Ubiquitous Power Internet of Things-Oriented Low-Latency Edge Task Scheduling Optimization Strategy

Abstract

Internet of things of cloud computing offers high-performance computing, storage and networking services, but there are still suffers from a high transmission and processing latency, poor scalability and other problems. Internet of things of edge computing can better meet the increasing requirements of electricity consumers for service quality, especially the increasingly stringent need for low delay. On the other hand, edge intelligent network technology can offers edge smart sensing while significantly improve the efficiency of task execution, but it will lead to a massive collaborative task scheduling optimization problem. In order to solve this problem, This paper studies an ubiquitous power internet of things (UPIoT) smart sensing network edge computing model and an improved multi node cluster cooperative scheduling optimization strategy. The cluster server is added to the edge aware computing network, and an improved low delay edge task collaborative scheduling algorithm (LLETCS) is designed by using the vertical cooperation and multi node cluster collaborative computing scheme between edge aware networks. Then the problem is transformed based on linear reconstruction technology, and a parallel optimization framework for solving the problem is proposed. The simulation results suggest that the proposed scheme can more effectively reduce the UPIoT edge computing latency, and improve the quality of service in UPIoT smart sensing networks.