The INFN-CNAF computing center, one of the Worldwide LHC Computing Grid Tier-1 sites, is serving a large set of scientific communities, in High Energy Physics and beyond. In order to increase efficiency and to remain competitive in the long run, CNAF is launching various activities aiming at implementing a global predictive maintenance solution for the site. This requires a site-wide effort in collecting, cleaning and structuring all possibly useful data coming from log files of the various Tier-1 services and systems, as a necessary step prior to designing machine learning based approaches for predictive maintenance. Among the Tier-1 services, efficient storage systems are one of the key ingredients of Tier-1 operations. CNAF uses the StoRM service as a Grid Storage Resource Manager solution: its operations are logged in a very complex manner, as the log content is deeply unstructured and hard to be exploited for analytics purposes. Despite such difficulty, the StoRM logs are a precious source of information for operators (e. g. real-time monitoring and anomaly detection), for developers (e. g. debugging, service stability, code improvements) and for site managers (service optimization, storage usage efficiency, time and money saving ways to spot and prevent unwanted behaviors). Based on previous experiences on Big Data Analytics and Machine/Deep learning in the CMS experiment, this work describes how the StoRM logs can be handled and parsed to extract the relevant information, how such log handling can be designed to work automatically, how to define and implement metrics to tag critical states of the service, how to correlate StoRM events with external services events, and ultimately how to contribute to the future CNAF-wide predictive maintenance system. Initial results in this activity are presented and discussed. Furthermore, a mention to ongoing complementary work at the CNAF center is also mentioned.

With the increase of the volume of data produced by IoT devices, there is a growing demand of applications capable of elaborating data anywhere along the IoT-to-Cloud path (Edge/Fog). In industrial environments, strict real-time constraints require computation to run as close to the data origin as possible (e.g., IoT Gateway or Edge nodes), whilst batch-wise tasks such as Big Data analytics and Machine Learning model training are advised to run on the Cloud, where computing resources are abundant. The H2020 IoTwins project leverages the digital twin concept to implement virtual representation of physical assets (e.g., machine parts, machines, production/control processes) and deliver a software platform that will help enterprises, and in particular SMEs, to build highly innovative, AI-based services that exploit the potential of IoT/Edge/Cloud computing paradigms. In this paper, we discuss the design principles of the IoTwins reference architecture, delving into technical details of its components and offered functionalities, and propose an exemplary software implementation.

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The concept of digital twins has growing more and more interest not only in the academic field but also among industrial environments thanks to the fact that the Internet of Things has enabled its cost-effective implementation. Digital twins (or digital models) refer to a virtual representation of a physical product or process that integrate data from various sources such as data APIs, historical data, embedded sensors and open data, giving to the manufacturers an unprecedented view into how their products are performing. The EU-funded IoTwins project plans to build testbeds for digital twins in order to run real-time computation as close to the data origin as possible (e.g., IoT Gateway or Edge nodes), and whilst batch-wise tasks such as Big Data analytics and Machine Learning model training are advised to run on the Cloud, where computing resources are abundant. In this paper, the basic concepts of the IoTwins project, its reference architecture, functionalities and components have been presented and discussed.