As a key 5G enabler, edge computing locates resources near users to enable low-latency services, and microser-vices architecture (MSA) decomposes monolithic services into microservices, easing deployment and scaling of mission-critical applications with high reliability requirements. Existing studies on microservice backup and deployment often adopt dedicated backup strategies, resulting in excessive redundancy and high resource consumption. This paper investigates reliable service deployment with shared backups in resource-constrained and dynamic edge environments, aiming to meet differentiated reliability requirements while optimizing resource efficiency. However, shared backups introduce interdependencies among services, complicating relationships among backup and deployment strategies, service reliability, resource costs, and loads. To model these intricate relationships, we divide microservices (service functions) of the same type into service function groups for sharing backups and formulate the problem as an integer nonlinear programming problem, proving its complexity. Moreover, the sequential arrival of requests in dynamic environments renders this problem a multi-stage decision problem, with high-dimensional resource constraints and a large-scale solution space adding complexity. Thus, we design a deep reinforcement learning (DRL)-based shared backup scheme with theoretical guarantees, which leverages DRL to capture long-term rewards and generate fractional solutions, while domain knowledge—specifically, the insight that providing additional backups to service function groups with low reliability yields greater reliability improvements—is used to adjust the fractional solutions. This adjustment helps prevent the DRL agent from falling into poor local optima and violating constraints. Finally, extensive experiments on real-world Alibaba traces show our method reduces costs by up to 70.52% compared to state-of-the-art approaches.
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