ارائه یک الگوریتم توازن بار نامتمرکز در محیط‌های ناهمگن
محورهای موضوعی : مهندسی برق و کامپیوتر
سمیرا حورعلی
1
(دانشگاه غیر انتفاعی- غیر دولتی شاهرود)
شهرام جمالی
2
(دانشگاه محقق اردبیلی)
فاطمه حورعلی
3
(دانشگاه صنعتی اسفراین)
کلید واژه: تکنیک فازی روش پرومته ماشین مجازی محاسبات ابری موازنه بار,
چکیده مقاله :
یکی از راهکارهای اساسی برای ارتقای کارایی در محیط ابر، موازنه بار می‌باشد. انتخاب VM مناسب برای انجام هر کار، تابع پارامترهای مختلفی مانند میزان منابع مورد نیاز کار نظیر CPU، حافظه، حجم منابع در اختیارVM ها، هزینه و سررسیدVM ها می‌باشد. در این مقاله با در نظر گرفتن تک‌تک این معیارها و اهداف طراحی مانند توازن بار، کاهش نرخ ایجاد VM جدید و مهاجرت VM ها، مسأله را در قالب پارامترهای مؤثر در کارایی مدل کرده و سپس مدل فوق را با استفاده از روش پرومته که یکی از پرکاربردترین روش‌های تصمیم‌گیری چندشاخصه است، حل می‌کنیم. در این روش انتخاب بهترین VM بر اساس ارزش اختصاص‌یافته به هر یک از معیارها صورت می‌گیرد که بر اساس منطق فازی تعیین می‌شود. جهت بررسی کارایی این روش، شبیه‌سازی‌های گسترده‌ای در محیط CloudSim صورت گرفته که نشان می‌دهد روش پیشنهادی نسبت به روش‌های موجود مانند FIFO، DLB و WRR از نقطه نظرات زمان پاسخ، نرخ موفقیت کارها، انحراف بار و نرخ مهاجرت VMها عملکرد بسیار بهتری دارد.
One of the key strategies to improve the efficiency is load balancing. Choosing the appropriate VM to do any task, is function of various parameters such as the amount of required resources like CPU, memory, the size of VM resource, cost and maturity of VMs. In this paper, by considering each of these criteria and design objectives such as load balancing, reducing the rate of create new VM, and VM migration, we modeling the problem in terms of effective parameters in performance. Then, we solving this model by using the PROMETHEE method, which is one of the most widely used method for MADM problems. In this method, selecting the best VM occurs based on the value assigned to each of criteria which is calculated based on fuzzy logic. To evaluate the performance of this approach, the necessary simulations have been carried out on CloudSim simulator and shown that the proposed method has better performance compared to FIFO, DLB and WRR methods on average in terms of response time, rate of success tasks, load variation and rate of VM migration.
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