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Joint optimization of energy saving and load balancing for data center networks based on software defined networks
Author(s) -
He Yihao,
Lu Zebin,
Lei Junru,
Deng Shuhua,
Gao Xieping
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.6134
Subject(s) - computer science , load balancing (electrical power) , data center , energy consumption , software defined networking , distributed computing , cloud computing , load management , scheduling (production processes) , integer programming , computer network , mathematical optimization , engineering , algorithm , geometry , mathematics , electrical engineering , grid , operating system
Summary To meet the surging demand for artificial intelligence and cloud service, data centers have been expanding rapidly on recent years. Therefore, data center networks have received great attention recently and more challenges gradually emerged. The exiting technology of data center networks (DCNs) has presented two problems: high energy consumption and network load imbalance. Traditionally, the middle‐box hardware is dedicated and complexly merged, such as network load balancer and network energy optimizer. As an emerging architecture, software defined networks (SDNs) brings an opportunity to accomplish load balancing and energy optimization simultaneously with its characteristics. In this article, we propose a traffic flow management strategy which jointly considers energy optimization and load balancing. The strategy forwards traffic flows with maximum available bandwidth multipath routing to balance network load. We minimize activated links and switches to save energy by scheduling traffic flows. We jointly formulate these as an integer linear programming (ILP) problem. We propose a heuristic algorithm to handle the problem. The full simulation results reveal the high efficiency of our algorithm and the coexistence of network energy optimization and load balancing.