Heterogeneous resource allocation with latency guarantee for computing power network✩

Ailing Zhong; Dapeng Wu; Boran Yang; Ruyan Wang

doi:10.1016/j.dcan.2025.03.011

›› 2026, Vol. 12 ›› Issue (1) :25 -37. DOI: 10.1016/j.dcan.2025.03.011

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Heterogeneous resource allocation with latency guarantee for computing power network^✩

Ailing Zhong ^a^,^b^,^c
, Dapeng Wu ^a^,^b^,^c^,^*
, Boran Yang ^d
, Ruyan Wang ^a^,^b^,^c

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^a School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

^b Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China, Chongqing 400065, China

^c Chongqing Key Laboratory of Ubiquitous Sensing and Networking, Chongqing 400065, China

^d School of Artificial Intelligence, Chongqing University of Technology, Chongqing 400054, China

^* School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China. E-mail addresses: d210101023@stu.cqupt.edu.cn (A. Zhong), wudp@cqupt.edu.cn (D. Wu), yangbr@cqut.edu.cn (B. Yang), wangry@cqupt.edu.cn (R. Wang).

Ailing Zhong received the B.E. and M.S. degree from Chongqing University of Posts and Telecommunications in 2018 and 2021. She is currently pursuing the Ph.D. degree in School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China. Her research interests include computing power network and network resource optimization.

Dapeng Wu received his M.S. degree in communication and information systems in June 2006 from Chongqing University of Posts and Telecommunications, and his Ph.D. degree from Beijing University of Posts and Telecommunications in 2009. He is currently the full Professor and with Chongqing University of Posts and telecommunications. He has authored over 100 publications and 2 books. His current research interests include social computing, wireless networks, and big data. Prof. Wu served as the TPC Chair or symposium chair of several international conferences. He served or is serving as an Editor and/or a Guest Editor for several technical journals such as IEEE Internet of Things Jour-nal, Digital Communications and Networks (Elsevier) and ACM/Springer Mobile Network and Applications and etc.

Boran Yang received his Ph.D. degree from Chongqing University of Posts and Telecommunications and joined Chongqing University of Technology, Chongqing, China, as a faculty member and research secretary in the School of Artificial Intelligence. He published more than 30 technical papers in top journals such as IEEE JSAC, TWC, TCSVT, TMM, TASL, TGCN, TVT, IOTJ, and GLOBECOM. His research interest includes social in-telligence, edge networks, and AIoT security.

Ruyan Wang received the Ph.D. degree from the University of Electronic Science and Technology of China, Chengdu, China, in 2007. He is the Dean of School of Communica-tion and Information Engineering, Chongqing University of Posts and Telecommunica-tions, Chongqing, China. His research interests include network performance analysis and multimedia information processing. He is a recipient of the Danian Huang Team from the Ministry of Education of the People’s Republic of China.

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Abstract

Computing Power Network (CPN) is a new paradigm that integrates communication, computing, and storage resources to provide services for tasks. However, tasks composed of non-independent subtasks have a preference for the resources required at each stage, which increases the diﬃculty of heterogeneous resource allocation and reduces the latency performance of CPN services. Motivated by this, this paper jointly optimizes the full-service cycle of tasks, including transmission, task partitioning, and oﬄoading. First, the transmission bandwidth is dynamically configured based on delay sensitivity of tasks. Second, with the real-time information from edge resource clusters and state resource clusters in the network, the optimal partitioning for a computation task is derived. Third, personalized resource allocation schemes are customized for computation and storage tasks respectively. Finally, the impact of resource parameter configuration on the latency violation probability of CPN is revealed. Moreover, compared with the benchmark schemes, our proposed scheme reduces the network latency violation probability by up to 1.17 × in the same network setting.

Keywords

Latency violation probability / Subtask dependencies / Resource allocation / Computing power network

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Ailing Zhong, Dapeng Wu, Boran Yang, Ruyan Wang. Heterogeneous resource allocation with latency guarantee for computing power network^✩. , 2026, 12(1): 25-37 DOI:10.1016/j.dcan.2025.03.011

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CRediT authorship contribution statement

Ailing Zhong: Writing-original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Dapeng Wu: Supervision, Funding acquisition, Con-ceptualization. Boran Yang: Writing-review & editing. Ruyan Wang: Supervision, Methodology.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported in part by the Chongqing Postgraduate Research and Innovation Project (CYB22250), National Natural Science Foundation of China (62271096, U20A20157), Natural Science Foundation of Chongqing-China (CSTB2023NSCQ-LZX0134, CSTB2024NSCQ-LZX0124), University Innovation Research Group of Chongqing (CXQT20017), Youth Innovation Group Support Program of ICE Discipline of CQUPT (SCIE-QN-2022-04).

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