A Practical way to Handle Service Migration of ML-based Applications in Industrial Analytics

Nowadays, Machine learning (ML) plays a significant role in Industrial Analytics. It enables predictive analytics, and helps uncovering essential insights to transform industries. As a result, real-time data analytics has become an essential requirement for industrial engineering jobs. Edge computing enables local intelligence and real-time analytics that are key for industry processes to take autonomous decisions locally at the edge of the network. However, outages in edge datacenters can jeopardize the whole plant security. In this paper, we proposed a practical approach to effectively handling service and data migration of ML-based applications in Industrial Analytics scenarios in the presence of a lack of computing resources at the edge. We argue that in this context the value of data is inversely proportional to their age and is very important to work with fresher data. In this paper, we describe our architectural approach for service and data handoff and show a predictive diagnostics case study deployed in an edge-enabled IIoT infrastructure. We evaluate our proposed approach in terms of drop of accuracy in a well-known edge computing emulator, i.e., openLEON. The experimental results show the benefit of our solution with respect to standard techniques.