Optimal decentralized flow control of Markovian queueing networks with multiple controllers

Hsiao, M.-T.T.; Lazar, Aurel A.

doi:10.1016/0166-5316(91)90054-7

Cited by 69 publications

(32 citation statements)

References 19 publications

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“…Modeling telecommunication network problems as dynamic games has produced Nash equilibria solutions in many settings such as capacity allocation in routing [11], congestion control in product form networks [12], flow control in Markovian queuing networks [8], and combined routing and flow control [2]. Incorporating electronic market concepts such as pricing congestible network resources has been shown to encourage efficiency [15].…”

Section: Related Workmentioning

confidence: 99%

A Game-Theoretic Formulation of Multi-Agent Resource Allocation

Bredin¹,

Maheswaran²,

Imer³

et al. 2005

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This paper considers resource allocation in a network with mobile agents competing for computational priority. We formulate this problem as a multi-agent game with the players being agents purchasing service from a common server. We show that there exists a computable Nash equilibrium when agents have perfect information into the future. From our game, we build a market-based CPU allocation policy and a strategy with which an agent may plan its expenditures for a multi-hop itinerary. We simulate a network of hosts and agents using our strategy to show that our resourceallocation mechanism effectively prioritizes agents according to their endowments and that our planning algorithm handles network delay gracefully.

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Section: Related Workmentioning

confidence: 99%

A Game-Theoretic Formulation of Multi-Agent Resource Allocation

Bredin¹,

Maheswaran²,

Imer³

et al. 2005

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“…See, e.g., Li and Başar (1987), Hsiao andLazar (1991), Lakshmivarahan (1981), and Fudenberg and Levine (1998).…”

Section: Learning and Equilibriummentioning

confidence: 99%

“…Game theoretical analysis has been applied to other queueing control problemse.g., Kulkarni (1983), Lee and Cohen (1985), Bovopoulos and Lazar (1987), Shenker (1990), Hsiao and Lazar (1991), Altman (1992), Altman and Koole (1992), Altman and Hordijk (1995), and Shimkin and Shwartz (1993)where the last five consider dynamic problems. Finally, results related to our work have been obtained in Assaf and Haviv (1990), Haviv (1991), and Xu and Shantikumar (1993).…”

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confidence: 99%

Individual Equilibrium and Learning in Processor Sharing Systems

Inria

Shimkin

1998

Operations Research

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We consider a processor-sharing service system, where the service rate to individual customers decreases as the load increases. Each arriving customer may observe the current load and should then choose whether to join the shared system. The alternative is a constant-cost option, modeled here for concreteness as a private server (e.g., a personal computer that serves as an alternative to a central mainframe computer). The customers wish to minimize their individual service times (or an increasing function thereof). However, the optimal choice for each customer depends on the decisions of subsequent ones, through their effect on the future load in the shared server. This decision problem is analyzed as a noncooperative dynamic game among the customers. We first show that any Nash equilibrium point consists of threshold decision rules and establish the existence and uniqueness of a symmetric equilibrium point. Computation of the equilibrium threshold is demonstrated for the case of Poisson arrivals, and some of its properties are delineated. We next consider a reasonable dynamic learning scheme, which converges to the symmetric Nash equilibrium point. In this model customers simply choose the better option based on available performance history. Convergence of this scheme is illustrated here via a simulation example and is established analytically in subsequent work.

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“…In wireless networks, users may have power control. The concept of a Nash equilibrium is also discussed in flow control [23,24,25,26,27,28] andin power control [29,30,31,32,33], both of which are not addressed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Bounds on Benefits and Harms of Adding Connections to Noncooperative Networks

Kameda

2004

Lecture Notes in Computer Science

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Abstract. In computer networks (and, say, transportation networks) , we can consider the situation where each user has its own routing decision so as to minimize noncooperatively the expected passage time of its packetfjob given the routing decisions of other users. Intuitively, it is anticipated that adding connections to such a noncooperative network may bring benefits at least to some users. The Braess paradox is, however, the first example of paradoxical cases where it is not always the case. This paper studies the bounds on the degrees of coincident cost improvement (benefits) and degradation (harms) for all users by adding connections to noncooperative networks. For Wardrop networks (noncooperative networks with infinitesimal users), the degree of benefits for all users can increase without bound by adding connections whereas no Wardrop network has been found for which the degree of harms can increase without bound for all users. In contrast, for Nash networks (noncooperative networks with a finite nurober of users), the degrees of both benefits and harms can increase without bound for all users. On the other hand , we see that, for some category of Wardrop networks, adding connections to them can bring neither benefits nor harms to all users, and that, for some homogeneaus networks, adding connections to them can never bring benefits to all users under any static policy including cooperative and noncooperative ones.

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Optimal decentralized flow control of Markovian queueing networks with multiple controllers

Cited by 69 publications

References 19 publications

A Game-Theoretic Formulation of Multi-Agent Resource Allocation

A Game-Theoretic Formulation of Multi-Agent Resource Allocation

Individual Equilibrium and Learning in Processor Sharing Systems

Bounds on Benefits and Harms of Adding Connections to Noncooperative Networks

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