Intelligent Job Allocation and Adaptive Migration in Cloud Environments using a Dynamic Dual-Threshold Strategy

doi:10.23940/ijpe.25.03.p6.168177

Abstract

Abstract: Cloud Computing Environment (CCE) plays a very important role in improving resource utilization in general and SLAs in particular, because while data resources may be slightly more cost-effective compared to traditional on-premise resources, there is a chance for cloud resource to lead to significant over-usage due to improper resource management in cloud systems. This paper proposes a new scheduling framework with two threshold load balancing policy for dynamic workload balancing on the Virtual Machines (VMs), which uses CPU utilization as a key indicator and defines the upper and lower thresholds of CPU utilization to identify overutilized and underutilized VMs. This is described by the dual threshold mechanism, which leads to migration of jobs to reduce the load of the VM, preventing overloading and ensuring the plane without occupation. To optimize the scheduling further, a wide-ranging job selection and migration algorithm is implemented that embraces CPU demand, RAM use, and migration expense as well. With this specific task, the focus shifts to how the focus can help in redistributing high-demand jobs to the underutilized VMs, thereby reducing power consumption and avoiding SLA violations. The algorithm also assesses the feasibility of migrations, meeting resource constraints and maintaining energy efficiency. The proposed framework is experimentally validated over varying job loads (10,000-50,000 tasks) and a Dynamic Workload (up-to 500 VMs), with results showcasing its prowess in handling multi-VM workloads in a highly dynamic environment. The results demonstrate a notable decrease in the general power consumption and SLA violations with respect to the non-migration cases. Specifically, power consumption reduced up to 16.67% during the high-demand hours and SLA violations reduced by 50%. It encourages sustainable cloud computing through intelligent load balancing, CPU optimization, and energy waste reduction. The dual threshold load balancing and job migration approach focus on the reliable, scalable, and energy-efficient cloud infrastructures. Future work will explore the extension of this framework to multi-dimensional resource metrics and real-time workload fluctuations in heterogeneous cloud environments.

Key words: cloud, task scheduling, load balancing, virtual machines, migration

Sonia Sharma and Rajendra Kumar Bharti. Intelligent Job Allocation and Adaptive Migration in Cloud Environments using a Dynamic Dual-Threshold Strategy [J]. Int J Performability Eng, 2025, 21(3): 168-177.

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