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<p>In the rapidly evolving landscape of cloud computing, the efficient allocation of resources is paramount for upholding Quality of Service (QoS) standards and optimizing overall system performance. This paper introduces a groundbreaking Proactive Resource Allocation Framework that leverages Support Vector Machine (SVM) regression models to address challenges related to accurate QoS prediction, dynamic resource scaling, and effective decision-making in cloud environments. The framework utilizes SVM regression to construct prediction models based on viewer proximity, incorporating geographic data to optimize resource distribution across geo-distributed cloud locations while considering user proximity. By anticipating resource needs based on user locations, the framework implements a proactive allocation strategy, significantly enhancing overall system responsiveness. The study introduces an SVM regression model for server load prediction, facilitating the implementation of efficient auto-scaling mechanisms for dynamic resource provisioning in response to changing workloads. The utilization of SVM regression not only enhances load forecasts with precision but also streamlines auto-scaling, mitigating resource overprovisioning and underutilization. This research addresses intricate challenges associated with accurate QoS predictions, emphasizing the necessity of aligning resource allocation with the evolving demands of cloud applications. The proposed system, through the integration of machine learning and SVM regression, offers a comprehensive solution to enhance resource allocation techniques in cloud environments. This approach empowers the dynamic scaling of resources, ensuring efficient decision-making processes that prioritize operational effectiveness and user experience. The findings of this study are expected to have significant implications for cloud service providers, enabling them to elevate QoS, proactively address resource allocation challenges, and optimize the efficiency of cloud-based applications. The proactive resource allocation approach presented herein contributes to ongoing efforts aimed at developing responsive and effective cloud infrastructures, aligning with the ever-changing dynamics of cloud environments. The performance details, as substantiated by the comprehensive results, underscore the effectiveness of the proposed framework across multiple folds, validating its capability to significantly improve precision, recall, F1-Score, and accuracy in resource allocation for cloud environments.</p>

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Proactive Resource Allocation Framework with SVM Regression for Cloud Environments

  • 期刊论文

  • 发表日期：

    2026年1月9日

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  • Giridhar Sripathi,
  • Danish Ali Khan

摘要

In the rapidly evolving landscape of cloud computing, the efficient allocation of resources is paramount for upholding Quality of Service (QoS) standards and optimizing overall system performance. This paper introduces a groundbreaking Proactive Resource Allocation Framework that leverages Support Vector Machine (SVM) regression models to address challenges related to accurate QoS prediction, dynamic resource scaling, and effective decision-making in cloud environments. The framework utilizes SVM regression to construct prediction models based on viewer proximity, incorporating geographic data to optimize resource distribution across geo-distributed cloud locations while considering user proximity. By anticipating resource needs based on user locations, the framework implements a proactive allocation strategy, significantly enhancing overall system responsiveness. The study introduces an SVM regression model for server load prediction, facilitating the implementation of efficient auto-scaling mechanisms for dynamic resource provisioning in response to changing workloads. The utilization of SVM regression not only enhances load forecasts with precision but also streamlines auto-scaling, mitigating resource overprovisioning and underutilization. This research addresses intricate challenges associated with accurate QoS predictions, emphasizing the necessity of aligning resource allocation with the evolving demands of cloud applications. The proposed system, through the integration of machine learning and SVM regression, offers a comprehensive solution to enhance resource allocation techniques in cloud environments. This approach empowers the dynamic scaling of resources, ensuring efficient decision-making processes that prioritize operational effectiveness and user experience. The findings of this study are expected to have significant implications for cloud service providers, enabling them to elevate QoS, proactively address resource allocation challenges, and optimize the efficiency of cloud-based applications. The proactive resource allocation approach presented herein contributes to ongoing efforts aimed at developing responsive and effective cloud infrastructures, aligning with the ever-changing dynamics of cloud environments. The performance details, as substantiated by the comprehensive results, underscore the effectiveness of the proposed framework across multiple folds, validating its capability to significantly improve precision, recall, F1-Score, and accuracy in resource allocation for cloud environments.