An Automated Auction in Atm Network Bandwidth

Miller, Mark S.; Krieger, David J.; Hardy, Norman; Hibbert, Chris; Tribble, Eric Dean

doi:10.1142/9789814261371_0005

Cited by 19 publications

(4 citation statements)

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“…Also, the need fur autonomous ¢mfitie~ implemCmting the role of resource traders is empha~ec~ Wh~ this is more oriented to end system~ "the enhancements and the mechanlsmm d~n'i"bed are well suited fur use in ~w netwodm. Me~ket-based ~nM has been applied to other problems such a~ bandwidth allocatlon [12] , operating system memory allocation [8] and CPU eched-];ng [25] . Modeling rmourcee and the aer-vice~ they provide as good~ ~h~ axe can be bought and sold in a cmnputafienal eccmcmzy is attractive to the ~tlve network cas~ The control problem can be redefined as the pmhlen of pmEt or ufffity m~h~afion for each indlvldnal lgayer.…”

Section: 1mentioning

confidence: 99%

Application of computational intelligence techniques in active networks

Vasilakos

Anagnostakis

Pedrycz

2001

Proceedings of the 2001 ACM Symposium on Applied Computing

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Computa0onal inference techniques have been suc-c~'ully used for solving control problems in packet. ~wi~h~g netwurk archltectur~ The inta'oductlon of active netw,rking adds a high degree of flexib~ity in cus-tomi~ng the netw~k infr~t, ruCtm'e and intmdu~n~ new fun~aBty. Therof~e, there is a clear need for investigating both the e~pp]icabi].i~ of computational int~]figenee te~hnlques in this new networking envir~mment, as we]] as the provisions of active netw~uking technology that cmnputationa] int~-lB~ence techniques can exp]oRfor impro~d operation-We report on the characteristics of these technologks, their syn~gy and on outline recent e~rts in the dean of a cmnputational intelligence toolkit and its app]icatian to muting on a novel active networking envlranment.Keywords o)mputa,tional int~Tl';~ence, active nel, w¢=ks, market-based contm~ agent based mu/~ng

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Section: 1mentioning

confidence: 99%

Application of computational intelligence techniques in active networks

Vasilakos

Anagnostakis

Pedrycz

2001

Proceedings of the 2001 ACM Symposium on Applied Computing

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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%

“…One is an exchange based economy (Sandholm 1993), the other is a price based economy (Miller and Drexler 1988, Stonebraker et al 1994, Waldspurger et al 1992, and Wellman 1993. Neither model fits the 0-1 multiple knapsack problem, where an approach using auctions is needed (Miller et al 1996 andSteiglitz et al 1996).…”

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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“…Bandwidth auctionning through a multi-agent system is being explored [12]; however, this work is still at an early stage.…”

Section: Related Workmentioning

confidence: 99%

Abstraction and constraint satisfaction techniques for planning bandwidth allocation

Frei

Faltings

Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer A

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Communication networks are expected to offer a wide range of services to an increasingly large number of users, with a diverse range of quality of service. This calls for efficient control and management of these networks. We address the problem of quality-of-service routing, more specifically the planning of bandwidth allocation to communication demands. Shortest path routing is the traditional technique applied to this problem. However, this can lead to poor network utilization and even congestion. We show how an abstraction technique combined with systematic search algorithms and heuristics derived from Artificial Intelligence make it possible to solve this problem more efficiently and in much tighter networks, in terms of bandwidth usage.

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An Automated Auction in Atm Network Bandwidth

Cited by 19 publications

References 0 publications

Application of computational intelligence techniques in active networks

Application of computational intelligence techniques in active networks

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

Abstraction and constraint satisfaction techniques for planning bandwidth allocation

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