Distributed Computing Jobs Scheduling Improvement Using Simulated Annealing Optimizer

Over the past decade, scheduling in distributed computing system has been an active research. However, it is still difficult to find an optimal scheduling algorithm to achieve load balancing for a specific scientific application which is executed in an unpredictable environment. This is due to the complex nature of the application which changes during runtime and due to the dynamic nature and unpredictability of the computational environment. This paper addresses these issues by presenting a simulated annealing (SA) approach as an optimizer which is an improved version of EG-EDF with tabu search optimizer. Instead of using tabu search, this work used SA to optimize the scheduling algorithm. The scheduling algorithms have been evaluated using three main criteria; number of delayed jobs, makespan time and total tardiness. Our results show the improvements to the main criteria mentioned.