Microservice resource management aims to ensure stable service instance loads and improve overall resource utilization through load balancing, elastic scaling, and container orchestration while meeting system service quality requirements.Existing research often focuses on localized solutions, addressing only single aspects of elastic scaling or load balancing without recognizing the systemic nature of microservice resource management.The complexity and dynamism of service dependencies make it challenging to quantify interactions between resource strategies and service loads.Additionally, microservice systems experience dynamic load variations influenced by user behavior, business activities, and external factors.The dynamic nature of load changes and the selection of load features significantly increase the difficulty of resource forecasting, further complicating microservice resource management.To address this problem, this paper proposes MDRM (MAPEbased Dynamic Resource Management), a dynamic optimization method that integrates load balancing and elastic scaling to overcome the limitations of isolated strategies.MDRM models system load based on business characteristics and service invocation relationships, accurately capturing dynamic variations.A composite model, combining parallel multi-layer CNNs and LSTMs, extracts spatiotemporal microservice features, enhancing resource forecasting accuracy.Additionally, MDRM formulates a comprehensive load balancing optimization function that synergizes with resource utilization and service response time objectives to generate optimal management strategies.Experimental results demonstrate that, compared to default resource management strategies in Docker Swarm and Kubernetes, MDRM significantly improves system throughput (approximately 1000 RPS) and reduces response time (approximately 30-40 ms), proving its effectiveness.

Modern enterprises are increasingly adopting microservice architectures to enhance system flexibility and scalability. However, in the face of ever-changing business requirements, the relationships between system components have become increasingly complex, resulting in significant challenges in maintaining system robustness. In recent years, multimodal data-driven approaches based on graph neural networks have emerged as a predominant solution for root cause localization in microservice systems. Our detailed analysis of architectural characteristics and existing research reveals two critical limitations. First, simple graph is insufficient to represent the one-to-many relationships inherent in microservice component interactions, such as deployment, subordinate, and dependency. Second, the current multimodal data-based method has difficulty in performing localization on faults occurring on hosts, services, and instances at the same time.To address these challenges, we propose HyperRCA, a novel multi-granular root cause analysis approach based on hypergraph neural networks. Our approach models system states during faults via a hypergraph with instances as graph nodes, explicitly capturing heterogeneous relationships through three innovative hyperedge designs: deployment hyperedges for infrastructure relationships, subordinate hyperedges for service hierarchies, and dependency hyperedges for inter-component interactions. We used hypergraph neural networks and multi-layer perceptrons to train a root cause localization model based on hyperedge features to achieve multi-granularity root cause localization. Experimental evaluations demonstrate significant performance improvements over state-of-the-art approaches. HyperRCA achieves a maximum HR@5 improvement of 112.62% on single-granularity datasets and 466.43% in multi-granularity scenarios.