MICROSERVICE ARCHITECTURE OF A MICROSOFT AZURE-BASED CLOUD PLATFORM FOR INTELLIGENT CONTROL OF COMPUTER-INTEGRATED ROBOTIC SYSTEMS
DOI:
https://doi.org/10.28925/2663-4023.2026.34.1328Keywords:
microservices architecture; Microsoft Azure; industrial automation; computer-integrated systems; cloud computing; Markov chains; queueing theory; Circuit Breaker; DevOps.Abstract
The article is dedicated to the development and study of a microservices-based cloud platform architecture built on Microsoft Azure for managing computer-integrated robotic systems (CIRS). The problems of scaling manufacturing information systems for processing large volumes of shop-floor data are considered, and the feasibility of applying microservices architecture and cloud technologies to ensure flexibility and reliability of production processes is substantiated. A review of recent research on cloud and edge computing for industry is carried out, and the previously unsolved part of the general problem is identified — the absence of comprehensive CIRS management platforms with quality-of-service, reliability and security guarantees. An extended mathematical model is developed, including latency analysis based on queueing theory (M/M/1 and M/M/c models), a reliability model based on continuous-time Markov chains, a load distribution optimization problem between cloud and edge nodes solved by linear programming methods, and a model of cloud resource energy efficiency. The methodology of experimental verification using a cluster of Azure virtual machines and the k6 load generator is described. The results present the five-level architecture of the developed platform, covering the device, edge computing, processing, data storage, and presentation layers; six key platform microservices, their API contracts, and architectural patterns (Circuit Breaker, Saga, CQRS, Event Sourcing) are described in detail; the security system based on OAuth 2.0, mTLS, and the STRIDE threat model is characterized, as well as the CI/CD pipeline for automated blue-green deployment. Experimental results demonstrate the effectiveness of the proposed approach: latencies are within 50–150 ms, throughput scales linearly up to 3800 requests per second, and the availability coefficient equals 0.9987, which agrees well with the calculations of the Markov model. Conclusions and directions for further research are formulated, in particular predictive equipment maintenance based on machine learning and integration with digital twin platforms.
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