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<p>Cloud computing now has become a standard approach to scalable computing service provision and on-demand computing resource provision in distributed setting. Despite the advantages of efficiently allocating resources and managing the use of energy, large-scale cloud data centers still have a major concern related to the management of energy consumption. The inefficient resource allocation can lead to virtual machines not being provisioned or to an inefficient distribution of virtual machines, which leads to higher energy consumption, higher operating costs and underutilization. The purpose of this research is to present an Hybrid Optimization Resource Allocation Model (HORAM) that can make use of resources efficiently and save energy in cloud computing environments. The proposed framework combines Particle Swarm Optimization (PSO) with the Genetic Algorithms (GA) to create an intelligent scheduling mechanism that dynamically manages the allocation of computing resources based on the demand for workloads and the metrics of system performance. A safe and reliable data processing solution for virtualized cloud-based infrastructures in the security perspective via a task scheduling module. Using the framework, system parameters (such as CPU and memory usage and network bandwidth) are tracked in real-time and virtual machines are optimized and placed to consume the least amount of energy. To evaluate the performance of the proposed model, extensive simulation are done using CloudSim. The findings suggest that HORAM is far better at using resources; fewer tasks are completed, and the total power consumed is lower than with traditional scheduling algorithms. The hybrid optimization strategy promotes the stability and scalability of systems in highly dynamic cloud environments. The suggested architecture is a viable solution to sustainable cloud infrastructure management. It can support the growing need for energy-efficient computing services in current enterprise and scientific computing applications. Energy consumption 320 kWh, utilization 88%, execution time 45&#xa0;s, throughput 95 tasks per second, allocation accuracy 95%, stability deviation 7%, indicating improved efficiency and performance.</p>

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Energy-Efficient Hybrid Metaheuristic Resource Allocation Model for Dynamic Virtual Machine Scheduling in Cloud Computing Platforms

  • 期刊论文

  • 发表日期：

    2026年7月22日

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  • S. Balakrishnan,
  • Kalavagunta Aravind,
  • T. Veeramani,
  • Manikandan Moovendran,
  • T. Anuradha,
  • Manna Sheela Rani Chetty

摘要

Cloud computing now has become a standard approach to scalable computing service provision and on-demand computing resource provision in distributed setting. Despite the advantages of efficiently allocating resources and managing the use of energy, large-scale cloud data centers still have a major concern related to the management of energy consumption. The inefficient resource allocation can lead to virtual machines not being provisioned or to an inefficient distribution of virtual machines, which leads to higher energy consumption, higher operating costs and underutilization. The purpose of this research is to present an Hybrid Optimization Resource Allocation Model (HORAM) that can make use of resources efficiently and save energy in cloud computing environments. The proposed framework combines Particle Swarm Optimization (PSO) with the Genetic Algorithms (GA) to create an intelligent scheduling mechanism that dynamically manages the allocation of computing resources based on the demand for workloads and the metrics of system performance. A safe and reliable data processing solution for virtualized cloud-based infrastructures in the security perspective via a task scheduling module. Using the framework, system parameters (such as CPU and memory usage and network bandwidth) are tracked in real-time and virtual machines are optimized and placed to consume the least amount of energy. To evaluate the performance of the proposed model, extensive simulation are done using CloudSim. The findings suggest that HORAM is far better at using resources; fewer tasks are completed, and the total power consumed is lower than with traditional scheduling algorithms. The hybrid optimization strategy promotes the stability and scalability of systems in highly dynamic cloud environments. The suggested architecture is a viable solution to sustainable cloud infrastructure management. It can support the growing need for energy-efficient computing services in current enterprise and scientific computing applications. Energy consumption 320 kWh, utilization 88%, execution time 45 s, throughput 95 tasks per second, allocation accuracy 95%, stability deviation 7%, indicating improved efficiency and performance.