Energy-Aware Scheduling for a Cloud Data Center by LSTM Prediction and African Vulture Optimization

As data volume increases and data parallelism strengthens, cloud service providers must reduce energy consumption by using effective cloud scheduling methods. This paper investigates scheduling multiple servers in cloud data centers to achieve more balanced energy consumption over the long term while ensuring task completion. We propose a method based on long short-term memory (LSTM) and the African vulture optimization algorithm (AVOA), combining prediction and scheduling. The proposed method is divided into two main modules: the prediction phase and the scheduling phase. The purpose of the prediction is to reserve partial resources on servers for large tasks, thereby reducing energy consumption by minimizing the number of active servers. Therefore, we use a prediction method based on LSTM to predict upcoming resource demands and anticipate server requirements. In the scheduling phase, we adjust the priority of tasks to ensure task completion while maximizing the processing of tasks with larger demands. Experiments are performed to validate the application and effectiveness of the proposed method, which outperforms the benchmark algorithms.