In the new era, the arrival of 5G has added a lot of fun and beauty to our lives, allowing us to enjoy the happiness brought by the network even more. The 5th Generation mobile technology (5G for short) is a new generation of broadband mobile communication technology with features such as higher speed, lower latency, and massive connectivity. Moreover, this is a network infrastructure that, in fact, realizes the interconnections among humans, computer machines, and sensors/things, i.e., Internet of things. The world can now be characterized as a whole global village. The 5 G, mobile edge computing networks, and cloud-edge collaborative computing allow us to communicate and share information on the cloud networks, allowing us to better understand the global village. Based on cloud-edge collaborative computing for 5G edge networks, this study builds a task scheduling algorithm, which will help us to check network defects and make better use of the 5G technology to serve customers. Experimental results demonstrate the extra efficiency of the proposed algorithm over the closest rivals.

The fast popularization of the Internet of Things (IoT) has caused the data scale to increase geometrically. The data of IoT devices is processed on the cloud, but the way of processing data in the cloud center gradually causes problems, such as communication delay, latency, and privacy leakage. Edge computing sinks some cloud center services to the edge of the device so that data processing is completed in the terminal network, thereby realizing rapid data processing. At the same time, since long-distance communication is avoided, user data is processed locally, so that user privacy data can be safely protected. A genetic algorithm is a type of heuristic algorithm that is based on the genetic development of organisms in nature and has a high global optimization capability. The basic aim and objective of this paper is to study the existing edge computing framework along with computing offloading technology. The genetic algorithm is investigated using multiedge computing-oriented collaborative computing offloading, which is helpful to the IoT’s growth as well as the generation and the use of data. The use of a genetic algorithm based on a color graph for load balancing on several edge servers is also investigated. In terms of the study’s performance evaluation, it is obvious that our proposed approach produces superior results than previous studies.