A reinforcement learning-based GWO-RNN approach for energy efficiency in data centers by minimizing virtual machine migration

In the era of exponential data growth, data centers face a pressing need to manage energy consumption while maintaining performance. Existing methods for optimizing energy efficiency, particularly in the context of virtual machine (VM) migrations, often fall short due to their inability to manage the complex, nonlinear relationships between resource utilization, and energy consumption. Moreover, many rely on static thresholds or single-resource metrics, which do not capture the dynamic and multi-faceted nature of data centers. In contrast, this paper introduces a novel approach that integrates a recurrent neural network (RNN) with a gray wolf optimizer (GWO). Unlike traditional models, this approach predicts future energy consumption using a more comprehensive set of resource metrics and dynamically manages workloads, reducing unnecessary VM migrations. The use of GWO optimizes the RNN's ability to capture nonlinearities, while reinforcement learning allows for continuous improvement based on real-time performance feedback. This novel combination demonstrates high predictive accuracy with minimal energy overhead, reducing the error margin to only 11% compared to optimal solutions. By doing so, the GWO-RNN framework provides a robust, adaptive solution for energy-efficient VM management in cloud environments, offering a significant advancement in the quest for sustainable data centers.