SerFlow: A Multistage Service-Enhanced Mechanism for Workflow Applications in CPSs With End-Edge-Cloud Collaboration

Bohai Zhao, Kai Peng, Kai Zhang, Hongliang Sun, Zhiying Tu, Dianhui Chu

Abstract: The predominant obstacles confronting contemporary cyber-physical systems (CPSs) are their extensive heterogeneity and stringent constraints, such as diverse applications and real-time service requirements. While the incorporation of mobile edge computing could alleviate some of these constraints, challenges persist in equilibrating services and loads due to the finite computational resources of edge servers. Concurrently, the implementation of associated modules or methodologies has been prompted by escalating apprehensions regarding service security, which may have a detrimental impact on the performance of CPS, particularly in terms of resource occupation and service overhead. To this end, we develop an end-edge–cloud-collaborative CPS framework in which tasks are modeled as workflow applications, followed by constructing a multistage service-enhanced method named SerFlow. In SerFlow, a relevance-aware security precaution mechanism is devised, which evaluates the connection hierarchy and correlation metrics across subtasks, subsequently establishing the service anti-conflict mechanism to augment security levels. Particularly, a comprehensive evaluation score for security precaution levels is provided, which enables the security precaution to engage in the following optimization operations and evaluation modules as a quantifiable metric. Leveraging the non-Euclidean geometry framework, we then develop a Pareto frontier modelling method that integrates Newton-Raphson and geodesic while a survival value evaluation strategy with correlation constraints is involved in accomplishing coarse-grained cluster selection. Subsequently, an improved value-based and model-free deep reinforcement learning algorithm is suggested to generate fine-grained service strategies in end-edge–cloud collaborative scenarios. Finally, comprehensive experiments demonstrate the effectiveness of SerFlow in achieving enhanced security precaution levels while maintaining superior service efficiency.

External IDs:dblp:journals/iotj/ZhaoPZSTC25