Author:

Keywords:

parallel processing, adaptive load balancing, reinforcement learning, heterogeneous network, intelligent agents, data intensive applications, Science & Technology, Technology, Computer Science, Theory & Methods, Computer Science, Adaptive load balancing

Abstract:

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 interconnection 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.