مروری بر کاربرد الگوریتم‌های فراابتکاری در توازن بار در رایانش ابری
محورهای موضوعی : فناوری اطلاعات و ارتباطات
مهدی مرسلی
1
,
ابوالفضل طرقی حقیقت
2
,
ساسان حسینعلی زاده
3
1 - دانشکده مهندسی برق،کامپیوتر و فناوری اطلاعات، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران
2 - دانشکده مهندسی برق، کامپیوتر و فناوری اطلاعات، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران
3 - پژوهشکده فناوری اطلاعات، پژوهشگاه ارتباطات و فناوری اطلاعات، تهران ، ایران.
کلید واژه: رایانش ابری, توازن بار, روش‌های فراابتکاری, بیش‌باری , کم‌باری,
چکیده مقاله :
با گسترش استفاده از رایانش ابری نیاز به بهبود کارایی و کاهش تاخیر در ابر افزایش می‌یابد. یکی از مسائل محیط‌های توزیع شده و مخصوصا ابر، عدم توازن بار و در نتیجه کاهش سرعت و کارایی و افزایش تاخیر در زمان ذخیره و بازیابی اطلاعات می‌باشد. روش‌های مختلفی برای متوازن سازی بار در محیط ابر ارائه شده‌اند که هر کدام از منظری به موضوع پرداخته‌اند و مزایا و معایب خود را دارند. ما در این کار نخست معیارهایی برای سنجش توازن بار در ابر ارائه کرده‌ایم و سپس به بررسی کاربرد روش‌های فراابتکاری در متوازن سازی بار در محیط ابر پرداخته‌ایم. پس از معرفی روش‌های توازن بار فراابتکاری مختلف، آنها را براساس معیارهای مذکور باهم مقایسه کرده و به مزایا و معایب هر کدام پرداخته‌ایم. الگوریتم‌های کلونی مورچه، کلونی مورچه مصنوعی، کلونی زنبور، کلونی زنبور مصنوعی، جست‌وجوی غذای زنبور عسل، ازدحام ذرات، ازدحام گربه‌ها، تبرید شبیه‌سازی شده، الگوریتم ژنتیک، جست‌وجوی ممنوعه، الگوریتم دسته ماهی‌ها و الگوریتم‌های ترکیبی و ... در این کار بررسی شده‌اند.
By widespread use of cloud computing, the need to improve performance and reduce latency in the cloud increases. One of the problems of distributed environments, especially clouds, is unbalanced load which results in reducing speed and efficiency and increasing delay in data storage and retrieval time. Various methods for load balancing in the cloud environment have been proposed, each of which has addressed the issue from its own perspective and has its advantages and disadvantages. In this research, we first provide some criteria for measuring load balance in the cloud and then examine the use of Metaheuristic methods in load balancing in the cloud environment. After introducing Metaheuristic load balancing methods, we have compared them based on the aforementioned criteria and discussed the advantages and disadvantages of each. Ant Colony Algorithms, Artificial Ant Colony, Bee Colony, Artificial Bee Colony, Bee Foraging Algorithm, Particle Swarm, Cat Swarm, Simulated Annealing, Genetic Algorithm, Tabu Search, Fish Swarm and Hybrid Algorithms and etc. examined in this research.
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