Configuration Balancing for Stochastic Requests

Franziska Eberle, Anupam Gupta, Nicole Megow, Benjamin Moseley, Rudy Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The configuration balancing problem with stochastic requests generalizes well-studied resource allocation problems such as load balancing and virtual circuit routing. There are given m resources and n requests; each request has multiple possible configurations, each of which increases the load of each resource by some amount. The goal is to select one configuration for each request to minimize the makespan: the load of the most-loaded resource. In the stochastic setting, the amount by which a configuration increases the resource load is uncertain until the configuration is chosen, but we are given a probability distribution. We develop both offline and online algorithms for configuration balancing with stochastic requests. When the requests are known offline, we give a non-adaptive policy for configuration balancing with stochastic requests that O(logmloglogm) -approximates the optimal adaptive policy, which matches a known lower bound for the special case of load balancing on identical machines. When requests arrive online in a list, we give a non-adaptive policy that is O(log m) competitive. Again, this result is asymptotically tight due to information-theoretic lower bounds for special cases (e.g., for load balancing on unrelated machines). Finally, we show how to leverage adaptivity in the special case of load balancing on related machines to obtain a constant-factor approximation offline and an O(log log m) -approximation online. A crucial technical ingredient in all of our results is a new structural characterization of the optimal adaptive policy that allows us to limit the correlations between its decisions.

Original languageEnglish (US)
Title of host publicationInteger Programming and Combinatorial Optimization - 24th International Conference, IPCO 2023, Proceedings
EditorsAlberto Del Pia, Volker Kaibel
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9783031327254
StatePublished - 2023
Event24th International Conference on Integer Programming and Combinatorial Optimization, IPCO 2023 - Madison, United States
Duration: Jun 21 2023Jun 23 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13904 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Conference on Integer Programming and Combinatorial Optimization, IPCO 2023
Country/TerritoryUnited States


  • load balancing
  • stochastic routing
  • stochastic scheduling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Configuration Balancing for Stochastic Requests'. Together they form a unique fingerprint.

Cite this