# Power Distribution IoT Tasks Online Scheduling Algorithm Based on Cloud-Edge Dependent Microservice
## 1 Introduction  
With the construction of the IoT for power distribution, the number of terminal devices and tasks is rapidly increasing, and the traditional cloud computing model has bottlenecks in terms of response speed and communication latency [1, 2]. Migrating some of the computations from the cloud to the edge computing nodes on the data source side can reduce the pressure on the cloud and satisfy real-time data analysis and low-latency task processing [3]. Power distribution IoT cloud and edge are based on the idea of software definition and the decoupling of hardware and software through microservice architecture to further reduce the application control granularity [4]. The literature [5] proposes an edge computing-based distribution automation architecture, including a five-layer architecture: master station, medium-voltage lines, distribution transformers, low-voltage lines, and low-voltage users. The intelligent distribution terminal is connected to the cloud master station, providing data to the cloud master station and accepting control commands from the cloud master station. At the same time, it supports massive equipment access and receives data uploaded from the end-side as well as issuing commands to the end-side equipment. The literature [6] proposes a real-time monitoring framework for a smart grid based on edge computing. With edge computing, the process of data mining and processing is moved to the edge server. While applications are increasing with the expansion of new services, there may be a shortage of device computing, storage, and communication resources. Using the characteristics of microservices independent of each other, according to the differentiated needs of application services, selecting some applications to offload to the cloud for execution and releasing the resources of the device are of great significance tofully utilize the respective advantages of the cloud edge and improve the efficiency of power task execution. There are many studies on task scheduling for grid devices. The literature [7] proposes an edge computing-based architecture for the optimal scheduling of active distribution grids. The grid is optimized by a control center in the region and edge computing nodes are used for the optimization of distributed energy sources. By establishing collaborative optimization between the control center and the edge nodes, the amount of information exchange as well as the bandwidth requirements are reduced. The literature [8] adds alternate edge nodes to the system architecture that only compute and do not generate tasks. Tasks can be offloaded to either the local edge node, the residential cloud platform, or the alternate edge computing node. The scheduling process considers the task latency and energy consumption, and uses game theory to offload the residential electricity tasks to achieve multi-party optimality by solving the Nash equilibrium. Under the cable monitoring application, the literature [9] models the task queue in the edge nodes and the task allocation among the edge nodes to solve the problem using an improved particle swarm algorithm. The proposed strategy reduces the execution time of tasks and improves the resource utilization of the system. By describing tasks with dependencies through a workflow model, the literature [10] proposes a list-based task scheduling algorithm that reduces task execution time and improves service quality by considering the running speed of containers and task concurrency in the task scheduling process. The limitation of computational capacity and resources of the edge server in processing tasks is better solved. The literature [11] uses virtual machines as task processing carriers to meet the requirements of latency-sensitive tasks by solving three subproblems: load balancing, resource allocation, and task assignment.