش ابری و لذا افزایش میزان درخواست‌ها، جهت دستیابی به مزایای اشاره شده، نیاز به استقرار مکانیزم‌های مناسبی جهت توازن بار، زمانبندی کار و مجازی‌سازی در رایانش ابری می‌باشد. این بار می‌تواند شامل ظرفیت حافظه، بار شبکه و یا تاخیر باشد. توازن بار فرایند توزیع بار در میان گره‌های مختلف یک سیستم توزیع شده جهت بهبود بهره‌برداری از منابع و زمان پاسخ می‌باشد در حالی‌ که از وضعیتی که درآن برخی از گره‌ها دارای بار سنگین باشند در حالی‌که گره‌های دیگر بیکار باشند و یا کار خیلی کمی برای انجام دادن داشته باشند اجتناب می‌کند. با توجه به ضرورت و اهمیت توازن بار در رایانش ابری، در این مقاله به مروری جامع از الگوریتم‌های استاتیک، پویا و الهام‌گرفته از طبیعت جهت متعادل‌سازی بار در یک فضای ابری برای رسیدگی به زمان پاسخ‌دهی مراکز داده و عملکرد کلی آن‌ها می‌پردازیم. با تحلیل الگوریتم‌های تعادل بار نشان می‌دهیم که الگوریتم کلونی مورچه ، الگوریتم ژنتیک و الگوریتم بهینه‌سازی ازدحام ذرات با تخصیص بهینه وظایف می‌توانند نقش مؤثرتری در متعادل-سازی بار در فضای ابری را داشته باشند. همچنین نتایج گویا این است که نرم‌افزار کلودسیم بیشترین کاربرد را در شبیه‌سازی الگوریتم‌های متعادل کردن بار در فضای ابری داشته است.

کلیدواژه‌ها

عنوان مقاله [English]

A review of load balancing algorithms in cloud computing environment

نویسندگان [English]

  • wahab aminiazar 1
  • rasoul farahi 2
  • fatmeh dashti 3
  • kamal rahami 4

1 Department of Computer Engineering, Islamic Azad University, Mahabad Branch, Iran

2 Department of Computer Engineering, Islamic Azad University, Mahabad Branch, Technical and Engineering Faculty

3 Tabriz Electricity Distribution Company, Tabriz, Iran

4 Department of Civil Engineering, Faculty of Civil Engineering, Islamic Azad University, Mahabad Branch, Mahabad, Iran

چکیده [English]

The instantaneous increase in users and their need for internet services caused that, in a short time, the companies that provided this type of service faced problems such as the inability to respond quickly to users and the increase in their costs. Therefore, many of these companies, with a lot of investments in research fields, thought of effective and cost-effective ways to serve a high volume of users, and in this way, new technology and an efficiency system called cloud computing were created. With the increase in users using cloud computing services and therefore the increase in the number of requests, in order to achieve the mentioned benefits, there is a need to establish appropriate mechanisms for load balancing, work scheduling and virtualization. Sazi is in cloud computing. This load can include memory capacity, network load or delay. Load balancing is the process of distributing load among different nodes of a distributed system in order to improve the utilization of resources and response time, while it is a situation in which some nodes have a heavy load while the node Others avoid being unemployed or having very little work to do. Considering the necessity and importance of load balancing in cloud computing, in this article, a comprehensive review of static, dynamic and nature-inspired algorithms for load balancing in a cloud space to handle the response time of data centers and their overall performance is given. We pay by analyzing the load balancing algorithms. We show that the ant colony algorithm, the genetic algorithm and the particle swarm optimization algorithm with optimal allocation of tasks can play a more effective role in balancing the load in the cloud space. Also, the results show that CloudSim software has been used the most in simulating load balancing algorithms in the cloud space.

