A Load Distribution Based Resource Allocation Strategy for Bag of Tasks (BoT) in Computational Grid Environment

In the ever-evolving landscape of computational grid systems, the meticulous selection of resources tailored to specific tasks is a formidable challenge. This paper introduces an efficient load distribution strategy known as Load Distribution Based Resource Allocation (LDRA), one of the foremost goals is to allocate resources to gain enhanced resource utilization and also try to achieve least possible execution time to fulfil the need for grid systems. A comprehensive performance evaluation unfolds to elevate grid efficiency, pitting LDRA against existing heuristics using the ETC Simulation Benchmark. The study expands further on the real-world dataset from the Gaia Cluster Configurations (https://​hpc.​uni.​lu/​systems/​gaia/​) to verify its significance in the real environment. The LDRA algorithm emerges with superior performance when compared to state-of-the-art such as Max–Min, Opportunistic Load Balancing, AlgHybrid_LB, and Resource Aware Load Balancing for resource utilization, makespan, flowtime, and energy efficiency in the majority of the cases in experimental evaluation. In some cases, the experimental results show that LDRA’s usage of the grid resources is remarkable, reaching over 99% in four cases and approaching 98% in two cases of the ETC simulation benchmark. These accomplishments are further mirrored in the evaluation against real datasets, where LDRA’s performance among peers is nothing short of exemplary in the cases under study.