We'd like to understand how you use our websites in order to improve them. Register your interest. We consider a service system where individual users share a common resource, modeled as a processor-sharing queue. Arriving users observe the current load in the system, and should decide whether to join it or not.
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We'd like to understand how you use our websites in order to improve them. Register your interest. We consider a service system where individual users share a common resource, modeled as a processor-sharing queue. Arriving users observe the current load in the system, and should decide whether to join it or not. The motivation for this model is based, in part, on best-effort service classes in computer communication networks. This decision problem is modeled as a noncooperative dynamic game between the users, where each user will enter the system only if its expected service time given the system description and policies of subsequent users is not larger than its quality of service QoS requirement.
The present work generalizes previous results by Altman and Shimkin , where all users were assumed identical in terms of their QoS requirements and decision policies; here we allow heterogeneous requirements, hence different policies.
The main result is the existence and uniqueness of the equilibrium point in this system, which specifies a unique threshold policy for each user type. Computation of the equilibrium thresholds are briefly discussed, as well as dynamic learning schemes which motivate the Nash equilibrium solution for this system.
This is a preview of subscription content, log in to check access. Rent this article via DeepDyve. Control 39 4 — Altman and N. Shimkin, Individual equilibrium and learning in a processor sharing system, Oper. Google Scholar. Assaf and M. Haviv, Reneging from processor sharing systems and random queues, Math. Basar and G. Ben-Shahar, A. Orda and N. Shimkin, Best-effort resource sharing by users with QoS requirements, in: Proc.
Buche and H. Kushner, Stochastic approximation and user adaptation in a competitive resource sharing system, preprint, Brown University Economides and J. Silvester, Multi-objective routing in integrated services networks: A game theory approach, in: Proc. Haviv, Stable srategies for processor sharing systems, European J. Hsiao and A. Lazar, Optimal decentralized flow control of Markovian queueing networks with multiple controllers, Performance Evaluation 13 3 — Korilis and A.
Lazar, On the existence of equilibria in noncooperative optimal flow control, J. ACM 42 3 — Lazar, A. Orda and D. Networking 5 6 — Mandelbaum and N.
Shimkin, A model for rational abandonments from invisible queues, Queueing Systems , to appear. Press, Cambridge, MA, Naor, On the regulation of queue size by Levying tolls, Econometrica 37 15— Orda, R. Rom and N. Networking 1 5 — Control 30 — Zhang and C. Download references. Reprints and Permissions. Ben-Shahar, I. Dynamic service sharing with heterogeneous preferences. Queueing Systems 35, 83— Download citation. Issue Date : July Search SpringerLink Search. Abstract We consider a service system where individual users share a common resource, modeled as a processor-sharing queue.
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Dynamic service sharing with heterogeneous preferences
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Roch A. The problem involves identifying, for all hop counts, the optimal, i. The AHOP problem arises naturally in the context of quality-of-service QoS routing in networks, where routes paths need to be computed that provide services guarantees, e. Because service guarantees are typically provided through some form of resource allocation on the path links computed for a new request, the hop count, which captures the number of links over which resources are allocated, is a commonly used cost measure. As a result, a standard approach for determining the cheapest path available that meets a desired level of service guarantees is to compute a minimum hop shortest optimal path. Furthermore, for efficiency purposes, it is desirable to precompute such optimal minimum hop paths for all possible service requests. Providing this information gives rise to solving the AHOP problem.