Cloud data center performance optimization through machine learning-based workload forecasting and energy efficiency

The accelerating adoption of cloud models has increased the amount of complexity in cloud data centers with particular emphasis on the energy management load efficiency on resources in relation to the workload tris. This paper introduces a new fully-converged architectural framework enhanced by machine learning features that addresses several common issues: workload forecasting, adaptive scheduling and energy optimization for better performance at hyper scale cloud data centers. Using a Gated Recurrent Unit (GRU), the method described in the paper learns and remembers the complicated sequence of nonlinear workloads, enabling it to allocate resources appropriately in advance. Schedule optimization is achieved via a gradient boosting method in which resource managers are proactively chosen based on the expected workload in order to enhance scheduling and reduce task waiting times. Furthermore, virtual machine clustering models based on energy consumption patterns are incorporated into the design to enhance the framework's efficiency in energy usage optimization by controlling the number of virtual machines and their migration. The test case application based on realistic data center traces verified better energy effectiveness and resource utilization levels with service level agreement compliance for this entire integrated method. The study further underscores the opportunity for machine learning models to pinpoint and even combine distinct operational stresses prevalent in cloud data center environments.