  We consider a general small-scale market for agent-to-agent resource sharing,
in which each agent could either be a server (seller) or a client (buyer) in
each time period. In every time period, a server has a certain amount of
resources that any client could consume, and randomly gets matched with a
client. Our target is to maximize the resource utilization in such an
agent-to-agent market, where the agents are strategic. During each transaction,
the server gets money and the client gets resources. Hence, trade ratio
maximization implies efficiency maximization of our system. We model the
proposed market system through a Mean Field Game approach and prove the
existence of the Mean Field Equilibrium, which can achieve an almost 100% trade
ratio. Finally, we carry out a simulation study motivated by an agent-to-agent
computing market, and a case study on a proposed photovoltaic market, and show
the designed market benefits both individuals and the system as a whole.
