Distributed Scheduling Algorithm for Multiple Task Flows Based on Q-learning
Shiyong Zhang · Journal of Chinese Computer Systems · 2010
Recently real-time dynamic task allocation mechanisms draw more attention.Allocation of multiple task flows are considered in this paper,and a distributed scheduling algorithm based on Q-learning for multiple task flows is proposed.This algorithm can not only adapt to task flow on itself,but also take arrival and allocation of other task flows into account,thereby maximizing long-term expected reward of the whole system.Distributed property renders it applicable to open multi-agent systems with local visibility,while reinforcement learning makes allocation decisions adaptable to environment uncertainty.Experiments establish that the algorithm has higher task throughput,improving system efficiency.