We consider scheduling in the M/G/1 queue with unknown job sizes. It is known that the Gittins policy minimizes mean response time in this setting. However, the behavior of the tail of response time under Gittins is poorly understood, even in the large-response-time limit. Characterizing Gittins’s asymptotic tail behavior is important because if Gittins has optimal tail asymptotics, then it simultaneously provides optimal mean response time and good tail performance. In this work, we give the first comprehensive account of Gittins’s asymptotic tail behavior. For heavy-tailed job sizes, we find that Gittins always has asymptotically optimal tail. The story for light-tailed job sizes is less clear-cut: Gittins’s tail can be optimal, pessimal, or in between. To remedy this, we show that a modification of Gittins avoids pessimal tail behavior, while achieving near-optimal mean response time.
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Optimal activation of halting multi‐armed bandit models
Abstract We study new types of dynamic allocation problems theHalting Banditmodels. As an application, we obtain new proofs for the classic Gittins index decomposition result compare Gittins (Journal of the Royal Statistical Society, Series B, 1979, 41, 148–177), and recent results of the authors in Cowan and Katehakis (Probability in the Engineering and Informational Sciences, 2015, 29, 51–76).
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- Award ID(s):
- 1662629
- PAR ID:
- 10669472
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Naval Research Logistics (NRL)
- Volume:
- 70
- Issue:
- 7
- ISSN:
- 0894-069X
- Page Range / eLocation ID:
- 639 to 652
- Subject(s) / Keyword(s):
- adaptive systems, autonomous reasoning and learning, dynamic data driven systems, machine learning, Markovian decision processes
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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