On the Performance of Cloud Services and Databases for Industrial IoT Scalable Applications

Abstract

In the Industry 4.0 the communication infrastructure is derived from the Internet of Things (IoT), and it is called Industrial IoT or IIoT. Smart objects deployed on the field collect a large amount of data which is stored and processed in the Cloud to create innovative services. However, differently from most of the consumer applications, the industrial scenario is generally constrained by time-related requirements and its needs for real-time behavior (i.e., bounded and possibly short delays). Unfortunately, timeliness is generally ignored by traditional service provider, and the Cloud is treated as a black box. For instance, Cloud databases (generally seen as “Database as a service”—DBaaS) have unknown or hard-to-compare impact on applications. The novelty of this work is to provide an experimental measurement methodology based on an abstract view of IIoT applications, in order to define some easy-to-evaluate metrics focused on DBaaS latency (no matter the actual implementation details are). In particular, the focus is on the impact of DBaaS on the overall communication delays in a typical IIoT scalable context (i.e., from the field to the Cloud and the way back). In order to show the effectiveness of the proposed approach, a real use case is discussed (it is a predictive maintenance application with a Siemens S7 industrial controller transmitting system health status information to a Cloudant DB inside the IBM Bluemix platform). Experiments carried on in this use case provide useful insights about the DBaaS performance: evaluation of delays, effects of involved number of devices (scalability and complexity), constraints of the architecture, and clear information for comparing with other implementations and for optimizing configuration. In other words, the proposed evaluation strategy helps in finding out the peculiarities of Cloud Database service implementations.

A proof of concept application of sensing technologies for managing proximity hazards on construction sites

“…If a suitable communication infrastructure (e.g., Wi-Fi or LTE) is available, the smartphone could act as a gateway and forward position data via MQTT (Message Queue Telemetry Transport) to a cloud backend in charge of all the computation. The performance difference between a full cloud or a mixed cloud-edge architecture has been analysed in [17] [18].…”

Study of latencies in ThingSpeak

“…He measured the roundtrip times of mobile and non-mobile devices, connected through wireless (WiFi and 3G) and wired (Ethernet) interfaces, while pinging five different AWS servers spread around the world. In [17], the author and his team studied the performance of a cloud database by measuring the time needed to complete a writing on the database and getting back a reaction. They used a Siemens PLC to generate data, the IBM Cloud to store data and fire events, and an industrial computer to catch those events, all connected through a MQTT broker.…”

Emerging Internet of Things Solutions and Technologies

“…Unfortunately, timeliness is generally ignored by traditional service providers, and the cloud is treated as a black box, including "database as a service" (DBaaS). The work reported in [5] provides an experimental measurement methodology based on an abstract view of industrial IoT (IIoT) applications, in order to define some easy to evaluate metrics focused on DBaaS latency. In particular, the focus is on the impact of DBaaS on the overall communication delays in a typical IIoT scalable context.…”