Improved Genetic Algorithm for Computation Offloading in Cloud-Edge-Terminal Collaboration Networks

The massive use of Internet of Things (IoT) mobile devices (MDs) and the increasing demand for their computing have created huge challenges for the current development of the IoT. Mobile edge computing (MEC) and cloud computing provide a scheme for these problems. In the process of offloading, MDs and servers are facing difficulties such as high consumption and high latency. So it is necessary to reasonably offload computing tasks to MDs, edge servers, or cloud servers. In view of this situation, the research direction of this article is how to reduce the power consumption of the device and the server while ensuring that the delay requirements of different tasks are met. First we formulate the proposed problem as a nonlinear combinatorial optimization problem, then propose the cloud-edge-terminal collaboration offloading algorithm based on improved genetic algorithm (IGA). Finally, the characteristics of the algorithm are studied by simulation and compared with other algorithms to verify the performance of the algorithm.