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Configuration balancing for stochastic requests

Eberle, Franziska, Gupta, Anupam, Megow, Nicole, Moseley, Benjamin and Zhou, Rudy (2023) Configuration balancing for stochastic requests. In: Del Pia, Alberto and Kaibel, Volker, (eds.) Integer Programming and Combinatorial Optimization - 24th International Conference, IPCO 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH, pp. 127-141. ISBN 9783031327254

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Identification Number: 10.1007/978-3-031-32726-1_10

Abstract

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.

Item Type: Book Section
Additional Information: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Divisions: Mathematics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics
Date Deposited: 25 Jul 2023 10:36
Last Modified: 26 May 2024 05:54
URI: http://eprints.lse.ac.uk/id/eprint/119826

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