Heuristic Task Scheduling Algorithm Based on Rational Ant Colony Optimization

Distributed data stream processing system is NP-complete problem to assign tasks to any number of nodes handling the task scheduling. Even for substantially reducing scheduling scale, the problem still cannot be avoided. This paper takes advantage of the classical algorithm (ant colony optimization) of heuristic methods to simulate the global task scheduling problem of distributed system. Rational improvement on ant colony optimization path-finding for the memory and CPU usage of each node achieves load balancing in a short time. It gives the suboptimal solution of the global task scheduling. The experiments show that the data stream processing system we proposed has good real-time characteristics and stability.