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Hybrid clouds are increasingly used to outsource non-critical applications to public clouds. However, the main challenge within such environments, is to ensure a cost-efficient distribution of the systems between the resources that are on/off premises. For Multi Agent Systems (MAS), this challenge is deepened due to irregular workload progress and intensive communication between the agents, which may result in high computing and data transfer costs. Thus, in this paper we propose a generic framework for adaptive cost-efficient deployment of MAS with a special focus on hybrid clouds. The framework is based mainly on the use of a performance evaluation process that consists of simulating various partitioning options to estimate and optimize the overall deployment costs. Further, to cope with the irregular workload changes within a MAS and dynamically adapt its initial deployment, we propose an extended version of the Fiduccia-Mattheyses algorithm (E-FM). The experimental results highlight the efficiency of E-FM and show that an efficient MAS deployment to hybrid clouds depends on various factors such as the cloud providers and their different cost-models, the network state, the used partitioning algorithm, and the initial deployment.

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Scalability is a key issue for Multi-Agent Systems (MAS) that aim to model and simulate complex systems. Distributed infrastructures such as clusters, grids and clouds are powerful computational environments that can be effectively used to run large-scale agent-based simulations. To properly distribute an agent-based system and ensure its performance, an appropriate partitioning approach is required. Although multiple partition methods for distributed MAS exist, they remain specific to the individual requirements of a given application domain. There is no generic approach for guiding the designers and developers to select an appropriate approach for partitioning a given agent-based system. Thus a recurrent challenging task, for MAS designers and developers, is how to evaluate, select and then apply the appropriate partitioning mechanism for a given MAS. Therefore, in this paper, we present a generic conceptual framework useful to analyze existing partitioning methods. It can also be used as a basis while designing a distributed architecture of new MAS.