An Approximate Algorithm for Resource Allocation Using Combinatorial Auctions

Combinatorial Auctions (CAs), where users bid on combination of items, have emerged as a useful tool for resource allocation in distributed systems. However, two main difficulties exist to the adoption of CAs in time-constrained environments. The first difficulty involves the computational complexit...

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Bibliographic Details
Published in:2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology : proceedings : 18-22 December, 2006, Hong Kong, China pp. 571 - 578
Main Authors: Avasarala, Viswanath, Polavarapu, Himanshu, Mullen, Tracy
Format: Conference Proceeding
Language:English
Published: Washington, DC, USA IEEE Computer Society 18.12.2006
IEEE
Series:ACM Conferences
Subjects:
Applied computing > Operations research > Decision analysis
Bandwidth
Computational complexity
Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Intelligent agents
Computing methodologies > Artificial intelligence > Search methodologies > Heuristic function construction
Content addressable storage
Cost accounting
Information systems > Information systems applications > Decision support systems
Intelligent sensors
Linear programming
Mathematics of computing > Discrete mathematics > Combinatorics > Combinatorial algorithms
Mathematics of computing > Mathematical analysis > Mathematical optimization > Mixed discrete-continuous optimization > Integer programming
Resource management
Stochastic processes
Supply chain management
Target tracking
Theory of computation > Design and analysis of algorithms > Mathematical optimization > Mixed discrete-continuous optimization > Integer programming
ISBN:9780769527482, 0769527485
Online Access:Get full text
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Summary:Combinatorial Auctions (CAs), where users bid on combination of items, have emerged as a useful tool for resource allocation in distributed systems. However, two main difficulties exist to the adoption of CAs in time-constrained environments. The first difficulty involves the computational complexity of winner determination. The second difficulty entails the computational complexity of eliciting utility valuations for all possible combinations of resources to different tasks. To address both issues, we developed a new algorithm, Seeded Genetic Algorithm (SGA) for finding high quality solutions quickly. SGA uses a novel representational schema that produces only feasible solutions. We compare the winner determination performance of our algorithm with Casanova, another local stochastic search procedure, on typically hard-to-solve bid distributions. We show that SGA converges to a better solution than Casanova for large problem sizes. However, for many bid distributions, exact winner determination using integer programming approaches is very fast, even for large problem sizes. In these cases, SGA can still provide significant time savings by eliminating the requirement for formulating all possible bids.
ISBN:9780769527482
0769527485
DOI:10.1109/IAT.2006.33