New architectures and algorithms are needed to reflect the mixture of localand global information that is available as multi-agent systems connect overthe cloud. We present a novel architecture for multi-agent coordination wherethe cloud is assumed to be able to gather information from all agents, performcentralized computations, and disseminate the results in an intermittentmanner. This architecture is used to solve a multi-agent optimization problemin which each agent has a local objective function unknown to the other agentsand in which the agents are collectively subject to global inequalityconstraints. Leveraging the cloud, a dual problem is formulated and solved byfinding a saddle point of the associated Lagrangian.
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