کلیدواژه‌ها [English]

  • Cloud Computing
  • Virtualization
  • Task Scheduling
  • Load Balancing
  • Resource Optimization
Rashid A, Chaturvedi A. Cloud computing characteristics and services: a brief review. International Journal of Computer Sciences and Engineering. 2019 Feb;7(2):421-6.
Bokhari MU, Makki Q, Tamandani YK. A survey on cloud computing. InBig Data Analytics: Proceedings of CSI 2015 2018 (pp. 149-164). Springer Singapore.
Kumar M, Sharma SC, Goel A, Singh SP. A comprehensive survey for scheduling techniques in cloud computing. Journal of Network and Computer Applications. 2019 Oct 1;143:1-33.
Arockiam L, Monikandan S, Parthasarathy G. Cloud computing: a survey. Journal of Computer and Communication Technology: Vol. 2017 Jan;8(1):4.
Lin CC, Liu P, Wu JJ. Energy-efficient virtual machine provision algorithms for cloud systems. In2011 Fourth IEEE International Conference on Utility and Cloud Computing 2011 Dec 5 (pp. 81-88). IEEE.
Ghomi EJ, Rahmani AM, Qader NN. Load-balancing algorithms in cloud computing: A survey. Journal of Network and Computer Applications. 2017 Jun 15;88:50-71.
Thakur A, Goraya MS. A taxonomic survey on load balancing in cloud. Journal of Network and Computer Applications. 2017 Nov 15;98:43-57.
Zhang J, Yu FR, Wang S, Huang T, Liu Z, Liu Y. Load balancing in data center networks: A survey. IEEE Communications Surveys & Tutorials. 2018 Mar 15;20(3):2324-52.
Shafiq DA, Jhanjhi NZ, Abdullah A. Load balancing techniques in cloud computing environment: A review. Journal of King Saud University-Computer and Information Sciences. 2022 Jul 1;34(7):3910-33.
Deepa T, Cheelu D. A comparative study of static and dynamic load balancing algorithms in cloud computing. In2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) 2017 Aug 1 (pp. 3375-3378). IEEE.
Aslam S, Shah MA. Load balancing algorithms in cloud computing: A survey of modern techniques. In2015 National software engineering conference (NSEC) 2015 Dec 17 (pp. 30-35). IEEE.
Shoja H, Nahid H, Azizi R. A comparative survey on load balancing algorithms in cloud computing. InFifth International Conference on Computing, Communications and Networking Technologies (ICCCNT) 2014 Jul 11 (pp. 1-5). IEEE.
Sajjan RS, Yashwantrao BR. Load balancing and its algorithms in cloud computing: a survey. International Journal of Computer Sciences and Engineering. 2017 Jan;5(1):95-100.
Malhotra M. A review, different improvised throttled load balancing algorithms in cloud computing environment”. Int. J. Eng. Technol. Manag. Appl. Sci.. 2017;5(7):410-6.
Megharaj G, Mohan KG. A survey on load balancing techniques in cloud computing. IOSR Journal of Computer Engineering (IOSR-JCE). 2016 Apr;18(2):55-61.
Sachdeva R, Kakkar S. A novel approach in cloud computing for load balancing using composite algorithms. Int J. 2017 Feb;7(2):198.
Rathore J, Keswani B, Rathore VS. An efficient load balancing algorithm for cloud environment. J Invent Comput Sci Commun Technol. 2018;4(1):37-41.
Khanchi M, Tyagi S. An efficient algorithm for load balancing in cloud computing. International Journal of Engineering Sciences & Research Technology. 2016 Jun;5(6):468-75.
Babu KR, Joy AA. Samuel (2017) Load balancing of tasks using hybrid technique with analytical method of esce & throttled algorithm. Int J Nov Res Dev.;2(6):61-6.
Gopinath PG, Vasudevan SK. An in-depth analysis and study of Load balancing techniques in the cloud computing environment. Procedia Computer Science. 2015 Jan 1;50:427-32.
George Amalarethinam DI, Kavitha S. Rescheduling enhanced Min-Min (REMM) algorithm for meta-task scheduling in cloud computing. InInternational Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018 2019 (pp. 895-902). Springer International Publishing.
Wang, S. C., Yan, K. Q., Liao, W. P., & Wang, S. S. (2010, July). Towards a load balancing in a three-level cloud computing network. In Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on (Vol. 1, pp. 108-113). IEEE.
