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<p>In this research paper, a new Fuzzy Analytic Hierarchy Process (FAHP) based Budget Optimized Virtual Machine Provisioning (BOPVM) framework is proposed to solve the problem of which CSP (cloud service providers) should be chosen to orchestrate resource provisioning in a hybrid cloud. The framework is divided into two phases. In Phase 1, the ratings of the CSPs are established depending on several parameters including the application workload and environment, the desired QoS parameters, and the cost considerations by applying the FAHP extent analysis method. In Phase 2, the BOPVM algorithm is used for resource allocation given pre-defined budgets, by proactively predicting the workload and identifying the optimal CSP and VM (Virtual Machine) combination. The decision criteria discussed include the performance of the identified CSPs and vendor scale, reliability, and cost optimization for evaluating CSPs about the proposed framework. It guarantees resource provisioning and scaling for varied application zones, production and non-production. The proposed methodology performs better than existing methods such as PRMF and DRPM by cutting virtual machine provisioning costs by up to 61.1% and time by up to 80%. Based on the above performance evaluation, the proposed BOPVM framework achieves the best result in terms of workload forecasts and the distribution of future workloads across the multiple CSPs and budget constraints. This novelty suggests its application can revolutionize hybrid cloud resources’ efficient allocation by prioritizing its effective and cost-efficient usage.</p>

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Intelligent Reasonable Optimization for Virtual Machine Provisioning in Hybrid Cloud Using Fuzzy AHP and Cost-Effective Autoscaling

  • 期刊论文

  • 发表日期：

    2025年8月19日

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  • Kiran Sree Pokkuluri,
  • Paramita Sarkar,
  • Vijay Birchha,
  • Sandeep Kumar Mathariya,
  • Vinod Veeramachaneni,
  • Suman Singh,
  • Vandana Roy

摘要

In this research paper, a new Fuzzy Analytic Hierarchy Process (FAHP) based Budget Optimized Virtual Machine Provisioning (BOPVM) framework is proposed to solve the problem of which CSP (cloud service providers) should be chosen to orchestrate resource provisioning in a hybrid cloud. The framework is divided into two phases. In Phase 1, the ratings of the CSPs are established depending on several parameters including the application workload and environment, the desired QoS parameters, and the cost considerations by applying the FAHP extent analysis method. In Phase 2, the BOPVM algorithm is used for resource allocation given pre-defined budgets, by proactively predicting the workload and identifying the optimal CSP and VM (Virtual Machine) combination. The decision criteria discussed include the performance of the identified CSPs and vendor scale, reliability, and cost optimization for evaluating CSPs about the proposed framework. It guarantees resource provisioning and scaling for varied application zones, production and non-production. The proposed methodology performs better than existing methods such as PRMF and DRPM by cutting virtual machine provisioning costs by up to 61.1% and time by up to 80%. Based on the above performance evaluation, the proposed BOPVM framework achieves the best result in terms of workload forecasts and the distribution of future workloads across the multiple CSPs and budget constraints. This novelty suggests its application can revolutionize hybrid cloud resources’ efficient allocation by prioritizing its effective and cost-efficient usage.