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Learning and Balancing Unknown Loads in Large-Scale Systems

Author

Listed:
  • Diego Goldsztajn

    (Department of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, Netherlands; and Inria, 06902 Sophia Antipolis, France)

  • Sem C. Borst

    (Department of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, Netherlands)

  • Johan S. H. van Leeuwaarden

    (Department of Econometrics and Operations Research, Tilburg University, 5037 AB Tilburg, Netherlands)

Abstract

Consider a system of identical server pools where tasks with exponentially distributed service times arrive as a time-inhomogeneous Poisson process. An admission threshold is used in an inner control loop to assign incoming tasks to server pools, while in an outer control loop, a learning scheme adjusts this threshold over time to keep it aligned with the unknown offered load of the system. In a many-server regime, we prove that the learning scheme reaches an equilibrium along intervals of time when the normalized offered load per server pool is suitably bounded and that this results in a balanced distribution of the load. Furthermore, we establish a similar result when tasks with Coxian distributed service times arrive at a constant rate and the threshold is adjusted using only the total number of tasks in the system. The novel proof technique developed in this paper, which differs from a traditional fluid limit analysis, allows us to handle rapid variations of the first learning scheme, triggered by excursions of the occupancy process that have vanishing size. Moreover, our approach allows us to characterize the asymptotic behavior of the system with Coxian distributed service times without relying on a fluid limit of a detailed state descriptor.

Suggested Citation

  • Diego Goldsztajn & Sem C. Borst & Johan S. H. van Leeuwaarden, 2025. "Learning and Balancing Unknown Loads in Large-Scale Systems," Mathematics of Operations Research, INFORMS, vol. 50(2), pages 1139-1172, May.
  • Handle: RePEc:inm:ormoor:v:50:y:2025:i:2:p:1139-1172
    DOI: 10.1287/moor.2021.0212
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