An Adaptive Access Method for Edge Clusters of Distribution Automation Terminals Based on Cloud-Edge Fusion

Abstract

As massive distribution automation terminals connect and data is acquired at high frequencies, the demand for low-latency processing of distribution service data has increased dramatically. Edge clusters, integrating multiple edge servers, can effectively mitigate transmission delays. Cloud-edge fusion leverages its data processing capabilities and the real-time responsiveness of edge computing to meet the needs of efficient data processing and optimal resource allocation. However, existing access methods for distribution automation terminals in cloud-edge fusion architectures exclusively depend on either cloud or edge computing for data processing. These conventional approaches fail to incorporate critical aspects such as: adaptive access mechanisms for edge clusters of distribution automation terminals, flexible strategies including data offloading, knowledge sharing among edge clusters, and load awareness capabilities. Consequently, they demonstrate significant limitations in achieving deep fusion between cloud and edge computing paradigms. Additionally, they lack consideration for the perception of global information and queue backlog, making it difficult to meet the low-latency data transmission requirements of distribution automation services in dynamic environments. To address these issues, we propose an adaptive access method for edge clusters of distribution automation terminals based on cloud-edge fusion. Firstly, a data processing architecture for adaptive access of distribution automation terminal edge clusters are designed to coordinate terminal access, data processing distribution, and decision optimization for computing resource allocation, enabling efficient data transmission and processing. Secondly, an optimization problem for adaptive access in edge clusters of distribution automation terminals is formulated, aiming to minimize the weighted sum of total queuing delay and load balancing degree. Finally, a federated twin delayed deep deterministic policy gradient (federated TD3)-based edge cluster adaptive access method for distribution automation terminal is proposed. This approach integrates model parameters from edge servers at the cloud level and distributes them to the edge cluster level, learning strategies for terminal access, data processing allocation, and computing resource allocation based on queue backlog fluctuations. This enhances load balancing between the distribution terminal layer and edge layer, achieving collaborative optimization of load balancing and delay under massive distribution terminal access. Simulation results demonstrate that the proposed method significantly reduces system queuing delay, optimizes load balancing, and enhances overall operation efficiency.