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Local MST computation with short advice

Published: 09 June 2007 Publication History

Abstract

We use the recently introduced advising scheme framework for measuring the difficulty of locally distributively computing a Minimum Spanning Tree (MST). An (m,t)-advising scheme for a distributed problem P is a way, for every possible input I of P, to provide an "advice" (i.e., a bit string) about I to each node so that: (1) the maximum size of the advices is at most m bits, and (2) the problem P can be solved distributively in at most t rounds using the advices as inputs. In case of MST, the output returned by each node of a weighted graph G is the edge leading to its parent in some rooted MST T of G. Clearly, there is a trivial (log n,0)-advising scheme for MST (each node is given the local port number of the edge leading to the root of some MST T), and it is known that any (0,t)-advising scheme satisfies t ≥ Ω (√n). Our main result is the construction of an (O(1),O(log n))-advising scheme for MST. That is, by only giving a constant number of bits of advice to each node, one can decrease exponentially the distributed computation time of MST in arbitrary graph, compared to algorithms dealing with the problem in absence of any a priori information. We also consider the average size of the advices. On the one hand, we show that any (m,0)-advising scheme for MST gives advices of average size Ω(log n). On the other hand we design an (m,1)-advising scheme for MST with advices of constant average size, that is one round is enough to decrease the average size of the advices from log(n) to constant.

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cover image ACM Conferences
SPAA '07: Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
June 2007
376 pages
ISBN:9781595936677
DOI:10.1145/1248377
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 09 June 2007

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Author Tags

  1. distributed algorithm
  2. local computation
  3. minimum spanning tree

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  • (2023)Pebble Guided Treasure Hunt in PlaneNetworked Systems10.1007/978-3-031-37765-5_11(141-156)Online publication date: 7-Jul-2023
  • (2017)Global Versus Local Computations: Fast Computing with IdentifiersStructural Information and Communication Complexity10.1007/978-3-319-72050-0_6(90-105)Online publication date: 30-Dec-2017
  • (2016)Optimizations for Multiple Collective Sources in Delivery Systems2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2016.0075(461-464)Online publication date: Oct-2016
  • (2015)Space-Optimal Time-Efficient Silent Self-Stabilizing Constructions of Constrained Spanning Trees2015 IEEE 35th International Conference on Distributed Computing Systems10.1109/ICDCS.2015.66(589-598)Online publication date: Jun-2015
  • (2013)Local Decision and Verification with Bounded-Size Outputs15th International Symposium on Stabilization, Safety, and Security of Distributed Systems - Volume 825510.5555/2718693.2718703(133-147)Online publication date: 13-Nov-2013
  • (2013)Towards a complexity theory for local distributed computingJournal of the ACM10.1145/249922860:5(1-26)Online publication date: 28-Oct-2013
  • (2013)Toward more localized local algorithmsDistributed Computing10.1007/s00446-012-0174-826:5-6(289-308)Online publication date: 1-Oct-2013
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  • (2012)On the Impact of Identifiers on Local DecisionPrinciples of Distributed Systems10.1007/978-3-642-35476-2_16(224-238)Online publication date: 2012
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