Architecture, software implementation and results analyzing of the usage an intelligent tool for configuring microservice applications

Abstract

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.