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In cloud computing, energy has been a major concern. This study does an evaluation of energy-efficient allocation of resources in the cloud. Because cloud allocation of resources is an NP-hard issue, several approximations have been proposed. Approximate solutions are also useful in resource allocation; therefore such solutions can be helpful for future work. This paper focuses on efficient energy-aware cloud (EEAC) computing approaches for system and device recognition and classification, optimization approaches, and energy / power control techniques. Network, clusters, and clouds are examples of system types, while processing units, and hybrid systems are examples of device kinds. The goal includes calculations such as execution time and energy usage, all while keeping power and energy consumption to a minimum. Scheduling controls, frequency-based policies (DVFS, DFS), application standardization, and hybrid approaches are common control measures. We investigate energy/power management solution and APIs, as well as methodologies and scenarios for forecasting or modeling power/energy use in current EEAC systems.
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