OPS: A Fairness Link Allocation Based on SDN in Datacenter Networks

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Communication Networks and Distributed Systems Pub Date : 2020-05-05 DOI:10.1504/ijcnds.2020.10028907
T. Kao, Y. Miao, San-Yuan Wang
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Abstract

Nowadays, redundant topologies with multiple forwarding paths between original-destination (OD) pairs have been proposed for datacentre networks, such as fat-tree topology. To achieve resultant fairness (fair utilisation) in this topology, previous algorithms were based on traditional shortest path algorithms or equal-cost multiple paths algorithms (ECMP). However, these algorithms are facing with take up a lot of resources of the SDN controller and may cause a large amount of loss of resultant fairness for the network that has different configurations of link capacity. In this paper, we present out port selection (OPS) mechanisms, including modular out port selection (MOPS) and random out port selection (ROPS), novel methods to improve resultant fairness of fat-tree topology SDN-based datacentre network. Compare with static load-balance methods, our MOPS and ROPS can reduce 65% and 33% loss of fairness, respectively. Further, combining with elephant flows detecting and handling mechanisms, MOPSE and ROPSE can dynamically assign better forwarding path for elephant flows.
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OPS:数据中心网络中基于SDN的公平链路分配
目前,在数据中心网络中提出了一种冗余拓扑,在OD对之间具有多条转发路径,如胖树拓扑。为了在这种拓扑结构中实现最终的公平性(公平利用),以前的算法是基于传统的最短路径算法或等成本多路径算法(ECMP)。但是,这些算法都面临着占用SDN控制器大量资源的问题,对于链路容量配置不同的网络,可能会造成大量的公平性损失。在本文中,我们提出了输出端口选择(OPS)机制,包括模块化端口选择(MOPS)和随机端口选择(ROPS),这是提高基于胖树拓扑sdn的数据中心网络结果公平性的新方法。与静态负载平衡方法相比,我们的MOPS和ROPS可以分别减少65%和33%的公平性损失。此外,MOPSE和ROPSE结合象流检测和处理机制,可以为象流动态分配更好的转发路径。
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来源期刊
CiteScore
2.50
自引率
46.20%
发文量
57
期刊介绍: IJCNDS aims to improve the state-of-the-art of worldwide research in communication networks and distributed systems and to address the various methodologies, tools, techniques, algorithms and results. It is not limited to networking issues in telecommunications; network problems in other application domains such as biological networks, social networks, and chemical networks will also be considered. This feature helps in promoting interdisciplinary research in these areas.
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