Jswa: An Improved Algorithm For Grid Workflow Scheduling Using Ant Colony Optimization

In this paper we propose an improved algorithm for scheduling grid workflow by using ant colony optimization method. Ant colony optimization (ACO) is a meta-heuristic for combinatorial optimization problems. JSWA algorithm is measured by using parameters such as reliability, cost, request and acknowledgement time and bandwidth. Regarding the proposed algorithm and its comparison with scheduling algorithm, we have established a new competency through which the tasks are carried out by considering preference criterion parameters. To do so, there should be less time complexities in accessing tasks for the present algorithms compared with the proposed one. By implementing a technical method we could consider a system in which the efficiency and optimization are increased and finally the time needed for program performance is decreased by using the target function. Also we could estimate the real time of tasks' commute by calculating the commute time compared with the previous algorithms. The result is that JSWA is more efficient than the algorithms such as ACS and MOACO.