Flexible Task Scheduling Based on Edge Computing and Cloud Collaboration

With the rapid development and popularization of 5G and the Internet of Things, a number of new applications have emerged, such as driverless cars. Most of these applications are time-delay sensitive, and some deficiencies were found during data processing through the cloud centric architecture. The data generated by terminals at the edge of the network is an urgent problem to be solved at present. In 5 g environments, edge computing can better meet the needs of low delay and wide connection applications, and support the fast request of terminal users. However, edge computing only has the edge layer computing advantage, and it is difficult to achieve global resource scheduling and configuration, which may lead to the problems of low resource utilization rate, long task processing delay and unbalanced system load, so as to lead to affect the service quality of users. To solve this problem, this paper studies task scheduling and resource collaboration based on a Cloud-Edge-Terminal collaborative architecture, proposes a genetic simulated annealing fusion algorithm, called GSA-EDGE, to achieve task scheduling and resource allocation, and designs a series of experiments to verify the effectiveness of the GSA-EDGE algorithm. The experimental results show that the proposed method can reduce the time delay of task processing compared with the local task processing method and the task average allocation method.