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Profit-Driven Service Caching Method for CPSS With Edge-Cloud Collaboration

Year: 2026

Type: article

Abstract: The edge-cloud collaboration model, as an emerging computing architecture, holds significant potential for applications, particularly in cyber–physical–social systems (CPSSs). In CPSS applications, service providers lease edge servers to cache core services, improving both user satisfaction and profit. However, a key challenge lies in designing personalized resource leasing and caching strategies that enhance service provider profit while maintaining high user satisfaction. To address this challenge, this article proposes a service provider profit-driven caching method for public edge devices. First, a resource leasing framework for edge-cloud collaboration is developed. Then, based on game theory, a resource leasing strategy for public edge servers is formulated. Finally, a personalized service caching approach is introduced, leveraging deep reinforcement learning. Theoretical analysis and experimental results demonstrate that the proposed method effectively boosts service provider profits while ensuring user satisfaction.

Source: IEEE Transactions on Computational Social Systems

DOI: 10.1109/tcss.2026.3724203

Authors: Wei Liu, Wenmin Lin, Hongsheng Hu, Hanzhi Yan, Hongyi Lyu, Wei Fan

Institutions: Southeast University, Hangzhou Normal University, University of Newcastle Australia, Macquarie University, University of Oxford

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,645

OpenAlex ID: W7204185585

FWCI:

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