FlowMate: scalable on-line flow clustering

  • Authors:
  • Ossama Younis;Sonia Fahmy

  • Affiliations:
  • Department of Computer Sciences, Purdue University, West Lafayette, IN;Department of Computer Sciences, Purdue University, West Lafayette, IN

  • Venue:
  • IEEE/ACM Transactions on Networking (TON)
  • Year:
  • 2005

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Abstract

We design and implement an efficient on-line approach, FlowMate, for clustering flows (connections) emanating from a busy server, according to shared bottlenecks. Clusters can be periodically input to load balancing, congestion coordination, aggregation, admission control, or pricing modules. FlowMate uses in-band (passive) end-to-end delay measurements to infer shared bottlenecks. Delay information is piggybacked on feedback from the receivers, or, if impossible, TCP or application round-trip time estimates are used. We simulate FlowMate and examine the effects of network load, traffic burstiness, network buffer sizes, and packet drop policies on clustering correctness, evaluated via a novel accuracy metric. We find that coordinated congestion management techniques are more fair when integrated with Flow-Mate. We also implement FlowMate in the Linux kernel v2.4.17 and evaluate its performance on the Emulab testbed, using both synthetic and tcplib-generated traffic. Our results demonstrate that clustering of medium to long-lived flows is accurate, even with bursty background traffic. Finally, we validate our results on the Internet Planetlab testbed.