Arshad Ali, S., Member, S., Alam, M., 2019. Resource Aware Min-Min (RAMM) Algorithm for Resource Allocation in Cloud Computing Environment, 3, pp. 1863–1870 doi: 10.35940/ijrte.C5197.098319.
Patel, G., Mehta, R., Bhoi, U., 2015. Enhanced Load Balanced Min-min Algorithm for Static Meta Task Scheduling in Cloud Computing. Procedia Comput. Sci. 57, 545–553.
Shanthan BH, Arockiam L. Resource based load balanced min min algorithm (RBLMM) for static meta task scheduling in cloud. InInternational conference on advances in computer science and technology. Int J Eng Technol Spec 2018 (No. 1-8).
Villanueva, J.C., 2015. Comparing Load Balancing Algorithms,” 28-Jun-2015. [Online]. Available: https://www.jscape.com/blog/load-balancing-algorithms. [Accessed: 18-May-2020].
Tailong V, Dimri V. Load balancing in cloud computing using modified optimize response time. International Journal of Advanced Research in Computer Science and Software Engineering. 2016 May;6(5).
Kaurav NS, Yadav P. A genetic algorithm-based load balancing approach for resource optimization for cloud computing environment. Int J Inf Comput Sci. 2019;6(3):175-84.
Richhariya V, Dubey R, Siddiqui R. Hybrid technique for load balancing in cloud computing using modified round robin algorithms. J Comput Math Sci. 2015 Dec;6(12):688-95.
Kiritbhai PB, Shah NY. Optimizing load balancing technique for efficient load balancing. Int J Innov Res Technol. 2017 Nov;4(6):39-44.
Ehsanimoghadam P, Effatparvar M. Load balancing based on bee colony algorithm with partitioning of public clouds. International Journal of Advanced Computer Science and Applications. 2018;9(4).
Gandhi, R., Genetic Algorithms - Data Driven Investor - Medium,” 12-May-2018. [Online]. Available: https://medium.com/datadriveninvestor/geneticalgorithms- 9f920939f7cc. [Accessed: 12-Jun-2020].
Wang T, Liu Z, Chen Y, Xu Y, Dai X. Load balancing task scheduling based on genetic algorithm in cloud computing. In2014 IEEE 12th international conference on dependable, autonomic and secure computing 2014 Aug 24 (pp. 146-152). IEEE.
Pilavare MS, Desai A. A novel approach towards improving performance of load balancing using genetic algorithm in cloud computing. In2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) 2015 Mar 19 (pp. 1-4). IEEE.
Khajemohammadi H, Fanian A, Gulliver TA. Fast workflow scheduling for grid computing based on a multi-objective genetic algorithm. In2013 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) 2013 Aug 27 (pp. 96-101). IEEE.
Parmesivan YA, Hasan S, Muhammed A. Performance evaluation of load balancing algorithm for virtual machine in data centre in cloud computing. Int. J. Eng. Technol. 2018;7(4.31):386-90.
Liu Z, Wang X. A PSO-based algorithm for load balancing in virtual machines of cloud computing environment. InAdvances in Swarm Intelligence: Third International Conference, ICSI 2012, Shenzhen, China, June 17-20, 2012 Proceedings, Part I 3 2012 (pp. 142-147). Springer Berlin Heidelberg.
Zhan S, Huo H. Improved PSO-based task scheduling algorithm in cloud computing. Journal of Information & Computational Science. 2012 Nov 1;9(13):3821-9.
Pathan AF, Mallikarjuna SB. A load balancing model based on cloud partitioning for the public cloud. International journal of information and computation technology. 2014;4(16).
Bachir B, Ali A, Abdellah M. Multiobjective optimization of an operational amplifier by the ant colony optimisation algorithm. Electrical and Electronic Engineering. 2012;2(4):230-5.
Mishra R, Jaiswal A. Ant colony optimization: A solution of load balancing in cloud. International Journal of Web & Semantic Technology. 2012 Apr 1;3(2):33-50.
Chen WN, Zhang J. An ant colony optimization approach to a grid workflow scheduling problem with various QoS requirements. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 2008 Oct 31;39(1):29-43.
Gao Y, Guan H, Qi Z, Hou Y, Liu L. A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. Journal of computer and system sciences. 2013 Dec 1;79(8):1230-42.
https://www.cloudsimtutorials.online/cloudsim-simulation-toolkit-an-introduction.