<p>In this modern era, cloud computing is not enough to meet today’s intelligent society’s data processing needs, so edge computing has emerged. In contrast to computation in the cloud, it elaborates user proximity and proximity to the data source. To store local, small sized, and processed data on the edges of the network is more effective. The edge paradigm, intended to be a leading computation due to its low latency, also faces many challenges due to computational capabilities and resource availability. Edge computing allows edge devices to release heavy loads and computational operations on the remote server. This allows us to take full advantage of the server-side computing and storage in edge devices. However, the offload of all highly compressed computing operations on a remote server at the same time may become overcrowded, leading to intensive processing delays for many computing operations and unexpectedly elevated power usage. Instead of that, it is possible that spare edge resources may need to be utilized effectively and the access to expensive cloud resources would be restricted. As a result, it is important to investigate the collaborative planning process (scheduling) for the edge servers with a cloud server based on task features, development objectives, and system status. It can assist in performing all the computing functions efficiently and effectively. This paper analyzes and summarizes computing conditions for the edge computing context and classifies the computation of tasks into various edge-cloud computing scenarios. At the end, based on the problem structure, various collaborative planning methods for computational functions are presented.</p>