Dynamic node clustering in hierarchical optical data center network architectures

Abstract

During the past decade an increasing trend in the Data Center Network's traffic has been observed. This traffic is characterized mostly by many small bursty flows (mice) that last for less than few milliseconds as well as a few heavier more persistent (elephant) flows between certain number of nodes. As a result many relatively underutilized network links become momentarily hotspots with increased chance of packet loss. A potential solution could be given by Reconfigurable Optical Data Centers, due to higher traffic aggregation links and topology adaptation capabilities. An example is a novel two level hierarchical WDM-Based scalable Data Center Network architecture, RHODA, which is based on the interconnection of high speed equal sized clusters of Racks. We study the traffic based dynamic cluster membership reconfiguration of the Racks. Main goal is to maintain a near optimal network operation with respect to minimization of the inter cluster traffic, while emphasising better link utilization and network scalability. We present four algorithms, two deterministic greedy and two stochastic iterative, and discuss the tradeoffs of their use. Our results draw two main conclusion: 1) Stochastic iterative algorithms are more suitable for dynamic traffic based reconfiguration 2) Fast algorithmic deployments come at a price of reduced optimality