A Genetic Algorithm for Multiedge Collaborative Computing Offloading Scheme

Abstract

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.