iRUC: Reducing Inter-Microservice Data Communication in Data-Intensive Systems via Unified Computation

In data-intensive microservice-based systems, frequent and large-scale inter-service communication poses a critical performance bottleneck, degrading throughput and escalating latency. Existing solutions exhibit notable limitations: microservice merging sacrifices loose coupling and system evolvability; resource-aware scheduling enhances communication efficiency but fails to reduce data volume; dynamic deployment reduces network distances while introducing compute-resource contention; and unnecessary data transfer elimination remains ineffective under massive data loads. Hence, to overcome these challenges, we propose iRUC, an approach forinter-service data communicationReduction viaUnifiedComputation. In particular, we first designGraphQL+, an executable declarative language that extends GraphQL with service-interaction semantics to achieve unified, cross-language modeling of data processing and transmission across microservices. Second, we develop anLLM-based multi-agent systemleveraging Claude 4.5 Sonnet and Gemini 2.5 Pro to automatically parse microservice code and synthesize corresponding GraphQL+ models. Third, we implement the unifiedexecution enginefor GraphQL+ models, including the database gateway that preserves microservice database autonomy while enabling cross-database queries. This design enables iRUC to perform the unified modeling and execution of data processing and transmission across microservices, thereby significantly reducing inter-service data transfer while maintaining microservice independence. Experimental evaluation on nine GitHub open-source microservice projects deployed on Huawei Cloud demonstrates iRUC’s effectiveness: compared with the unnecessary transfer elimination, dynamic deployment, and serverless computing approaches, iRUC improves throughput by 5.57×, 1.52×, and 1.87×, respectively, while reducing latency to 7.7%, 40.7%, and 37.4% of those approaches. These results show that iRUC achieves significant performance improvements in large-scale data processing scenarios.