جایگذاری کنترلکنندهها در شبکه‌های نرم‌افزار محور با استفاده از یک الگوریتم فراابتکاری گسسته بر پایه‌ی ژنتیک
محورهای موضوعی : مجله فناوری اطلاعات در طراحی مهندسی
مهناز خجند
1
,
کامبیز مجیدزاده
2
,
محمد مصدری
3
,
یوسف فرهنگ
4
1 - دانشگاه آزاد اسلامی واحد ارومیه
2 - دانشکده فنی مهندسی، دانشگاه آزاد اسلامی واحد ارومیه، ارومیه، ایران
3 - Computer Engineering Department, Islamic Azad University
4 - دانشکده فنی مهندسی، دانشگاه آزاد اسلامی واحد خوی، خوی، ایران
کلید واژه: شبکه های نرم افزار محور, مسئله قرارگیری کنترلکننده, الگوریتم های فراابتکاری, الگوریتم بهینه‌سازی مبتنی بر تضاد نخبگان , الگوریتم ژنتیک.,
چکیده مقاله :
شبکه‌های نرم‌افزارمحور (SDN) شامل جداسازی صفحه کنترل از صفحه داده است. در SDN کنترل شبکه توسط موجودیتی به نام کنترلکننده که در صفحه کنترل قرار دارد تعیین می‌شود. تعیین تعداد و مکان بهینه کنترلکنندهها در صفحه کنترل به عنوان مسأله‌ی جایگذاری کنترلکنندهها (CPP) شناخته می‌شود. در این مقاله CPP با استفاده از Kmean نظارت نشده (U-kmeans ) و الگوریتم بهینه‌ساز گله اسب (HOA) حل شده است. الگوریتم U-kmeans سوییچ‌ها را خوشه‌بندی می‌کند و تعداد کنترل‌گرها را تعیین می‌کند. از آنجا که مسأله CPP گسسته است، الگوریتم HOA از اپراتورهای ژنتیکی استفاده می‌کند که HOA بهبود یافته (MHOA) نام دارد. مرحله‌‌ی بعدی این مقاله، شامل یافتن مکان بهینه‌ی هر کنترلکننده در داخل خوشه خود با استفاده از MHOA است. برای بهبود نرخ همگرایی، MHOA از استراتژی یادگیری مبتنی بر مخالفت نخبگان (EOBL) استفاده می‌کند. نتایج نشان می‌دهد که روش پیشنهادی در مقایسه با سایر الگوریتم‌های مطرح شده از نظر تأخیر انتها به انتها، عدم توازن بار و مصرف انرژی عملکرد بهتری دارد. روش پیشنهادی با کاهش عدم توازن بار 9.66٪، تأخیر انتها به انتها 19.65٪ و میانگین مصرف انرژی 8.43٪ بهبود یافته است.
Software-Defined Networking (SDN) concept involves separating the control plane from the data plane. The network control is determined by an entity called the controller located on the control plane. Determining the optimal number and placement of controllers on the control plane is known as the Controller Placement Problem (CPP). This article addresses the resolution of CPP using Unsupervised Kmeans (U-kmeans) and Horse Herd Optimized Algorithm (HOA). The U-kmeans algorithm clusters switches and determines the number of controllers. Since the CPP problem is discrete, the HOA algorithm uses genetic operators, called Modified HOA (MHOA). The next step of this article involves finding the optimal location for each controller within its cluster using MHOA. To improve the convergence rate, MHOA utilizes an Elite Opposition-based Learning (EOBL) strategy. The effectiveness and scalability of the proposed algorithm are evaluated through simulation tests on various networks. The results show that the proposed method outperforms other state-of-the-art algorithms regarding metrics such as end-to-end delay, load imbalance, and energy consumption. In particular, the proposed method reduces load imbalance by 9.66%, end-to-end delay by 19.65%, and average energy consumption by 8.43%.
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