# A Review of Edge Computing Technology and Its Applications in Power Systems
## 3 Review of Edge Computing Technologies
### Computational Offloading  
In edge computing, computational offloading refers to the technique that transfers heavy computation tasks to nearby edge servers [57]. With the increasing number of resource-demanding applications, improving hardware in mobile ends and networks will still not be able to keep up with the trend in demand. Therefore, computational offloading has gained attention in edge computing research. Many researchers have studied computational offloading to resource-rich edge servers. Computational offloading is the key technology to enable MAEC to run complex applications on edge equipment by delivering compute-intensive tasks to separate edge servers. There have been many related research achievements mainly discussing offloading strategies. In 2016, [PERSON] et al. [58] proposed a mobile edge computing-based offloading framework in a cloud-enabled vehicular network. In this framework, an efficient offloading strategy is designed. Meanwhile, considering the limitation of the edge servers and latency tolerance of the computing tasks, a contract-based computing resource scheduling scheme is designed. The experiments show that the proposed scheme improves the performance of the MAEC utility. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & **Application Area** & **References** \\ \hline \multirow{4}{*}{Multi-Access Edge Computing} & Healthcare & [21] \\  & Smart City & [22; 23] \\  & Video Analytics & [24; 25] \\  & Connected Vehicles & [26] \\ \hline \multirow{4}{*}{Cloudlet Computing} & Face Recognition & [31] \\  & Healthcare & [32] \\  & Smart Homes & [33; 34; 35] \\  & Information System & [36] \\ \hline \multirow{4}{*}{Fog Computing} & Healthcare & [45; 46; 47; 48] \\  & Smart City & [49; 50; 51; 52] \\ \cline{1-1}  & Video and Game Analytics & [53; 54] \\ \cline{1-1}  & Intelligent Traffic & [55; 56] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Reference Table of Edge Computing Architecture. In 2020, [PERSON] et al. [59] also focused on task offloading in vehicular edge computing networks. Various delay-sensitive vehicular applications have emerged due to the rapid development of vehicular networks. MAEC is becoming a promising paradigm, benefiting vehicular edge computing networks by offloading the vehicles' compute-intensive tasks to nearby edge nodes. The authors introduced fiber-wireless technology to address load-balancing issues and proposed a software-defined networking (SDN)-based load-balancing task offloading scheme in Wi-Fi-enhanced vehicular edge computing networks. This scheme uses SDN to support centralized network and vehicle data management. The experiment results demonstrate that this scheme can achieve superior performance on delay reduction. Real-time analytics of video content are widely recognized as a prime application for edge computing. They often involve heavy computing tasks and a large amount of data. In 2018, [PERSON] et al. [60] proposed VideoEdge, which is a hierarchical architecture. This architecture includes cameras, clusters, and the cloud for video analytics targeting query optimizations. The architecture presents a prominent demand for determining the optimal balance between various resources and precision, thus reducing the search space. VideoEdge, for each video inquiry, chooses the most suitable amalgamation of algorithmic elements and distributes them across diverse clusters. VideoEdge operates under the assumption that camera-based computing is absent, shifting the entirety of computer vision processing to secluded clusters. [PERSON] et al. [61] proposed a trust-aware task offloading method (TOM) for video surveillance in edge computing-enabled Internet of Vehicles. This method aims to minimize the response time of the services and achieve the load balance among the edge nodes. By analyzing the process of video tasks, the authors formulated the problems of task offloading, time cost of services, and privacy entropy as a multi-objective optimization problem.