A Distributed Scheduling Framework Based on Selfish Autonomous Agents for Federated Cloud Environments

Today, large-scale distributed Cloud computing infrastructures are often characterized by the fact that each participating organization, like a company in the free economy scenario, when making its resources available, will strive for reducing its own cost and for optimizing its own benefit, regardless of the consequences on the global Cloud’s welfare, in terms of effectiveness and performance losses. The selfish behavior of all these entities (end-users, Cloud providers, underlying enterprises/data centers), competing strategically for resources (distributed storage, processing power, bandwidth), motivates the use of game theory and autonomous agents for effective Multi-User Task Scheduling. Accordingly, we present a novel uncoordinated fully distributed scheduling scheme for federated cloud organizations, based on independent, competing, and self-interested job/task execution agents, driven by optimum social welfare criteria towards a Nash equilibrium solution. The agents’ behavior is also conditioned by marginal costs, to force some kind of implicit coordination between the (often conflicting) objectives of the various entities involved in the cloud. Due to its inherent parallel nature, such schema can provide a significantly better scalability in presence of a large number of tasks and resources involved into the scheduling system.