Cloud computing is one of the hottest exploring technology in computation. This environment comprises several heterogeneous resources that have the capability to perform the computation for various applications. It achieves re-usability, reduced cost, flexibility, cost-efficient; this results in the increased rate of the service providers. However, the cloud providers struggle to provide certain services within the minimized energy consumption since several algorithms focus on achieving the minimum makespan. Even though the minimized makespan can reduce the SLA violation, it fails to minimize energy efficiency. To reduce the energy consumption and the makespan, there is a specific energy-aware scheduling technique that minimizes the under-loaded resources.
   Most of the research papers based on energy-aware scheduling fail to support the parameters such as availability and accuracy on real-time applications. The existing research struggle in the case of optimizing a trade-off between SLA violation and energy efficiency. The few existing algorithms employ the dynamic threshold for monitoring the load in the resource in order to minimize energy consumption. However, they reduce the workload in the resource using the VM migration; this tends to degrade the performance and increase the operational cost. The emerging techniques are responsible for developing an algorithm for the service providers to improve the coordination among the distributed data, resource provisioning, energy-aware scheduling. Also, they have to reduce energy consumption and improve resource utilization to guarantee the quality of service (QoS) while delivering the service.