ENERGY-EFFICIENT TRAFFIC SCHEDULING MECHANISM BASED ON IMPROVED GENETIC ALGORITHM

作     者：Bai, Ningzhuo Li, Honghui Fu, Xueliang 

作者机构：College of Computer & Information Engineering Inner Mongolia Agricultural University China 

出 版 物：《UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science》 (UPB Sci. Bull. Ser. C Electr. Eng. Comput. Sci.)

年 卷 期：2024年第86卷第4期

页      面：417-436页

核心收录：

基　　金：This work is partially supported by the National Natural Science Foundation Project (61962047)  the Inner Mongolia Autonomous Region Natural Science Foundation Project (2024MS06002)  the Inner Mongolia Autonomous Region Science and Technology Major Special Project (2021ZD0005)  the Inner Mongolia Autonomous Region Directly Affiliated Universities Basic Research Business Fee Project (BR22-14-05)  the Inner Mongolia Autonomous Region University Science and Technology Innovation Team Construction Special Project (BR231302)  the Inner Mongolia Autonomous Region Science and Technology Plan Project (2022YFHH0070) 

主　　题：Data centers 

摘      要：The swift advancements in information technology have ushered in the era of large language models and artificial intelligence. Alongside the rapid expansion of data center scale and business demands, the issue of energy consumption in data center networks has become increasingly prominent. Software-Defined Networking (SDN) revolutionizes network architecture by decoupling the data plane from the control plane, enhancing network flexibility and its adoption in data center networks. To tackle data center energy consumption, this paper introduces an energy-saving traffic scheduling algorithm for SDN data center networks, utilizing an enhanced genetic algorithm. Leveraging network monitoring capabilities in SDN, the algorithm enhances traffic scheduling through an advanced genetic algorithm and puts inactive switches into sleep mode to meet the demands of current traffic flow. This approach reduces active network devices, leading to energy savings. Simulation results confirm the method’s feasibility and effectiveness. © 2024, Politechnica University of Bucharest. All rights reserved.