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<p>The rapid advancement of cloud technology has heightened the demand for real-time data processing systems that provide accuracy, flexibility, and scalability. Autoscaling manages cloud resources automatically in real-time, employing either reactive or proactive approaches. Reactive autoscaling adjusts resources based on predefined thresholds but can be inefficient during fluctuating workloads. In contrast, proactive autoscaling predicts future workloads, enabling preemptive resource adjustments to optimize performance. This study proposes an autoscaling approach based on the monitor-analyze-plan-execute (MAPE) framework, which emphasizes proactive strategies by integrating feature selection techniques with a multivariate transformer (MV-Transformer) approach. This MV-Transformer approach excels at capturing long-term dependencies and complex interactions among multiple variables while using less memory. The framework enhances resource provisioning, as evidenced by the lowest under-provisioning value of 0.2892240 and duration of time under-provisioning value of 10.6676060, indicating superior performance. Additionally, the MAPE autoscaling framework achieves an elastic speedup of 2.9818, compared to 1.3200 for Bi-LSTM, 1.0230 for LSTM, and 1.0000 for reactive without autoscaling. The proactive MV-Transformer approach demonstrates significant improvements in resource management by evaluating this elastic speedup and resource provisioning metrics against both reactive without autoscaling and other proactive autoscaling approaches. For real-world implementation, docker desktop and Kubernetes were used to dynamically scale VMs based on workload, orchestrated by the MAPE autoscaling framework. This approach also helps in handling high dynamic workloads and overall efficiency in cloud computing, particularly in scaling and de-scaling. Our implementation codes are available at the following GitHub link: <a href="https://github.com/BABLU-KUMAR/MV-Transformer-based-MAPE-Autoscaling-Framework/tree/main">https://github.com/BABLU-KUMAR/MV-Transformer-based-MAPE-Autoscaling-Framework/tree/main</a>.</p>

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A multivariate transformer-based monitor-analyze-plan-execute (MAPE) autoscaling framework for dynamic resource allocation in cloud environment

  • 期刊论文

  • 发表日期：

    2025年2月13日

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  • Bablu Kumar,
  • Anshul Verma,
  • Pradeepika Verma

摘要

The rapid advancement of cloud technology has heightened the demand for real-time data processing systems that provide accuracy, flexibility, and scalability. Autoscaling manages cloud resources automatically in real-time, employing either reactive or proactive approaches. Reactive autoscaling adjusts resources based on predefined thresholds but can be inefficient during fluctuating workloads. In contrast, proactive autoscaling predicts future workloads, enabling preemptive resource adjustments to optimize performance. This study proposes an autoscaling approach based on the monitor-analyze-plan-execute (MAPE) framework, which emphasizes proactive strategies by integrating feature selection techniques with a multivariate transformer (MV-Transformer) approach. This MV-Transformer approach excels at capturing long-term dependencies and complex interactions among multiple variables while using less memory. The framework enhances resource provisioning, as evidenced by the lowest under-provisioning value of 0.2892240 and duration of time under-provisioning value of 10.6676060, indicating superior performance. Additionally, the MAPE autoscaling framework achieves an elastic speedup of 2.9818, compared to 1.3200 for Bi-LSTM, 1.0230 for LSTM, and 1.0000 for reactive without autoscaling. The proactive MV-Transformer approach demonstrates significant improvements in resource management by evaluating this elastic speedup and resource provisioning metrics against both reactive without autoscaling and other proactive autoscaling approaches. For real-world implementation, docker desktop and Kubernetes were used to dynamically scale VMs based on workload, orchestrated by the MAPE autoscaling framework. This approach also helps in handling high dynamic workloads and overall efficiency in cloud computing, particularly in scaling and de-scaling. Our implementation codes are available at the following GitHub link: https://github.com/BABLU-KUMAR/MV-Transformer-based-MAPE-Autoscaling-Framework/tree/main.