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Learning Proportionally Fair Allocations with Low Regret by Mohammad Sadegh Talebi and Alexandre Proutiere is a Computer Science article available to read on EtoBox.

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This paper addresses a generic sequential resource allocation problem, where in each round a decision maker selects an allocation of resources (servers) to a set of tasks consisting of a large number of jobs. A job of task i assigned to server j is successfully treated with probability θ\_ij $ in a round, and the decision maker is informed on whether this job is completed at the end of the round. The probabilities θ\_ij $'s are initially unknown and have to be learned. The objective of the decision maker is to sequentially assign jobs of various tasks to servers so that it rapidly learns and converges to the Proportionally Fair (PF) allocation (or other similar allocations achieving an appropriate trade-off between efficiency and fairness). We formulate the problem as a multi-armed bandit (MAB) optimization problem, and devise sequential assignment algorithms with low regret (defined as the difference in utility achieved by an oracle algorithm aware of the θ\_ij $'s and by the proposed algorithm over a given number of slots). We first provide the properties of the so-called Restricted-PF (RPF) allocation, obtained by assuming that each task can only use a single server, and in part

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Author

Mohammad Sadegh Talebi and Alexandre Proutiere

Publisher

ACM

Published

2018

Language

EN

Field

Computer Science (Physical Sciences)