Adaptive Load Balancing of Parallel Applications with Reinforcement Learning on Heterogeneous Networks

We report on the improvements that can be achieved by applying machine learning techniques, in particular reinforcement learning, for the dynamic load balancing of parallel applications. The applications being considered here are coarse grain data intensive applications. Such applications put high pressure on the interconnect of the hardware. Synchronization and load balancing in complex, heterogeneous networks need fast, flexible, adaptive load balancing algorithms. Using reinforcement learning it is possible to improve upon the classic job farming approach.