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Optimizing cloud task scheduling and VM placement with multi-objective constraints using hybrid lemurs and gannet optimization algorithm
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DOI：10.1007/s11042-026-21197-7.png)
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Abstract
En 中文
Resource management in cloud computing is critical since utilization of resources results in low costs and minimizing wastage of resources. Most traditional paradigms of cloud resource planning do not meet the requirements for managing assets actively and macroscopically, which results in some resilience. Cloud computing environment causes an increased pressure on the accurate planning and organization of tasks and resources, especially concerning the schedule of the tasks and the provision of Virtual Machines to process customer application jobs. This task scheduling problem is significant since it bears a straight relationship to the performance, cost, and resource utilization of the cloud. To overcome these hurdles, this paper presents the HL-GOA, which is a combination of a Meta-Heuristic Optimization Technique; the Lemurs-based Gannet Optimization Algorithm. This work focuses on the enhancement of scheduling of tasks and the arrangement of the virtual machine in the cloud computing context via a model that includes the multi-constraint objectives like cost, time, used resource, make span, and throughput. When evaluating our HL-GOA algorithm with two Virtual Machines, our scheduling time and VM placement time is 30.23%, 6.25%, and 11.76%, and 10.44% less than ESO, RSO, LO, and GOA algorithms respectively. The simulation results offer evidence of the model when working out task scheduling and VM placement problems, indicating the ability of enhancing the efficiency of clouds services.
Keywords:
Optimal task scheduling
VM placement
Hybrid lemurs-based gannet optimization algorithm
Multi objective constraints
Journal
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3
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1.9W
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