Dynamic Adaptation of Replication Strategies for Reliable Agents

To make large-scale multi-agent systems reliable, we propose an adaptive application of replication strategies. Critical agents are replicated to avoid failures. As criticality of agents may evolve during the course of computation and problem solving, we need to dynamically and automatically adapt the number of replicas of agents, in order to maximize their reliability and availability based on available resources. We are studying an approach and mechanisms for evaluating the criticality of a given agent (based on application-level semantic information, e.g. messages intention, and also systemlevel statistical information, e.g., communication load) and for deciding what strategy to apply (e.g., active replication, passive) and how to parameterize it (e.g., number of replicas). In this paper, we first present the replication mechanism and the framework named DarX that we developed to replicate agents. We then describe a new model to evaluate dynamically the criticality of agents. Then we describe the implementation of this model with the DarX fault-tolerant framework.