A Dual-Layer Deep Reinforcement Learning-Based Bilateral Consensus Service Placement Approach for Edge Computing

Edge computing (EC), as a computing paradigm that mitigates cloud load and reduces task latency, has attracted widespread attention from both academia and industry. Current research on EC primarily focuses on edge task offloading problems, while effectively matching tasks with microservices after offloading is also crucial for reliable task processing. Therefore, considering the differentiated hardware resource requirements of various task types, this article designs a heterogeneous computing model that enables precise matching between tasks and edge server (ES) computational capabilities. To ensure ESs proactively deploy effective microservices and maintain trustworthy operations, we introduce an incentive mechanism and an ES discriminator algorithm. Considering diverse demands in EC scenarios, where ESs pursue higher incentive returns while reducing energy consumption, and the system aims to minimize latency and maintain reliability under limited incentive budgets, we construct an interconnected satisfaction model between ESs and the system. Based on this, we propose a bilateral consensus service placement (BCSP) algorithm that balances incentive consensus between ESs and the system, achieving rational microservice deployment and optimized task processing efficiency. Experimental results show that compared with existing algorithms, the proposed BCSP algorithm better accommodates multi-party requirements and enhances both efficiency and reliability in microservice placement.