Load balancing method using reinforcement learning in SDN

カテゴリ: 部門大会

論文No: SS2-2

グループ名: 【C】2024年電気学会電子・情報・システム部門大会

発行日: 2024/08/28

タイトル(英語): Load balancing method using reinforcement learning in SDN

著者名: 松村 太希（千葉大学）,中間 公啓（千葉大学）,小圷 成一（千葉大学）,銭 飛（関東学院大学）

著者名(英語): Taiki Matsumura (Chiba University),Kimihiro Nakama (Chiba University),Seiichi Koakutsu (Chiba University),Fei Quian (Kanto Gakuin University)

キーワード: 学習オートマトン|ネットワーク|負荷分散|強化学習|SDN ネットワーキング|learning Automaton|network|load balancing|Reinforcement Learning|SDN Networking

要約(日本語): The role of data centers has been becoming increasingly crucial in recent years, because there has been a growing emphasis on decentralizing data processing in IoT networking. The ability to swiftly respond to incoming requests has emerged as a pivotal metric for assessing data center systems. Load balancing challenges in data centers have persistently posed hurdles, affecting performance enhancement, availability improvement, and cost reduction. This article delves into the load balancing challenges in SDN-enabled data centers and introduces a novel load balancing method using reinforcement learning. The proposed method seeks to equalize server loads for queries with established access patterns. The results of computer experiments indicate the effectiveness of the proposed method.