Frontiers of Data and Computing ›› 2022, Vol. 4 ›› Issue (6): 29-37.

CSTR: 32002.14.jfdc.CN10-1649/TP.2022.06.004

doi: 10.11871/jfdc.issn.2096-742X.2022.06.004

• Special Issue: East-West Computing Requirement Transfer: A Century Project to Start the Era of Computility Economy • Previous Articles     Next Articles

An Intelligent Scheduling and Allocation Method of Big Data Computing Resources Based on Computing Power Network

JIN Tianjiao1(),LI Wei2,*()   

  1. 1. China Mobile Group Zhejiang Co., Ltd., Hangzhou, Zhejiang 310030, China
    2. China Academy of Information and Communications Technology, Beijing 100191, China
  • Received:2022-08-04 Online:2022-12-20 Published:2022-12-20
  • Contact: LI Wei E-mail:jintianjiao@zj.chinamobile.com;liwei3@caict.ac.cn

Abstract:

[Objective] Since entering the age of computing power, with ubiquitous access and the interconnection of everything, the amount of data in the whole society has ushered in explosive growth. We need to solve the problems of insufficient big data computing resources, heterogeneous computing power, and edge computing power through the computing power network. [Methods] Based on the computing power network, redesign the big data computing architecture, and provide the ability of big data computing power scheduling, resource encapsulation, and unified scheduling through the resource scheduling layer. At the same time, the particle swarm optimization algorithm is used to intelligently calculate and find the optimal node in resource scheduling and allocation, so as to achieve the optimal balance of resource allocation. [Results] Through the new resource scheduling method, intelligent scheduling of all kinds of computing power can fundamentally solve the problems of insufficient big data computing resources, heterogeneous computing power, and edge computing power. [Conclusions] The intelligent scheduling and allocation method of big data computing resources based on computing power networks can intelligently schedule social idle computing power, heterogeneous computing power, and edge computing power, and solve the problem of insufficient computing power demand and uneven distribution of computing power from a global perspective.

Key words: computing power network, big data computing, resource scheduling