ارائه یک روش تأمین منبع فعالانه مبتنی‌بر پیشگویی برای زمان‌بندی گردش کار چندهدفه در رایانش ابری

نوع مقاله : مقاله پژوهشی (کاربردی)

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه مهندسی کامپیوتر، واحد دولت آباد، دانشگاه آزاد اسلامی، اصفهان، ایران.

2 دانشیار، گروه مهندسی کامپیوتر، واحد دولت آباد، دانشگاه آزاد اسلامی، اصفهان، ایران.

3 دانشیار، گروه مهندسی کامپیوتر، واحد مبارکه، دانشگاه آزاد اسلامی، اصفهان، ایران.

چکیده

به‌منظور مدیریت فعالانه بار کاری در طول اجرای برنامه کاربردی روی سیستم ابر، بار کاری باید از طریق یک روش مناسب پیشگویی شود و از طریق یک کنترل‌کننده سیستم مقیاس­پذیر تعداد منابع موردنیاز مدیریت شوند. از سوی دیگر زمان­بندی گردش­های کار به‌منظور بهره­وری منابع و برآورده کردن نیازمندی­های کیفی کاربران مختلف به نگاشت مناسب منابع ابری به وظایف گردش کار نیاز دارد. زمان­بندی گردش کار یک مسئله NP کامل است و الگوریتم­های تکاملی چندهدفه اغلب برای حل این مسائل مفید هستند. بیشتر کارهای گذشته تنها بر تأمین منبع یا زمان­بندی اجرای گردش کارها تمرکز کرده­اند و تاکنون به ترکیب پویای آن‌ها توجه نشده است. براساس کمبودی که در این زمینه وجود دارد، در این مقاله یک استراتژی تأمین منبع فعالانه با استفاده از شبکه عصبی رقمی­ساز بردار یادگیر (LVQ) برای پیشگویی بارهای کاری آینده ارائه می­شود و یک کنترل‌کننده سیستم فازی برای محاسبه تعداد مناسب منابع موردنیاز پیشنهاد می­شود که این منابع باید به سیستم اختصاص داده شوند. همچنین یک الگوریتم زمان­بندی مبتنی بر روش برنامه­ریزی خطی چندهدفه برای اجرای بارهای کاری روی منابع موجود پیشنهاد می­شود. در نهایت ارزیابی مقاله با سه نوع بارکاری واقعی انجام می­شود. نتایج آزمایش‌ها نشان می­دهند که روش پیشنهادی متوسط هزینه اجرا و زمان پاسخ را در مقایسه با کارهای انجام شده دیگر، کاهش و میزان بهره­وری منابع را افزایش می­دهد. 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

A Prediction based Proactive Resource Provisioning Strategy for Multi-Objective Workflow Scheduling in Cloud Computing

نویسندگان [English]

  • Mohammadreza Mahmoudian 1
  • Reihaneh Khorsand 2
  • Mohammadreza Ramezanpour 3
1 M.Sc. Student, Department of Computer Engineering, Dolatabad Branch, Islamic Azad University, Isfahan, Iran.
2 Associate Professor, Department of Computer Engineering, Dolatabad Branch, Islamic Azad University, Isfahan, Iran.
3 Associate Professor, Department of Computer Engineering, Mobarakeh Branch, Islamic Azad University, Isfahan, Iran.
چکیده [English]

In order to manage the workload proactively on the Cloud system during application execution, workload should be predicted through a proper approach and count of resources handled through an auto-scaling system controller. On the other hand, the workflow scheduling requires proper mapping of cloud resources to workflow tasks to efficiently utilize resources and meet different user’s quality of service requirements. Workflow scheduling is NP-complete problem and multi-objective evolutionary algorithms have shown their merit for solving such problem. Most of the works in the literature focused either on dynamic resource provisioning or scheduling algorithms for executing workloads. Based on this deficiency, in this paper, a prediction‑based proactive resource provisioning strategy based on learning vector quantization (LVQ) artificial neural network was proposed to predict the workloads in future and a fuzzy system controller was proposed to compute the proper number of resources to be allocated to the Cloud system. In addition, the multi-objective linear programming scheduling algorithm was proposed to execute workloads effectively on available resources. An evaluation with three kinds of real scientific workflows was performed. The experimental results showed that the proposed approach efficiently reduced execution average cost, and response time along with higher resource utilization in comparison with its counterparts.

کلیدواژه‌ها [English]

  • Cloud computing Multi
  • objective scheduling Scalability Quality of service LVQ

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فصلنامه علمی کارافن
دوره 19، شماره 3 - شماره پیاپی 59
فنی و مهندسی
آذر 1401
صفحه 529-551

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  • تاریخ دریافت: 02 خرداد 1401
  • تاریخ بازنگری: 13 آبان 1401
  • تاریخ پذیرش: 28 آبان 1401

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