Economic Models for Allocating Resources in Computer Systems

Ferguson, Donald F.; Nikolaou, Christos; Sairamesh, Jakka; Yemini, Yechiam

doi:10.1142/9789814261371_0007

Cited by 112 publications

(43 citation statements)

References 15 publications

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“…2 The advent of networks of computers did not alienate scheduling from economics, and scheduling for distributed computer systems has also been recognized as an activity under resource management [2,12]. Markets for computing resources have been embraced by many as the key components in the operation of distributed systems [1,3,4,5,10,11,14,15,18,19,20,21,22,23]. …”

Section: Scheduling and Economicsmentioning

confidence: 99%

Markets as Global Scheduling Mechanisms: The Current State

Nakai

2002

Lecture Notes in Computer Science

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Abstract. Viewing the computers as "suppliers" and the users as "consumers" of computing services, markets for computing services/resources have been examined as one of the most promising mechanisms for global scheduling. We first establish how economics can contribute to scheduling. We further define the criterion for a scheme to qualify as an application of economics. Many studies to date have claimed to have applied economics to scheduling. If their scheduling mechanisms do not utilize economics, contrary to their claims, their favorable results do not contribute to the assertion that markets provide the best framework for global scheduling. For any of the schemes examined, we could not reach the conclusion that it makes full use of economics.

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Section: Scheduling and Economicsmentioning

confidence: 99%

Markets as Global Scheduling Mechanisms: The Current State

Nakai

2002

Lecture Notes in Computer Science

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“…Market-oriented algorithms have been applied successfully towards solving complex resource allocation problems such as distributed multi-commodity flow in the trucking industry (Wellman 1993(Wellman , 1996, query processing and data migration in a distributed database (Stonebraker et al 1994), task scheduling in a distributed operating system (Huberman 1995, Malone et al 1988, Miller and Drexler 1988, network and file system resource allocation (Ferguson et al 1988, Gagliano and Mitchem 1996, Kurose and Simha 1989, Kuwabara et al 1996, Miller et al 1996, and Yemini 1981, allocating tradeable pollution permits (Marron and Bartels 1996), task allocation in discrete manufacturing systems (Baker 1996, Tilley 1996, and load balancing in distributed systems (Ferguson et al 1996). Most of the current research has focused on developing one of two types of economic models.…”

Section: Market Applicationsmentioning

confidence: 99%

Approximating the 0–1 Multiple Knapsack Problem with Agent Decomposition and Market Negotiation

Smolinski

2000

Intelligent Problem Solving. Methodologies and Approaches

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The 0-1 multiple knapsack problem appears in many domains from financial portfolio management to cargo ship stowing. Methods for solving it range from approximate algorithms, such as greedy algorithms, to exact algorithms, such as branch and bound. Approximate algorithms have no bounds on how poorly they perform and exact algorithms can suffer from exponential time and space complexities with large data sets. This paper introduces a market model based on agent decomposition and market auctions for approximating the 0-1 multiple knapsack problem, and an algorithm that implements the model (M(x)). M(x) traverses the solution space rather than getting caught in a local maximum, overcoming an inherent problem of many greedy algorithms. The use of agents ensures that infeasible solutions are not considered while traversing the solution space and that traversal of the solution space is not just random, but is also directed. M(x) is compared to a bound and bound algorithm (BB) and a simple greedy algorithm with a random shuffle (G(x)). The results suggest that M(x) is a good algorithm for approximating the 0-1 Multiple Knapsack problem. M(x) almost always found solutions that were close to optimal in a fraction of the time it took BB to run and with much less memory on large test data sets. M(x) usually performed better than G(x) on hard problems with correlated data.

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“…Microeconomics can serve as an efficient mechanism for resource management, optimal allocations, and revenue generation [2][3][4][5][6][7][8]. An excellent description of the issues related to pricing and managing QoS-enabled networks in a retail market is provided in [2].…”

Section: Introductionmentioning

confidence: 99%