زمانبندی کارها در محاسبات ابری با استفاده از یادگیری عمیق تقویتی
الموضوعات : سامانه‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند
بهنام سالاری
1
,
مهدی اکبری
2
,
فرامرز صافی
3
,
بهرنگ برکتین
4
1 - دانشجوی دکتری، گروه مهندسی کامپیوتر، واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد ، ایران
2 - استادیار، گروه مهندسی کامپیوتر، واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد ، ایران
3 - دانشیار، گروه مهندسی کامپیوتر، واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد ، ایران
4 - دانشیار، گروه مهندسی کامپیوتر، واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد ، ایران
الکلمات المفتاحية: زمانبندی کارها, محاسبات ابری, یادگیری عمیق تقویتی, فرآیند تصمیم گیری مارکوف ,
ملخص المقالة :
زمانبندی وظایف و مصرف انرژی از جمله مسائل مهم در محاسبات ابری هستند. عدم استفاده از یک رویکرد زمانبندی مناسب در محاسبات ابری ممکن است باعث مصرف انرژی بالا و بهره‌وری پایین منابع شود. به علت پویایی و محدودیت منابع ابری برای اجرای درخواست‌های متنوع و متغیر با زمان کاربران، یک مکانیزم زمانبندی مؤثر مورد نیاز است که خود را با شرایط پویای سیستم وفق دهد. یادگیری عمیق، یک روش کاربردی برای مقابله با مشکلات مدیریت منابع رایانش ابری به عنوان یک ایده نوآورانه در سال‌های اخیر مورد توجه قرار گرفته است. از جمله الگوریتمهای یادگیری عمیق استفاده شده در این زمینه می‌توان از الگوریتمهای DQL ، ERDQL و DRL-LSTM نام برد که در دیتاستهای بزرگ به نتایج قابل قبولی رسیدهاند. ولی این الگوریتم‌ها دارای مشکلاتی از جمله تخمین غیر واقعی پاداش، آموزش طولانی مدت، نمونه برداری تصادفی از حافظه و عدم استفاده مناسب از فضای حالت می‌باشند که بر نتایج حاصل از استفاده آنها در مسائل بهینه سازی مؤثر است. در این تحقیق یک الگوریتم یادگیری عیمق تقویتی بهبود یافته برای زمانبندی کارها در محاسبات ابری ارائه شده است تا مشکلات تخمین غیر واقعی پاداش، آموزش طولانی مدت و نمونه برداری تصادفی از حافظه را بهبود دهد. همچنین ساختار فرآیند تصمیمگیری مارکوف در قسمت مدلسازی به گونهای تعریف میشود که مشکلات عدم استفاده مناسب از فضای حالت را تعدیل نماید. لذا با استفاده از الگوریتم پیشنهادی نتایج مورد انتظار این است که منجر به بهبود زمان پاسخ، زمان انتظار، برون‌ده، زمان‌کل کارها و تعداد نقض SLA گردد. در این مقاله، الگوریتم توسعه‌یافته با مجموعه داده (GOCJ (Google Cloud Jobs مورد ارزیابی قرار می‌گیرد.
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