Online balanced repartitioning

Chen Avin, Andreas Loukas, Maciej Pacut, Stefan Schmid

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

7 Scopus citations


Distributed cloud applications, including batch processing, streaming, and scale-out databases, generate a significant amount of network traffic and a considerable fraction of their runtime is due to network activity. This paper initiates the study of deterministic algorithms for collocating frequently communicating nodes in a distributed networked systems in an online fashion. In particular, we introduce the Balanced RePartitioning (BRP) problem: Given an arbitrary sequence of pairwise communication requests between n nodes, with patterns that may change over time, the objective is to dynamically partition the nodes into ℓ clusters, each of size k, at a minimum cost. Every communication request needs to be served: if the communicating nodes are located in the same cluster, the request is served locally, at cost 0; if the nodes are located in different clusters, the request is served remotely using inter-cluster communication, at cost 1. The partitioning can be updated dynamically (i.e., repartitioned), by migrating nodes between clusters at cost α per node migration. The goal is to devise online algorithms which find a good trade-off between the communication and the migration cost, i.e., “rent” or “buy”, while maintaining partitions which minimize the number of inter-cluster communications. BRP features interesting connections to other well-known online problems. In particular, we show that scenarios with ℓ = 2 generalize online paging, and scenarios with k = 2 constitute a novel online version of maximum matching. We consider settings both with and without cluster-size augmentation. Somewhat surprisingly (and unlike online paging), we prove that any deterministic online algorithm has a competitive ratio of at least k, even with augmentation. Our main technical contribution is an O(k log k)-competitive deterministic algorithm for the setting with (constant) augmentation. This is attractive as, in contrast to ℓ, k is likely to be small in practice. For the case of matching (k = 2), we present a constant competitive algorithm that does not rely on augmentation.

Original languageEnglish
Title of host publicationDistributed Computing - 30th International Symposium, DISC 2016, Proceedings
EditorsCyril Gavoille, David Ilcinkas
PublisherSpringer Verlag
Number of pages14
ISBN (Print)9783662534250
StatePublished - 1 Jan 2016
Event30th International Symposium on Distributed Computing, DISC 2016 - Paris, France
Duration: 27 Sep 201629 Sep 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9888 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference30th International Symposium on Distributed Computing, DISC 2016


  • Algorithms
  • Cloud computing
  • Clustering
  • Competitive analysis
  • Dynamic graphs
  • Graph partitioning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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