Local-on-average distributed tasks

Merav Parter, David Peleg, Shay Solomon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations


A distributed task is local if its time complexity is (nearly) constant, otherwise it is global. Unfortunately, local tasks are relatively scarce, and most distributed tasks require time at least logarithmic in the network size (and often higher than that). In a dynamic setting, i.e., when the network undergoes repeated and frequent topological changes, such as vertex and edge insertions and deletions, it is desirable to be able to perform a local update procedure around the modified part of the network, rather than running a static global algorithm from scratch following each change. This paper makes a step towards establishing the hypothesis that many (statically) non-local distributed tasks are local-on-average in the dynamic setting, namely, their amortized time complexity is O(log∗ n). Towards establishing the plausibility of this hypothesis, we propose a strategy for transforming static O(polylog(n)) time algorithms into dynamic O(log∗ n) amortized time update procedures. We then demonstrate the usefulness of our strategy by applying it to several fundamental problems whose static time complexity is logarithmic, including forestdecomposition, edge-orientation and coloring sparse graphs, and show that their amortized time complexity in the dynamic setting is indeed O(log∗ n).

Original languageEnglish
Title of host publication27th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2016
EditorsRobert Krauthgamer
PublisherAssociation for Computing Machinery
Number of pages20
ISBN (Electronic)9781510819672
StatePublished - 2016
Event27th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2016 - Arlington, United States
Duration: 10 Jan 201612 Jan 2016

Publication series

NameProceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms


Conference27th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2016
Country/TerritoryUnited States


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