Adaptive Stream-based Shifting Bottleneck Detection in IoT-based Computing Architectures

Abstract

Cloud computing is revolutionizing the backbone of data analysis applications, including industrial ones. One of its main pillars is the separation of the logic with which data is accessed (e.g., to study the efficiency of a manufacturing system) from the actual hardware (e.g., server) that maintains and analyses the data. Large distributed cyber-physical systems enabled by, among other technologies, the Internet of Things (IoT), made nonetheless clear that "what to do" with the data and "where to do it" are not disjoint problems; i.e., cloud computing on its own is not enough. Fog and edge computing have emerged as complementary options, to distribute the analysis, helping with challenges by means of close-to-the-source data analysis. We show for a key problem for industrial processes, that of shifting bottleneck detection, how to take advantage of such multi-tier computing architectures, to perform continuous and configurable analysis of data from Manufacturing Execution Systems. We propose a processing framework, STRATUM, and an algorithm, AMBLE, for continuous, data stream processing. STRATUM seamlessly distributes and parallelizes the processing across the tiers and AMBLE guarantees consistent analysis in spite of timing fluctuations, which are commonly introduced due to e.g. the communication system; it also achieves efficiency through appropriate data structures for in-memory processing. The experimental study on a real-world dataset, taken from a production line over two years and including 8.5 million entries, shows the benefits of the proposed solution in enabling configurable and efficient analysis.