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- W4380669417 abstract "We study the optimal control problem in stochastic queueing networks with a set of job dispatchers connected to a set of parallel servers with queues. Jobs arrive at the dispatchers and get routed to the servers following some routing policy. The arrival processes of jobs and the service processes of servers are stochastic with unknown arrival rates and service rates. Upon the completion of each job from dispatcher un at server sm, a random utility whose mean is unknown is obtained. We seek to design a control policy that makes routing decisions at the dispatchers and scheduling decisions at the servers to maximize the total utility obtained by the end of a finite time horizon T. The performance of policies is measured by regret, which is defined as the difference in total expected utility with respect to the optimal dynamic policy that has access to arrival rates, service rates and underlying utilities." @default.
- W4380669417 created "2023-06-15" @default.
- W4380669417 creator A5036587377 @default.
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- W4380669417 date "2023-06-19" @default.
- W4380669417 modified "2023-09-28" @default.
- W4380669417 title "Joint Learning and Control in Stochastic Queueing Networks with Unknown Utilities" @default.
- W4380669417 doi "https://doi.org/10.1145/3578338.3593547" @default.
- W4380669417 hasPublicationYear "2023" @default.
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