Privacy-Protected Joint Service Placement and Task Offloading for Knowledge-Defined Cloud-Edge Networking

Le résumé fourni par la source

In cloud-edge collaborative networking, intelligent devices often need to offload tasks that they cannot handle to edge or cloud servers for processing. So the key problem is how to deploy various types of services and offload tasks to the appropriate servers. Deep Reinforcement Learning (DRL) algorithms have been widely used to address these issues. However, existing DRL solutions typically use centralized training methods, which can not address the challenge of obtaining global network states in practical scenarios due to the extremely large network scale and privacy concerns. In this paper, we designed a Knowledge-Defined Cloud-Edge Collaborative Networking (KDCECN) architecture for managing network information and proposed a Partially Observed Lightweight exchange Hierarchical DRL algorithm (PO-LeHDRL). This algorithm fully considers factors such as the tolerable delay of tasks, the size of tasks, and different service deployment strategies, which solves the joint service placement and task offloading problems in the cloud-edge collaborative network in a distributed training and decision-making manner. The innovation of this scheme lies in achieving data privacy protection. In this scheme, edge nodes do not need to share their respective network measurement information and apply Laplace noise to the reward value using the differential privacy mechanism, effectively reducing additional communication overhead and protecting the privacy of network data on edge devices. The experimental results show that compared with the baselines, our scheme can improve the system's task completion rate and reduce the task completion delay, and it shows scalability across different network environments.

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