Non-cooperative scheduling of multiple bag-of-task applications

Multiple applications that execute concurrently on heterogeneous platforms compete for CPU and network resources. In this paper we analyze the behavior of $K$ non-cooperative schedulers using the optimal strategy that maximize their efficiency. Meanwhile fairness is ensured at a system level ignoring applications characteristics. We limit our study to simple single-level master-worker platforms and the case where applications consist of a large number of independent tasks. The tasks of a given application all have the same computation and communication requirements, but these requirements can vary from one application to another. Therefore, each scheduler aims at maximizing its throughput. We give closed-form formula of the equilibrium reached by such a system and study its performances. We characterize the situations where this Nash equilibrium is Pareto-optimal and show that even though no catastrophic situation (Braess-like paradox) can occur, such an equilibrium can be arbitrarily bad for any classical performance measure