## 2 Related Work  
The energy efficient task assignment problem in the mobile fog-cloud computing has been investigated widely to achieve different goals. The author in [14] proposed a fog-cloud architecture to reduce the energy consumption of the resources. The goal was to process real time data generated by different sensors at the fog-nodes. These nodes are separated by long distances and need to transmit so much energy to the multiple hops away cloud computing. An energy-aware optimal task assignment for mobile heterogeneous embedded systems in cloud computing has proposed in [18]. This research work focused on the assignment of different tasks on heterogeneous resources of the public in order to execute them with a given deadline. The DVFS technique is exploited by prior works in order to manage energy consumption in cloud resources. [PERSON]. et al. [18] in the extended version [19] proposed additional phases to assign the workload among different cloud servers in order to manage load and energy consumption for all offloaded tasks to the cloud system. Exploiting massive D2D collaboration for energy-efficient mobile edge computing algorithm is proposed in [20] where all application tasks are executed between devices in order to minimize devices energy utilization. Fog cloud and device computing environment and an energy-efficient with incentive-aware task offloading framework via network-assisted D2D has been investigated in [21], to observe the application performance and energy consumption tradeoffs. The present works [22; 23] have proposed fog-cloud architecture and energy efficient task assignment and task scheduling methods emphasizing that all applications can be executed in a parallel manner in cloud-fog architecture. [24] stresses the same task assignment and scheduling approach via network-assisted D2D collaboration. Several researchers have done work on energy efficiency in a cloud computing environment by considering different parameters, such as VM migration, device offloading and algorithms of allocation of VMs [25]. One such work provided by [PERSON] et al. and proposed three heuristic models namely Maximum Fill (MF), Smallest Void Detection (SVD) and Median Migration Time (MeMT) for energy reduction with a slight change in SLA [26]. During the experiment, the energy problem of resource utilization was analyzed and cloud-computing model for energy consumption was developed. This work focused on the linear relationship between resource utilization and energy consumption. Focusing VM migration and resource allocation a technique was considering for synchronization oriented shorter stop-and-copy phase. The algorithms were developed for complete operation step for energy-aware heuristic models, including MeMT, SVD, and MF. The evaluation of data of VMs was employed in PlanetLab servers. The results show that the proposed approaches can reduce power consumption during host shutdown and VM migration while maintaining the high performance of the system. The author proposed an adaptive heuristic based threshold algorithm for detecting the overloaded hosts in the cloud data center by gradient descent regression model using a statistical analysis of past CPU utilization data [27]. This algorithm sets a dynamic upper threshold for detecting overloaded host, thereby minimizing the performance degradation, which directly affects SLA. They also introducedthe VM selection algorithm called bandwidth-aware selection policy. This algorithm selects a VM based on network traffic, which directly affects migration cost. Therefore, it significantly minimizes the VM migration cost from one host to another host. Proposed energy-aware methods for detecting overloaded host and VM selection from an overloaded host are minimizing energy consumption and SLA violation of a cloud data center. After detecting underloaded hosts, all VM should be migrated from underloaded host to the most appropriate host and turn all idle host to energy saving mode. This would directly minimize the energy consumption of the cloud data center.