Architecture, software implementation and results analyzing of the usage an intelligent tool for configuring microservice applications - Zinov’ev, Dmytro; Tkachuk, Mykola (Bulletin of V.N. Karazin Kharkiv National University, series «Mathematical modeling. Information technology. Automated control systems», 2025)
Actuality.  Developing applications with a microservice architecture requires effective configuration management under varying load conditions, reliability, fault tolerance, and scalability requirements. This creates a need for intelligent adaptive configuration tools that can operate in near-real time mode.Goal. To create an intelligent tool for adaptive management of MCA configurations with a decision-making module based on Case-Based Reasoning (CBR), design its architecture, make a software implementation, as well as experimentally evaluate the work on a test site and compare several CBR methods.Research methods. The basic concepts of MSA configuration processes are clarified; a polygon with three services (auth, product, order) and performance requirements (≤1000 simultaneous requests, average latency ≤200 ms) is designed. Adaptive microservice configuration management is implemented as a microservice with REST API (FastAPI) and a precedent database (PostgreSQL); QoS, resource, "cost" and adaptability metrics are used. Five CBR methods are investigated: K-Nearest Neighbors, Weighted KNN, Feature-Based Retrieval, Cluster-Based Retrieval, Indexing & Hashing. A series of measurements of configuration selection time for a precedent database of 50–1000 records with averaging over 100 runs is conducted.Results.  The subsystem correctly identifies states and applies relevant configurations for different scenarios (low/medium/high/peak), meeting the requirement of a matching time of ≤0.5 s. The Indexing & Hashing method demonstrated the highest performance (≈27.6–50.3 ms for 50–1000 precedents); KNN has a linear time growth, and Weighted KNN provides controllability due to metric weights. The implemented web interface provides monitoring and manual/automatic mode of applying changes in real time.Conclusions. The proposed architecture and software implementation of the CBR tool confirm the practical feasibility of adaptive configuration of the MCA and create a basis for managed solutions that are scaled by data. Further directions are outlined: evolution of the case base with online learning, multi-criteria optimization (performance/reliability/cost/energy efficiency), deeper integration with orchestrators and service mesh and increased explainability of solutions.