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An Efficient PTAS for Stochastic Load Balancing with Poisson Jobs

Authors Anindya De, Sanjeev Khanna, Huan Li, Hesam Nikpey

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ACM Subject Classification
  • Theory of computation → Discrete optimization
  • Theory of computation → Approximation algorithms analysis
  • Theory of computation → Scheduling algorithms
Keywords
  • Efficient PTAS
  • Makespan Minimization
  • Scheduling
  • Stochastic Load Balancing
  • Poisson Distribution

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Abstract

We give the first polynomial-time approximation scheme (PTAS) for the stochastic load balancing problem when the job sizes follow Poisson distributions. This improves upon the 2-approximation algorithm due to Goel and Indyk (FOCS'99). Moreover, our approximation scheme is an efficient PTAS that has a running time double exponential in 1/ε but nearly-linear in n, where n is the number of jobs and ε is the target error. Previously, a PTAS (not efficient) was only known for jobs that obey exponential distributions (Goel and Indyk, FOCS'99).
Our algorithm relies on several probabilistic ingredients including some (seemingly) new results on scaling and the so-called "focusing effect" of maximum of Poisson random variables which might be of independent interest.

Cite As Get BibTex

Anindya De, Sanjeev Khanna, Huan Li, and Hesam Nikpey. An Efficient PTAS for Stochastic Load Balancing with Poisson Jobs. In 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 168, pp. 37:1-37:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.ICALP.2020.37

Author Details

Anindya De
  • Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
Sanjeev Khanna
  • Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
Huan Li
  • Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
Hesam Nikpey
  • Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA

Acknowledgements

We thank the anonymous reviewers for their valuable feedback. This work was supported in part by NSF awards CCF-1617851, CCF-1763514, CCF-1910534, CCF-1926872, and CCF-1934876.

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