When it comes to cloud computing, energy efficiency has been one of the primary concerns. inside the scope of this study is an analysis of how effectively and economically resources are distributed inside the cloud. There have been various different approximations suggested due to the fact that cloud resource allocation is an NP-hard problem. The distribution of resources is another area that might benefit from approximate solutions; hence, such methods can be valuable for future work. In this study, we focus on efficient energy-aware cloud computing (EEAC) methodologies for system and device detection and classification, optimization approaches, and energy/power management strategies. Processing units and hybrid systems are examples of the different sorts of devices, whereas networks, clusters, and clouds are instances of the different kinds of system types. The objective is to perform calculations such as the execution time and the energy use while maintaining as low a level of power and energy consumption as possible. We explore power and energy management solutions and application programming interfaces (APIs), as well as approaches and scenarios for predicting or modelling power or energy consumption in existing EEAC systems. This paper provides a survey of approaches and techniques for energy efficiency in cloud computing.

Cloud computing, energy efficient, power consumption, energy usage, energy aware cloud.