Adaptive Resource-Aware Virtual Machine Placement in Cloud Computing for Optimizing Performance and Cost Efficiency

Sarvesh Kumar; Anubha Jain; Astha Pareek

doi:10.57159/jcmm.5.3.261068

Authors

Sarvesh Kumar Department of Computer Science and IT, IIS (Deemed to Be University), Jaipur, Rajasthan, India https://orcid.org/0000-0001-9459-1312
Anubha Jain Department of Computer Science and IT, IIS (Deemed to Be University), Jaipur, Rajasthan, India https://orcid.org/0000-0002-5785-1266
Astha Pareek Department of Computer Science and IT, IIS (Deemed to Be University), Jaipur, Rajasthan, India https://orcid.org/0009-0007-5549-5476

DOI:

https://doi.org/10.57159/jcmm.5.3.261068

Keywords:

Virtual Machine Placement, Adaptive Scheduling, Load Balancing, Resource Utilization, Cost Optimization, Cloud Infrastructure Management

Abstract

Cloud computing environments increasingly rely on efficient virtual machine (VM) placement strategies to optimize resource utilization, reduce operational costs, and maintain Quality of Service (QoS) requirements. Existing VM placement approaches often struggle to adapt to dynamic workload variations, heterogeneous resource demands, and fluctuating infrastructure conditions, leading to inefficient resource allocation, increased energy consumption, and higher Service Level Agreement (SLA) violations. This study addresses these limitations by proposing an adaptive resource-aware VM placement framework designed to enhance performance and cost efficiency in cloud data centers. The proposed framework integrates resource consumption analysis, workload-aware scheduling, adaptive load balancing, and service placement optimization within a unified cloud management model. The system continuously monitors infrastructure utilization metrics, including processor usage, memory allocation, storage demand, and network load, to dynamically determine optimal VM placement decisions. Experimental evaluation was conducted using a cloud simulation environment under varying workload conditions and heterogeneous resource configurations. Performance was assessed using resource utilization, SLA violation rate, execution time, load balancing efficiency, and operational cost. Simulation results demonstrate that the proposed model significantly outperforms conventional VM placement techniques, achieving improved resource utilization efficiency, reduced SLA violations, enhanced load balancing performance, and lower operational costs under dynamic workloads. The study concludes that adaptive resource-aware VM placement can substantially improve cloud infrastructure management by enabling intelligent and flexible resource allocation, offering broader implications for developing cost-efficient, scalable, and energy-aware cloud computing systems.

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