In this article, optimization of decision support queries is considered in the context of widearea distributed databases. An original approach based on the "mobile agent" paradigm is proposed and evaluated. Agents' autonomy and reactivity allow operators of the execution plan to adapt dynamically to estimation errors on relations and to evolutions in the state of the execution system, avoiding time overheads commonly associated with centralized monitoring. We present decentralized self-adaptive algorithms for dynamic optimization of join operators, and their implementations in Java using mobile agents. Then, we evaluate performance depending on error rate on statistical information on database, and on communication bandwidth and CPU frequency. The results show that the agent-based approach can lead to a significant reduction of response time and provide decision criteria for developing an effective migration policy.

Abstract. This paper deals with resource discovery in data Grid environments while taking into account the semantic heterogeneity of data sources. A domain ontology is associated to each domain and mappings exist between these domain ontologies. Our resource discovery method is adapted to any mapping graph topology between domain ontologies provided that the graph is connected. The proposed addressing system allows permanent access from any domain ontology to another despite the dynamicity of nodes. The performance evaluation of the proposed method proved the discovery cost reduction especially for inter-ontology queries. They also provide a significant maintenance costs reduction of such system especially when peers frequently join / leave the system.

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