NEW APPROACH TO MULTI-LEVEL PROCESSOR SCHEDULING

摘要

The problem of finding the best quantum time in multi-level processor scheduling is addressed in this paper. Processor scheduling is one of the most important issues in operating systems design. Different schedulers are introduced to solve this problem. In one scheduling approach, processes are placed in different queues according to their properties, and the processor allocates time to each queue iteratively. One of the most important parameters of a processor's efficiency in this approach is the amount of time slices associated to each processor queue. In this paper, an ant colony optimization (ACO) algorithm is presented to solve the problem of finding appropriate time slices to assign to each processor queue. In this technique, each ant tries to find an appropriate scheduling. Ant algorithm searches the problem space to find the best scheduling. The quality of each ant's solution is evaluated using a new fitness function. This fitness function is designed according to the evaluation parameters of each processor queue and also according to the queue theory's relations. Also a heuristic function is presented which prompts ant to select better solutions. Computational tests are presented and the comparisons made with genetic algorithm (GA) and particle swarm optimization (PSO) algorithms which try to solve same problem. The results show the efficiency of this algorithm.