Simulation study demonstrates improved throughput and reduced makespan in cloud computing task scheduling, indicating higher resource efficiency and lower latency.
Task scheduling intends to map the user-submitted tasks to the available virtual machines (VMs) based on their capacity. However, the prevailing techniques fail to perform the run-time mapping between the forthcoming workload and cloud resources. This research develops the Lapin Hunt algorithm-optimized multi-parameter-enabled task scheduler (LH-MTS) to perform energy-efficient task scheduling. The task scheduling is managed using the multi-parameter task allocation controller, where the LH-MTS algorithm schedules the tasks based on the VM parameters. The scheduler uses the multi-objective parameters in the cloud to establish the maximum throughput, resource utilization, and reliability, while reducing the makespan and delay. The results reveal that the proposed LH-MTS approach attained a high throughput of 99.98 bps, showing an improvement of 7.55% and 8.25% over the existing task scheduling with multi-objective grey wolf optimizer (TSMGWO) and fruitfly hybridized cuckoo search (FHCS) algorithms. In addition, the proposed LH-MTS approach attained the makespan of 586 s, displaying a significant reduction of 46.44 s compared to TSMGWO, and 155.91 s compared to the FHCS algorithm.
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Basha et al. (2026) studied this question.
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