Production Scheduling on Heterogeneous Computing Environment Using Modified GRASP

Abstract

Abstract Heterogeneous computing environment refers to the use of multiple computing Sockets with different capabilities or characteristics in a parallel computing system. The production of task scheduling is one of the key issues with heterogeneous computing systems. This production of task scheduling problem desires to map tasks to heterogeneous machines in a way that will optimize the system's overall performance, such as minimization the schedule length of execution time. Because the task scheduling problem is NP-hard, intelligent algorithms are used to solve it, allowing us to achieve at a somewhat optimal result. To handle task scheduling in heterogeneous computing systems, this work adopted two algorithms one of them is a Greedy Randomized-based Simulated Annealing algorithm and the other is a GRASP-based Tabu Search algorithm. Additionally, greedy initial solutions with relatively optimized have taken the place of the random starting population. To enhance the capabilities of the Simulated Annealing or Tabu search Algorithm, the random initial solution has also been replaced by greedy initial solution with relatively optimal solutions. Results from testing the proposed approach on random graphs and graphs from real-world applications in heterogeneous computing systems with a variety of features showed that GRASP based Tabu Search was significantly more efficient than GRASP based Simulated annealing and the two algorithms more efficient than previous scheduling algorithms.