A Performance Study of GA and LSH in Multiprocessor Job Scheduling

Multiprocessor task scheduling is an important and computationally difficult problem. This paper proposes a comparison study of genetic algorithm and list scheduling algorithm. Both algorithms are naturally parallelizable but have heavy data dependencies. Based on experimental results, this paper presents a detailed analysis of the scalability, advantages and disadvantages of each algorithm. Multiprocessors have emerged as a powerful computing means for running real-time applications, especially where a uni-processor system would not be sufficient enough to execute all the tasks. The high performance and reliability of multiprocessors have made them a powerful computing resource. Such computing environment requires an efficient algorithm to determine when and on which processor a given task should execute. In multiprocessor systems, an efficient scheduling of a parallel program onto the processors that minimizes the entire execution time is vital for achieving a high performance. This scheduling problem is known to be NPHard. In multiprocessor scheduling problem, a given program is to be scheduled in a given multiprocessor system such that the program’s execution time is minimized. The last job must be completed as early as possible. Genetic algorithm (GA) is one of the widely used techniques for constrained optimization. Genetic algorithms are basically search algorithms based on the mechanics of natural selection and natural genetics. List scheduling techniques assign a priority to each task to be scheduled then sort the list of tasks in decreasing priority. As processors become available, the highest priority task in the task list is assigned to be processed and removed from the list. If more than one task has the same priority, selection from among the candidate tasks is typically random. This paper compares Genetic algorithm (GA) with List Scheduling heuristic (LSH) to solve scheduling problem of multiprocessors.