Task scheduling using glowworm-based optimal heterogeneous earliest finish time algorithm for mobile grid

Mobile grids include features that support mobile users and mobile resources, including limited energy, unstable network connections, etc. Grid computing is primarily concerned with aggregating the power of widely distributed resources of diverse nature in order to provide users with useful services. Due to the composition of the mobile resource, the reliability of the resource is greatly reduced, which has an adverse effect on grid scheduling. In order to schedule tasks, researchers have presented a number of traditional scheduling algorithms. Among them, the heterogeneous earliest finish time (HEFT) scheduling algorithm produces the best results. However, it is necessary to reduce the communication overhead associated with this algorithm. As a result, this paper presents the Cluster-based Optimized HEFT algorithm (COHEFT). Based on the proposed algorithm, tasks and resources in the grid are grouped into clusters. In addition, the clustered tasks are scheduled using the Glowworm swarm optimization (GSO) algorithm. The results of this study show that the C-GSHEFT algorithm is compared with the HEFT, Min-Min and Max–Min algorithms in terms of makepan, performance, execution time and energy consumption, and it is demonstrated that C-GSHEFT achieves the best results with a completion rate of 98%.