A two-phase multipathing scheme based on genetic algorithm for data center networking

Lyno Henrique G. Ferraz, D. M. F. Mattos, O. Duarte
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引用次数: 14

Abstract

Data centers for cloud computing should allocate services with different traffic patterns, provide high data transfer capacity and link fault tolerance. Data center network topologies provide physical connection redundancy, which forwarding mechanisms avail to generate multiple paths. In this paper, we divide multipathing into two phases: (i) Configuration phase based on genetic algorithms to minimize path lengths and maximize link usage diversity; (ii) Path selection phase based on heuristics to minimize path reuse. The proposed multipathing scheme implements minimal modification in infrastructure. Our proposal only requires common network devices features and it avoids any tenant modification. We develop a flow simulator to evaluate multipathing techniques. The simulations model flow behaviors in different data center scenarios and compares the proposed scheme with multipathing techniques in literature. The results show the proposed scheme enhances transmission rates, even in the highest network utilization scenarios.
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一种基于遗传算法的数据中心组网两阶段多路径方案
云计算数据中心应按照不同的流量模式分配业务,提供高的数据传输能力和链路容错能力。数据中心网络拓扑结构提供了物理连接冗余,通过转发机制可以生成多条路径。本文将多路径划分为两个阶段:(i)基于遗传算法的配置阶段,以最小化路径长度和最大化链路使用多样性;(ii)基于启发式的路径选择阶段,以尽量减少路径重用。该多路径方案实现了对基础设施的最小修改。我们的建议只需要常见的网络设备特性,并且避免了任何租户修改。我们开发了一个流模拟器来评估多路径技术。模拟了不同数据中心场景下的流行为,并与文献中的多路径技术进行了比较。结果表明,即使在网络利用率最高的情况下,该方案也能提高传输速率。
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