Modern software systems increasingly adopt microservice architectures (MSA) to achieve modularity, scalability, and independent deployment. However, the decentralized nature of microservices introduces complex challenges in resource allocation, load balancing, and maintaining system-wide performance under dynamic workloads. Traditional orchestration methods often rely on heuristic or static rules that are insufficient for optimizing resource usage in highly variable and interactive environments. This research explores the application of game theory as a formal framework for modeling and optimizing interactions among microservices. In this approach, each microservice is treated as a rational agent or player that independently selects strategies for resource consumption, scaling, or request routing. By applying models of non-cooperative games, such as congestion games, we identify equilibrium states (e.g., Nash equilibrium) that ensure stable and fair allocation of limited resources. In cooperative settings, game-theoretic mechanisms like Nash Bargaining can promote system-wide optimization through strategic coordination. Simulation results demonstrate that game-theoretic strategies can significantly improve performance metrics, including average response time, resource utilization, and system resilience, compared to conventional approaches. Moreover, the integration of game-theoretic models with machine learning enables adaptive decision-making, allowing services to update strategies based on observed system states and predicted loads. The paper shows that game theory provides a powerful and scalable foundation for the self-optimization of microservice-based systems. It opens new possibilities for designing intelligent orchestration layers capable of dynamically balancing autonomy and coordination in distributed software environments.

microservice architecture, optimization, game theory, Nash equilibrium, resource allocation, container orchestration, quality of service (QoS)

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