Frontiers of Data and Computing ›› 2020, Vol. 2 ›› Issue (4): 80-91.doi: 10.11871/jfdc.issn.2096-742X.2020.04.007

• Special Issue: Next Generation Internet Technology & Application • Previous Articles     Next Articles

Research on Edge Resource Scheduling Solutions for Computing Power Network

Li Mingxuan1,*(),Cao Chang1(),Tang Xiongyan1(),He Tao1(),Li Jianfei1(),Liu Qiuyan2()   

  1. 1. Department of Future Network Research, China Unicom Network Research Institute, Beijing 100048, China
    2. Department of Wireless Technology Research, China Unicom Network Research Institute, Beijing 100048, China
  • Received:2020-03-28 Online:2020-08-20 Published:2020-09-10
  • Contact: Li Mingxuan E-mail:limx59@chinaunicom.cn;caoc15@chinaunicom.cn;tangxy@chinaunicom.cn;het21@chinaunicom.cn;lijf299@chinaunicom.cn;liuqy95@chinaunicom.cn

Abstract:

[Objective] This paper introduces a network organization method for computing power network to satisfy business needs, which is able to flexibly schedule and allocate computing resources among clouds, networks, and edge devices. This approach aims to schedule and manage a wider range of computing resources in a unified framework. At the edge of the network, due to the large number of embedded devices and their different architectures, it is difficult for the existing resource scheduling methods to meet the demand of computing power. [Methods] Starting from the computing network architecture and based on the cloud-native resource scheduling mechanism, a lightweight, multi-cluster hierarchical edge resource scheduling scheme is described. [Results] Based on the lightweight cloud-native platform, we successfully manage and deploy a massive amount of hetero-architecture edge devices inside computing power networks in a unified framework. [Limitations] As a unified resource scheduling platform for front-end equipment in the “cloud, edge, and end” designed for computing power networks, it is important to solve the problems of implementing cloud-side collaboration, deploying artificial intelligence algorithms in front-end embedded clusters and making front-end equipment more autonomy. [Conclusions] The front-end embedded resource scheduling solution for computing power networks can be widely used in the Internet of Things, Internet of Vehicles, smart cities and other fields to improve the autonomous processing capabilities of the front-end equipment, and solve other practical problems such as the lack of innovation capabilities and insufficient supports in intelligent industry of China.

Key words: computing power network, container technology, embedded technology, edge computing