The BLUE active queue management algorithms

  • Authors:

  • Wu-chang Feng;Kang G. Shin;Dilip D. Kandlur;Debanjan Saha

  • Affiliations:

  • Oregon Health and Science University, Beaverton, OR;University of Michigan, Ann Arbor, MI;IBM T. J. Watson Research Center, Yorktown Heights, NY;Tellium, Inc., West Long Branch, NJ

  • Venue:
  • IEEE/ACM Transactions on Networking (TON)
  • Year:
  • 2002

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Abstract

In order to stem the increasing packet loss rates caused by an exponential increase in network traffic, the ietf has been considering the deployment of active queue management techniques such as Red [14]. While active queue management can potentially reduce packet loss rates in the Internet, we show that current techniques are ineffective in preventing high loss rates. The inherent problem with these queue management algorithms is that they use queue lengths as the indicator of the severity of congestion. In light of this observation, a fundamentally different active queue management algorithm, called Blue, is proposed, implemented, and evaluated. Blue uses packet loss and link idle events to manage congestion. Using both simulation and controlled experiments, Blue is shown to perform significantly better than Red, both in terms of packet loss rates and buffer size requirements in the network. As an extension to Blue, a novel technique based on Bloom filters [2] is described for enforcing fairness among a large number of flows. In particular, we propose and evaluate Stochastic Fair Blue (SFB), a queue management algorithm which can identify and rate-limit nonresponsive flows using a very small amount of state information.