Abstract
Modern data centers can process a massive amount of data in a short time with minimal errors. Data center networks (DCNs) use equal-cost, multi-path topologies to deliver split flows across alternative paths between the core layer and hosted servers, which could lead to significant overload if path scheduling is inefficient. Thus, distributing incoming requests among these paths is crucial for providing higher throughput and protection against link or switch failures. Several approaches have been proposed for path selection, mainly relying on round-robin and least-congested methods. In this paper, we propose a load-balancing method based on betweenness centrality to improve the overall performance of a data center in terms of throughput, delay, and energy consumption. For evaluation, we compare our method with baseline methods of different DCN topologies: fat-tree, DCell, and BCube. On average, the evaluation results show that our method outperforms the others. It increases throughput by 202% and 33% while reducing delay by 20% and 22%, and energy consumption by 40% and 41% compared to the round-robin and least-congested methods, respectively.
Highlights
Nowadays, studies in computer networking focus on data center networks (DCNs) and challenges involved in scheduling the paths of these networks
Our evaluation is based on three performance metrics: throughput, end-to-end delay, and energy consumption
The throughput results in the fat-tree topology, as displayed in Fig. 5a show that our method has the best results compared to the other load-balancing methods
Summary
Studies in computer networking focus on data center networks (DCNs) and challenges involved in scheduling the paths of these networks. Data center switches are designed to forward data between endpoints, while servers process the data [1], [2], [3]. The importance of these data centers is increasing, as a considerable number of networks are linked. The most commonly used architecture in current DCNs is the three-tier architecture, comprising a core layer, an aggregate layer, and an access layer, from top to bottom, as shown in Fig. 1 [3], [2], [4]. All topologies presented were implemented and evaluated in our study.
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