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Configuration balancing for stochastic requests.

Franziska Eberle1, Anupam Gupta2, Nicole Megow3

  • 1Department of Mathematics, Technische Universität Berlin, Straße des 17. Juni 136, 10623 Berlin, Germany.

Mathematical Programming
|March 3, 2025
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Summary
This summary is machine-generated.

This study introduces algorithms for configuration balancing with stochastic requests, optimizing resource allocation. New methods achieve near-optimal performance for offline and online scenarios, improving load balancing strategies.

Keywords:
Load balancingStochastic routingStochastic schedulingUnrelated machines

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Area of Science:

  • Computer Science
  • Operations Research

Background:

  • Configuration balancing generalizes resource allocation problems like load balancing.
  • Stochastic requests introduce uncertainty in resource load, requiring probabilistic approaches.

Purpose of the Study:

  • Develop offline and online algorithms for configuration balancing with stochastic requests.
  • Minimize the makespan (maximum resource load) under uncertainty.

Main Methods:

  • Designed non-adaptive policies for offline and online settings.
  • Leveraged adaptivity for specific load balancing scenarios (related machines).
  • Utilized a novel structural characterization of optimal adaptive policies.

Main Results:

  • Achieved a -approximation for offline configuration balancing, matching lower bounds.
  • -competitive non-adaptive policy for online requests, asymptotically tight.
  • Constant-factor and -approximations for load balancing on related machines using adaptivity.

Conclusions:

  • The developed algorithms offer efficient solutions for stochastic configuration balancing.
  • The structural characterization of optimal policies is key to the results.
  • Findings advance the field of resource allocation under uncertainty.