Distributed Computing

, Volume 18, Issue 3, pp 179–188

Efficient adaptive collect using randomization

  • Hagit Attiya
  • Fabian Kuhn
  • C. Greg Plaxton
  • Mirjam Wattenhofer
  • Roger Wattenhofer
Special Issue: Disc 04

DOI: 10.1007/s00446-005-0143-6

Cite this article as:
Attiya, H., Kuhn, F., Plaxton, C.G. et al. Distrib. Comput. (2006) 18: 179. doi:10.1007/s00446-005-0143-6

Abstract

An adaptive algorithm, whose step complexity adjusts to the number of active processes, is attractive for distributed systems with a highly-variable number of processes. The cornerstone of many adaptive algorithms is an adaptive mechanism to collect up-to-date information from all participating processes. To date, all known collect algorithms either have non-linear step complexity or they are impractical because of unrealistic memory overhead.

This paper presents new randomized collect algorithms with asymptotically optimal O(k) step complexity and linear memory overhead only. In addition we present a new deterministic collect algorithm that beats the best step complexity for previous polynomial-memory algorithms.

Keywords

Adaptive algorithmsTotal contentionRandomization

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • Hagit Attiya
    • 1
  • Fabian Kuhn
    • 2
  • C. Greg Plaxton
    • 3
    • 4
  • Mirjam Wattenhofer
    • 5
  • Roger Wattenhofer
    • 2
  1. 1.Department of Computer ScienceThe TechnionHaifaIsrael
  2. 2.Deptartment of Information Technology and Electrical Engineering, ETH ZurichZurichSwitzerland
  3. 3.Department of Computer ScienceUniversity of Texas at AustinAustin
  4. 4.Akamai Technologies, Inc.Cambridge
  5. 5.Deptartment of Computer Science, ETH ZurichZurichSwitzerland