A Computing and Transmission Integrated Optimization Method for Cloud-Edge-End Computing First System

Chen Xingyan; Zhang Xuesong; Xie Zhilong; Zhao Yu; Wu Gang

doi:10.7544/issn1000-1239.202221053

Journal of Computer Research and Development > 2023 > 60(4): 719-734. > DOI: 10.7544/issn1000-1239.202221053 CSTR: 32373.14.issn1000-1239.202221053

Chen Xingyan, Zhang Xuesong, Xie Zhilong, Zhao Yu, Wu Gang. A Computing and Transmission Integrated Optimization Method for Cloud-Edge-End Computing First System[J]. Journal of Computer Research and Development, 2023, 60(4): 719-734. DOI: 10.7544/issn1000-1239.202221053

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A Computing and Transmission Integrated Optimization Method for Cloud-Edge-End Computing First System

Institute of Digital Economy and Interdisciplinary Science Innovation, Southwestern University of Finance and Economics, Chengdu 611130

Funds: This work was supported by the Natural Science Foundation of Sichuan Province (2023NSFSC0032, 2023NSFSC0114)

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Author Bio:
Chen Xingyan: born in 1994. PhD, lecturer, master supervisor. Member of CCF and of IEEE. His main research interests include multimedia communications, multi-agent reinforcement learning, stochastic optimization

Zhang Xuesong: born in 1993. Master candidate. His main research interests include multimedia communications and financial technology

Xie Zhilong: born in 1978. PhD, associate professor, master supervisor. Member of CCF. His main research interests include data mining, financial technology and stochastic optimization

Zhao Yu: born in 1984. PhD, professor, PhD supervisor. member of CCF, member of IEEE. His main research interests include data mining, natural language processing, graph learning, and machine learning

Wu Gang: born in 1985. PhD, associate professor, master supervisor. His main research interests include stochastic optimization, international economy and trading, regional management and urban governance
Received Date: December 29, 2022
Revised Date: February 12, 2023
Available Online: March 09, 2023

Graphical Abstract

Abstract

Abstract

Collaborative optimization of “cloud-edge-end” resource collaboration optimization is one of the key challenges in the deployment of computing power networks. Effectively integrating heterogeneous computing resources, such as high-performance cloud computing, low-latency edge computing, and low-cost user devices, is of great significance for the construction of computing first networks. Based on this, we propose a joint optimization scheme for computing and transmission in “cloud-edge-end” computing first networks, aiming to provide a systematic solution from three aspects: application service model, network state awareness, and resource collaboration optimization. Firstly, according to the characteristics of general application services, the traditional network service chain representation model is improved, and a generalized graph structure-based universal service model is proposed. Secondly, to characterize the dynamic rules of heterogeneous network states, a dual virtual queue structure for modeling time-varying computing and transmission loads is proposed. Thirdly, to reduce the complexity of joint optimization of computing and transmission resources in large-scale computing first networks, an augmented graph model based on graph concepts is proposed. This model can transform the joint optimization problem of computing and transmission into a routing problem of the augmented graph, simplifying the formal representation difficulty of heterogeneous resource joint optimization problems. To solve this problem in practice, a heterogeneous resource collaboration optimization algorithm based on the Polyak Heavy-Ball Method is designed, and the algorithm complexity and related theoretical performance analysis are provided. Finally, through numerical simulations and prototype system experiments, the correctness of the algorithm's theoretical performance is verified, as well as the performance advantages in terms of service utility and resource cost compared to three contemporary relevant solutions.

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References (33)

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