A Comprehensive Survey on Energy and Performance-Aware Scheduling Approaches in Cloud Computing

As cloud computing is growing rapidly and resource management is becoming more important, energy and performance-aware task scheduling in cloud computing is an important study topic. To optimize energy usage and boost overall system performance in cloud computing, this study gives a comprehensive overview of the available research on task scheduling strategies. We show the advantages and limitations of a wide variety of scheduling algorithms, methods, and approaches. This review examines how academics and industry professionals have dealt with cloud computing's unique mix of difficulties, including fluctuating workloads, diverse resource requirements, and unpredictable performance. Important gains in both energy economy and system performance have resulted from adopting machine learning techniques that can adapt to various scheduling conditions. Our research highlights the need for data-driven models and Artificial Intelligence (AI-assisted) scheduling decisions to maximize resource utilization and satisfy the wide-ranging performance needs of cloud applications. We also highlight areas where additional study is needed, such as in the areas of large-scale data processing, security, and dynamic scheduling systems that can accommodate workload changes. This survey is useful for researchers, professionals, and policymakers in the domain of Cloud Computing (CC) since it compiles the results of a wide range of studies.