Collaborative Evolution of Intelligent Agents in Large-Scale Microservice Systems

2025-08-28

AI Summary

Governing large-scale microservice systems faces challenges stemming from complex service dependencies, dynamically evolving topologies, and highly volatile workloads. Method: This paper proposes a multi-agent coevolutionary optimization framework that integrates graph representation learning (to model service dependencies), multi-agent reinforcement learning (MARL), and evolutionary game theory. It establishes a “game-driven” policy optimization mechanism wherein agent strategy distributions are dynamically refined via selection and mutation operations, enabling both local adaptability and global coordination. The approach adopts a centralized training with decentralized execution paradigm, leveraging graph neural networks (GNNs) and Markov decision processes (MDPs) for modeling. Contribution/Results: Experiments demonstrate that the method significantly outperforms state-of-the-art baselines in coordination efficiency, environmental adaptability, and policy convergence speed, thereby enhancing governance effectiveness and operational stability of microservice systems.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Game Theory and Economic Paradigms: Coordination and Collaboration
• Machine Learning: Scalability of ML Systems

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

This paper proposes an intelligent service optimization method based on a multi-agent collaborative evolution mechanism to address governance challenges in large-scale microservice architectures. These challenges include complex service dependencies, dynamic topology structures, and fluctuating workloads. The method models each service as an agent and introduces graph representation learning to construct a service dependency graph. This enables agents to perceive and embed structural changes within the system. Each agent learns its policy based on a Markov Decision Process. A centralized training and decentralized execution framework is used to integrate local autonomy with global coordination. To enhance overall system performance and adaptability, a game-driven policy optimization mechanism is designed. Through a selection-mutation process, agent strategy distributions are dynamically adjusted. This supports adaptive collaboration and behavioral evolution among services. Under this mechanism, the system can quickly respond and achieve stable policy convergence when facing scenarios such as sudden workload spikes, topology reconfigurations, or resource conflicts. To evaluate the effectiveness of the proposed method, experiments are conducted on a representative microservice simulation platform. Comparative analyses are performed against several advanced approaches, focusing on coordination efficiency, adaptability, and policy convergence performance. Experimental results show that the proposed method outperforms others in several key metrics. It significantly improves governance efficiency and operational stability in large-scale microservice systems. The method demonstrates strong practical value and engineering feasibility.

Problem

• Addressing governance challenges in large-scale microservice architectures
• Managing complex service dependencies and dynamic topology structures
• Handling fluctuating workloads and ensuring system stability

Innovation

• Multi-agent collaborative evolution for service optimization
• Graph representation learning for dependency perception
• Game-driven policy optimization with selection-mutation