MAIA: A Microservices-based Architecture for Industrial Data Analytics
This addresses the problem of efficient and scalable data analytics for industrial manufacturers shifting to decentralized production, though it is incremental as it adapts existing microservices concepts to this domain.
The paper tackles the challenge of applying decentralized architectures to industrial data analytics by proposing a microservices-based approach, with a prototype achieving less than 20ms end-to-end latency for predicting paths of 100 autonomous robots on commodity hardware.
In recent decades, it has become a significant tendency for industrial manufacturers to adopt decentralization as a new manufacturing paradigm. This enables more efficient operations and facilitates the shift from mass to customized production. At the same time, advances in data analytics give more insights into the production lines, thus improving its overall productivity. The primary objective of this paper is to apply a decentralized architecture to address new challenges in industrial analytics. The main contributions of this work are therefore two-fold: (1) an assessment of the microservices' feasibility in industrial environments, and (2) a microservices-based architecture for industrial data analytics. Also, a prototype has been developed, analyzed, and evaluated, to provide further practical insights. Initial evaluation results of this prototype underpin the adoption of microservices in industrial analytics with less than 20ms end-to-end processing latency for predicting movement paths for 100 autonomous robots on a commodity hardware server. However, it also identifies several drawbacks of the approach, which is, among others, the complexity in structure, leading to higher resource consumption.