Hybrid Deep Learning Framework for Dynamic and Energy-Efficient Workload Migration in Cloud Computing Environments

Resource allocation and energy efficiency optimization have become increasingly crucial needs in the fast-evolving landscape of cloud computing, driven by increasing user demands and shrinking environmental windows. The paper introduces a Hybrid Deep Learning Framework that operates with Dynamic and Energy-Efficient Workload Migration. Using the combination of both Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM), the workload management can be improved in the cloud environment to a very great extent. With real-time data from a cloud workload, the use of the proposed framework focuses on Min-Max normalization during pre-processing, which adapts uniform scaling of the features given: CPU usage, memory utilization, and energy consumption. This preprocessing stage enables the CNN to effectively derive meaningful patterns and characteristics of resource demands and energy trends. The complex sequences are learned in the LSTM network that is used for predicting future workload behaviors based on temporal dependencies in the data. With this impressive accuracy of 98%, the hybrid approach widely outperforms existing methodologies including standalone CNN and LSTM models, which thereby proves its effectiveness in the predictions of workload shifts and optimization of energy usage. The implementation of the framework in Python further enhances the performance of prediction besides providing a feedback loop mechanism for refining adaptation of the model based on evaluation metrics. This iterative improvement process allows the framework to evolve and respond to changed dynamics in workloads, thereby contributing to more sustainable and efficient cloud computing infrastructures. With a focus on both operational efficiency and environmental sustainability, this hybrid deep learning framework, it demonstrates an attractive solution towards the challenges of modern cloud resource management problems.