Lexicographically optimal smoothing for broadband traffic multiplexing

  • Authors:

  • Stergios Anastasiadis;Peter Varman;Jeffrey Scott Vitter;Ke Yi

  • Affiliations:

  • Duke University, Durham, N.C.;Rice University, Houston, TX;Duke University, Durham, N.C.;Duke University, Durham, N.C.

  • Venue:
  • Proceedings of the twenty-first annual symposium on Principles of distributed computing
  • Year:
  • 2002

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Abstract

We investigate the problem of smoothing multiplexed network traffic, when either a streaming server transmits data to multiple clients, or a server accesses data from multiple storage devices or other servers. We introduce efficient algorithms for lexicographically optimally smoothing the aggregate bandwidth requirements over a shared network link. In the data transmission problem, we consider the case in which the clients have different buffer capacities but no bandwidth constraints, or no buffer capacities but different bandwidth constraints. For the data access problem, we handle the general case of a shared buffer capacity and individual network bandwidth constraints. Previous approaches in the literature for the data access problem handled either the case of only a single stream or did not compute the lexicographically optimal schedule.Lexicographically optimal smoothing (lexopt smoothing) has several advantages. By provably minimizing the variance of the required aggregate bandwidth, maximum resource requirements within the network become more predictable, and useful resource utilization increases. Fairness in sharing a network link by multiple users can be improved, and new requests from future clients are more likely to be successfully admitted without the need for frequently rescheduling previously accepted traffic. Efficient resource management at the network edges can better meet quality of service requirements without restricting the scalability of the system.