طبقه‌بندی و بررسی جامع روش‌های بهینه‌سازی تعادل بار شبکه‌های نرم‌افزار محور

نوع مقاله : مقاله مروری

نویسندگان

دانشکده مهندسی کامپیوتر، دانشگاه اصفهان، اصفهان، ایران

چکیده

ترافیک شبکه‌ها روزبه‌روز در حال افزایش است؛ به همین دلیل برای مدیریت شبکه‌ها از فناوری شبکه‌های نرم‌افزار محور استفاده می‌شود؛ زیرا این فناوری یک نمای کلی از شبکه ارائه داده و مدیریت پیشرفته را امکان‌پذیر می‌کند. در ضمن برای بهبود عملکرد در شبکه‌های نرم‌افزار محور به تعادل بار نیز نیاز است. برای ایجاد تعادل بار در شبکه‌های نرم‌افزار محور رویکردهای بسیاری پیشنهاد شده است. این رویکردها می‌توانند طبقه‌بندی باشند؛ با این حال طبقه‌بندی‌های ارائه شده تاکنون دقیق نیستند. در این مقاله، طبقه‌بندی دقیقی برای تعادل بار شبکه‌های نرم‌افزار محور ارائه شده و در ادامه رویکردهایی که از الگوریتم‌های بهینه‌سازی مبتنی بر هوش مصنوعی برای حل مساله تعادل بار استفاده کرده‌اند، مورد تجزیه و تحلیل قرار گرفته‌اند. در نهایت روش‌های پیش‌بینی تعادل بار در شبکه‌های نرم‌افزار محور شرح داده شده و چگونگی کمک آنها به کاهش مصرف انرژی مورد بررسی قرار گرفته است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Taxonomy and comprehensive review of optimization techniques of load balancing software-defined networks

نویسندگان [English]

  • Somaye Imanpour
  • Ahmadreza Montazerolghaem
Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran
چکیده [English]

The network traffic is increasing daily; consequently, software-defined network technology is employed to manage the network, as this technology provides an overview of the network and enables advanced management. In software-defined networks, load balancing is also necessary to improve performance. Many approaches have been proposed for load balancing in software-defined networks. These approaches can be the taxonomy; however, the taxonomies presented so far are not exact. In this article, a detailed taxonomy for load balancing of software-defined networks is provided. Then, the approaches that use optimization algorithms based on artificial intelligence to address the load balancing challenge in software-defined networks are discussed. Finally, the methods for predicting the load balance in software-defined networks and how this contributes to reducing energy consumption are presented.

کلیدواژه‌ها [English]

  • Load balancing
  • Software defined network
  • Optimization
  • Prediction
  • Network function virtualization
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