Heuristic-Based Task Scheduling in Grid Computing: A Scalable Framework for Load Balancing, Resource Utilization, and Execution Time Reduction

Abstract

Grid computing actively changed the sphere of large-scale distributed processing, allowing the subsequent employment of geographically distributed and heterogeneous resources. However, efficient scheduling of tasks in such environments is not a simple feat as it has to address issues such as dynamic workloads, heterogeneity of resources as well as scalability. This research work adopts a heuristic-based task scheduling framework which combines GA, ACO, and PSO to achieve maximum performance in terms of less makespan, improved resource utilization, load balance, and energy consumption. The framework was implemented and evaluated using the GridSim simulation tool under different tasks of load varying from 100 up to 500. A comparative analysis showed that, in general, heuristic schedulers, especially PSO, were more effective than FCFS in all the tested criteria. Other methods showed higher makespan while still producing an evenly distributed load, PSO provided the shortest make span, the fastest resource utilization and a very low average wait and response time. Therefore, the research adds to the existing literature about intelligent adaptive scheduling technologies in grids through proposing a scalable solution fitting both demands for execution efficiency and economy. These results provide insights on the impacts of heuristic optimization on the developments of the grid computing to be more responsive, energy efficient and high throughput.