Scheduling optimization for upstream dataflows in edge computing

Abstract

Edge computing can alleviate the problem of insufficient computational resources for the user equipment, improve the network processing environment, and promote the user experience. Edge computing is well known as a prospective method for the development of the Internet of Things (IoT). However, with the development of smart terminals, much more time is required for scheduling the terminal high-intensity upstream dataflow in the edge server than for scheduling that in the downstream dataflow. In this paper, we study the scheduling strategy for upstream dataflows in edge computing networks and introduce a three-tier edge computing network architecture. We propose a Time-Slicing Self-Adaptive Scheduling (TSAS) algorithm based on the hierarchical queue, which can reduce the queuing delay of the dataflow, improve the timeliness of dataflow processing and achieve an efficient and reasonable performance of dataflow scheduling. The experimental results show that the TSAS algorithm can reduce latency, minimize energy consumption, and increase system throughput.