A Value Ordering Heuristic for Local Search in Distributed Resource Allocation

Abstract

In this paper we develop a localized value-ordering heuristic for distributed resource allocation problems. We show how this value ordering heuristics can be used to achieve desirable properties (increased effectiveness, or better allocations). The specific distributed resource allocation problem that we consider is sensor allocation in sensor networks, and the algorithmic skeleton that we use to experiment this heuristic is the distributed breakout algorithm. We compare this technique with the standard DBA and with another value-ordering heuristic [10] and see from the experimental results that it significantly outperforms both of them in terms of the number of cycles required to solve the problem (and therefore improvements in terms of communication and time requirements), especially when the problems are difficult. The resulting algorithm is also able to solve a higher percentage of the test problems. We show that a simple variation of this technique exhibits an interesting competition behavior that could be used to achieve higher quality allocations of the resource pool. Moreover, combinations of the two methods are possible, leading to interesting results. Finally, we note that this heuristic is domain, but not algorithm specific (meaning that it could most likely give good results in conjunction with other DisCSP algorithms as well). Content Areas: constraint satisfaction, distributed AI, problem solving