Optimized parallelization heuristic for task scheduling
Waheed Aslam Ghumman, Pedro López-García · Computational intelligence · 2010
Distributed and multiprocessor computing is a a basic need for increasingly complex computational requirements. Scheduling the tasks efficiently is as important in multiprocessor systems as getting the correct results. In this paper, we present a heuristic algorithm for optimized parallelization of tasks in multiprocessor environments. This heuristic can be applied in such multiprocessor environments where task and resource information is completely know at time of scheduling or incomplete task information is known at scheduling time. For the case of incomplete task information, we have given a method of using static cost analysis to get different sets of parallel tasks and one set among these alternatives is chosen in runtime based on the value of input parameter. We have verified our approach using a prolog (CLP) program for random sets of tasks and resource with 100% positive results. Our heuristic algorithm is quite simple yet effective which makes it diiferent than already existing scheduling algorithms, heuristics and genetic algorithms.