Cloud Workload

Cloud workload management aims to optimize resource utilization and performance in dynamic cloud environments by efficiently allocating and scaling resources to meet fluctuating demands. Current research focuses on developing advanced algorithms, including reinforcement learning and machine learning models like transformers and LSTMs, for tasks such as load balancing, anomaly detection, and workload prediction. These efforts are driven by the need to improve resource efficiency, reduce costs, and ensure high quality of service for cloud applications, impacting both the efficiency of cloud infrastructure and the performance of applications running on it.