Exploiting IP multicast in content-based publish-subscribe systems

  • Authors:
  • Lukasz Opyrchal;Mark Astley;Joshua Auerbach;Guruduth Banavar;Robert Strom;Daniel Sturman

  • Affiliations:
  • Dept. of EECS, University of Michigan, 1301 Beal Avenue, Ann Arbor, MI;IBM T.J. Watson Research Center, 30 Saw Mill River Rd., Hawthorne, NY;IBM T.J. Watson Research Center, 30 Saw Mill River Rd., Hawthorne, NY;IBM T.J. Watson Research Center, 30 Saw Mill River Rd., Hawthorne, NY;IBM T.J. Watson Research Center, 30 Saw Mill River Rd., Hawthorne, NY;IBM T.J. Watson Research Center, 30 Saw Mill River Rd., Hawthorne, NY

  • Venue:
  • IFIP/ACM International Conference on Distributed systems platforms
  • Year:
  • 2000

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Abstract

Publish-subscribe systems are evolving toward using content-based subscription rather than subject-based subscription. A key problem in implementing such systems is that a straightforward mapping from matching sets to multicast groups produces a number of groups that rapidly grows beyond practical limits. This paper proposes a set of alternative algorithms for solving this problem, by: (1) using a smaller set of overbroad multicast groups, judiciously chosen to minimize imprecision; (2) issuing multiple multicasts to appropriately chosen clusters; or (3) sending an event over multiple hops each involving a multicast to a set of neighbors. We evaluate these algorithms on a simulated wide-area network. We find that (1) a simple flooding algorithm is viable over an extensive range of conditions; and (2) under conditions of high selectivity and high regionalism of subscriptions, the other approaches mentioned above perform significantly better; however, the specific algorithm to use depends upon the economics of deployment.