Embedded cost model in mobile agents for large scale query optimization

Execution plans generated by traditional optimizers for large scale distributed queries can be sub-optimal because: the estimations are inaccurate, the data are unavailable and the execution environment is unstable. To deal with the sub-optimality, we propose to execute each relational operator of an execution plan by a mobile agent. A mobile agent can change its execution site in order to correct the sub-optimality. In this paper, we address the problem of choosing an execution site for a mobile agent among execution sites of the considered system. For this, we propose to integrate a cost model into the mobile agents. We define also the various interactions that might occur between a mobile agent and its execution site. The performance evaluation shows that the cost model embedded in the agent allows to choose the most appropriate execution site