  New architectures and algorithms are needed to reflect the mixture of local
and global information that is available as multi-agent systems connect over
the cloud. We present a novel architecture for multi-agent coordination where
the cloud is assumed to be able to gather information from all agents, perform
centralized computations, and disseminate the results in an intermittent
manner. This architecture is used to solve a multi-agent optimization problem
in which each agent has a local objective function unknown to the other agents
and in which the agents are collectively subject to global inequality
constraints. Leveraging the cloud, a dual problem is formulated and solved by
finding a saddle point of the associated Lagrangian.
