论文标题
可重新配置数据中心网络的自我调整的自我树拓扑
Self-Adjusting Ego-Trees Topology for Reconfigurable Datacenter Networks
论文作者
论文摘要
数据中心的最新拓扑（DC）和高性能计算（HPC）网络是符合需求和静态的。因此，此类网络拓扑是针对最坏的交通情况进行了优化的，并且在存在这种情况时无法利用需求模式的优势。但是，最近的光学开关技术可以实时动态重新配置电路开关拓扑的概念。这种能力为设计自我调整网络的设计打开了大门：具有需求感和动态拓扑的网络，可以在线建立和重新调整节点之间的链接并响应不断发展的流量模式。 本文研究了一个最近提出的光叶子可重构网络的模型。我们提出了一种新颖的算法，即Greedyegotrees，该算法会动态地改变网络拓扑。该算法贪婪地为网络中的节点建造了自我树，并考虑到网络的全局需求，在那里节点合作以互相帮助。我们表明，Greedyegotrees具有良好的理论特性，优于其他可能的算法（例如静态扩张器和贪婪的动态匹配），并且可以显着改善实际DC和HPC痕迹的平均路径长度。
State-of-the-art topologies for datacenters (DC) and high-performance computing (HPC) networks are demand-oblivious and static. Therefore, such network topologies are optimized for the worst-case traffic scenarios and can't take advantage of changing demand patterns when such exist. However, recent optical switching technologies enable the concept of dynamically reconfiguring circuit-switched topologies in real-time. This capability opens the door for the design of self-adjusting networks: networks with demand-aware and dynamic topologies in which links between nodes can be established and re-adjusted online and respond to evolving traffic patterns. This paper studies a recently proposed model for optical leaf-spine reconfigurable networks. We present a novel algorithm, GreedyEgoTrees, that dynamically changes the network topology. The algorithm greedily builds ego trees for nodes in the network, where nodes cooperate to help each other, taking into account the global needs of the network. We show that GreedyEgoTrees has nice theoretical properties, outperforms other possible algorithms (like static expander and greedy dynamic matching) and can significantly improve the average path length for real DC and HPC traces.
