الگوریتم زمانبندی کار مبتنی بر امنیت با استفاده از تکنیک بهینه‌سازی ازدحام ذرات و یادگیری انطباقی چندگانه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 بخش علوم کامپیوتر، دانشگاه شهید باهنر کرمان، کرمان، ایران

2 گروه علوم کامپیوتر، دانشکده ریاضی و رایانه، دانشگاه شهیدباهنر کرمان، کرمان، ایران

3 گروه علوم کامپیوتر، دانشکده ریاضی و رایانه، دانشکاه شهید باهنر کرمان، کرمان، ایران

چکیده

امروزه بسیاری از مسائل علمی پیچیده نیاز به قدرت محاسباتی و فضای ذخیره⁠سازی بالایی دارند. رایانش ابری مدلی است برای دسترسی آسان و بنا به سفارشِ منابع رایانشی مانند فضای ذخیره‌سازی با کمترین نیاز به دخالت فراهم‌کننده خدمات. ابرها به دلیل مزایای بسیار مورد استقبال قرار گرفتند ولی با توجه به برون‌سپاری، مسائل مربوط به امنیت و حفظ حریم خصوصی به عنوان مهم‌ترین مشکلات این حوزه مطرح می‌شوند. از طرف دیگر، زمانبندی کارها یک مسئله اساسی در سیستم‌های توزیع شده‌ای چون رایانش ابری است. زیرا در یک‌ زمان واحد، کارهای متعددی برای اجرا شدن وجود دارد که به منابع متفاوتی احتیاج دارند درحالی‌که منابع محدود هستند. از این‌رو باید به طور هوشمندانه کارها زمانبندی شوند تا عملکرد سیستم و سوددهی فراهم‌کننده حداکثر گردد. برای حل این مشکل، روش‌های مختلف مانند الگوریتم⁠های مبتنی بر گرادیان برای مسائل مستمر و تک مدلی معمول هستند. اما اگر برای زمانبندی در رایانش ابری استفاده شوند، به دلیل فضای جستجوی بزرگ و طبیعت پیچیده مسائل، این الگوریتم⁠ها ممکن است راه⁠حل رضایت⁠بخشی ارائه ندهند. روش⁠های فرا⁠اکتشافی کارآمد می⁠توانند با این مشکل مقابله کرده و راهحل نزدیک به بهینه در کوتاه⁠ترین دوره زمانی را پیدا کنند. در نتیجه در این مقاله، الگوریتم زمانبندی برای بهبود امنیت با استفاده الگوریتم بهینه‌سازی ازدحام ذرات بهبودیافته ارائه شده است. الگوریتم بهبودیافته با استفاده از یادگیری انطباقی منجر به تنوع در جمعیت می‌شود و لذا تعادلی بین عملیات اکتشاف و بهره‌برداری به دست می‌آید. الگوریتم زمانبندی پیشنهادی همزمان پنج پارامتر (زمان بازگشت، بار، مصرف انرژی، هزینه و امنیت) را در حین توزیع کارها در نظر می‌گیرد تا در نهایت منجر به توزیع بار و کاهش مصرف انرژی می‌گردد. الگوریتم⁠ پیشنهادی با استفاده از شبیه‌ساز کلودسیم پیاده⁠سازی و با روش‌های مربوطه (CJS, OTSS, GTSA, JSSS) مقایسه می⁠شود. نتایج حاصل از شبیه‌سازی نشان می‌دهد که الگوریتم پیشنهادی با در نظر گرفتن ویژگی‌های کارها و منابع، کارایی و اثربخشی قابل‌توجهی در محیط رایانش ابری خصوصاً در بار کاری بالا دارد.

کلیدواژه‌ها


عنوان مقاله [English]

Security-aware Task Scheduling Algorithm based on Multi Adaptive Learning and PSO Technique

نویسندگان [English]

  • N. Mansouri 1
  • B. Mohammad Hasani Zade 2
  • R. Ghafari 3
1 Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran
2 Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran
3 Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran
چکیده [English]

Today, many scientific problems require high computational power and storage space. Cloud computing is a model for easy access to different resources such as storage space with minimal need for service provider interaction. The cloud environment has been used for many benefits, but security and privacy issues are important challenges due to outsourcing. On the other hand, task scheduling is a fundamental issue in distributed systems such as cloud computing. Because there are several tasks to be performed that require different resources while resources are limited. Therefore, cloud tasks must be intelligently scheduled so that system performance and provider profitability are maximized. To solve this challenge, various techniques such as gradient-based algorithms for continuous and single-model problems are common. In cloud computing, due to the large search space and complex nature, these algorithms may not provide a suitable solution. Efficient meta-heuristic techniques can deal with these problems and find near-optimal solutions in a reasonable time. In this paper, a security-based scheduling algorithm using an improved Particle Swarm Optimization algorithm is presented. The improved algorithm uses multi adaptive learning to provide diversity in a population. Therefore, a good balance between exploration and exploitation. The proposed task scheduling algorithm simultaneously considers five parameters (i.e., round trip time, load, energy consumption, cost, and security) to provide load balancing and reduce energy consumption. The proposed algorithm is implemented using the CloudSim simulator and compared with the relevant strategies (i.e., CJS, OTSS, GTSA, and JSSS). The simulation results show that the proposed algorithm, considering the characteristics of tasks and resources, has significant efficiency and effectiveness in the cloud environment, especially at high workloads.

کلیدواژه‌ها [English]

  • Cloud computing
  • Meta- heuristic
  • Task scheduling
  • Security
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دوره 9، شماره 2 - شماره پیاپی 34
شماره پیاپی 34، فصلنامه تابستان
تیر 1400
صفحه 159-178
  • تاریخ دریافت: 12 مهر 1399
  • تاریخ بازنگری: 03 آذر 1399
  • تاریخ پذیرش: 22 دی 1399
  • تاریخ انتشار: 01 تیر 1400