While the digital twins paradigm has attracted the interest of several research communities over the past twenty years, it has also gained ground recently in industrial environments, where mature technologies such as cloud, edge and IoT promise to enable the cost-effective implementation of digital twins. In the industrial manufacturing field, a digital model refers to a virtual representation of a physical product or process that integrates data taken from various sources, such as application program interface (API) data, historical data, embedded sensor data and open data, and that is capable of providing manufacturers with unprecedented insights into the product’s expected performance or the defects that may cause malfunctions. The EU-funded IoTwins project aims to build a solid platform that manufacturers can access to develop hybrid digital twins (DTs) of their assets, deploy them as close to the data origin as possible (on IoT gateway or on edge nodes) and take advantage of cloud-based resources to off-load intensive computational tasks such as, e.g., big data analytics and machine learning (ML) model training. In this paper, we present the main research goals of the IoTwins project and discuss its reference architecture, platform functionalities and building components. Finally, we discuss an industry-related use case that showcases how manufacturers can leverage the potential of the IoTwins platform to develop and execute distributed DTs for the the predictive-maintenance purpose.

The growth in the number and types of cloud-based services offered to IT customers is supported by the constant entry of new actors in the market and the consolidation of disruptive technologies such as AI, Big Data and Micro-services. From the customer’s perspective, in a market landscape where the cloud offer is highly diversified due to the presence of multiple competing service providers, picking the service that best accommodate their specific needs is a critical challenge. Once the choice is made, so called “cloud orchestration tools” (orchestrators) are required to take care of the customer application’s life-cycle. While big players offer their customers proprietary orchestrators, in the literature quite a number of open-source initiatives have launched multi-cloud orchestrators capable of transparently managing applications on top of the most representative cloud platforms. In this paper, we propose TORCH, a TOSCA-based framework for the deployment and orchestration of cloud applications, both classical and containerised, on multiple cloud providers. The framework assists the cloud customer in defining application requirements by using standard specification models. Unlike other multi-cloud orchestrators, adopts a strategy that separates the provisioning workflow from the actual invocation of proprietary cloud services API. The main benefit is the possibility to add support to any cloud platforms at a very low implementation cost. In the paper, we present a prototypal implementation of TORCH and showcase its interaction with two different container-based cluster platforms. Preliminary performance tests conducted on a small-scale test-bed confirm the potential of TORCH.

A Digital Twin (DT) refers to a virtual representation or digital replica of a physical object, system, process, or entity. This concept involves creating a detailed, real-time digital counterpart that mimics the behavior, characteristics, and attributes of its physical counterpart. DTs have the potential to improve efficiency, reduce costs, and enhance decision-making by providing a detailed, real-time understanding of the physical systems they represent. While this technology is finding application in numerous fields, such as energy, healthcare, and transportation, it appears to be a key component of the digital transformation of industries fostered by the fourth Industrial revolution (Industry 4.0). In this paper, we present the research results achieved by IoTwins, a European research project aimed at investigating opportunities and issues of adopting DTs in the fields of industrial manufacturing and facility management. Particularly, we discuss a DT model and a reference architecture for use by the research community to implement a platform for the development and deployment of industrial DTs in the cloud continuum. Guided by the devised architectures’ principles, we implemented an open platform and a development methodology to help companies build DT-based industrial applications and deploy them in the so-called Edge/Cloud continuum. To prove the research value and the usability of the implemented platform, we discuss a simple yet practical development use case.

Currently in the cloud market resources are mainly allocated according to the fixed-price, direct selling model. Market principles such as the supply-demand rate are not taken into consideration by cloud providers. In the literature several market-based resource allocation models and algorithms have been proposed, showing that dynamic pricing models might result more convenient for both the providers and the consumers of cloud resources. In this paper, we discuss about the use of alternative auction-based mechanisms to sell the “residual” computing capacity, i.e., the capacity which the provider is not able to allocate through direct-selling. The design of a procurement market for computing resources is proposed: in such a market we devised an adaptive bidding strategy that suggests to the provider the right actions useful to attain its business objective. The resource overbooking mechanism is also proposed to overcome the problem of resource underutilization. A software simulator was implemented with the aim of testing and validating the proposed mechanisms. Results show that, by fine-tuning their own strategy, providers manage to pursue specific objectives.

Current markets of cloud resources are mainly based on a fixed pricing model. In the recent literature several market-based resource allocation models and algorithms have been proposed, showing that dynamic pricing models could result more profitable for both providers and consumers. In this paper we propose a market of resources where the demand and the offer of resources is matched in auction-based sales. Specifically, we looked at this market from the perspective of the provider, who needs a strategy to allocate at best their unused computing capacity. We proposed an adaptive strategy that, suitably customized to the provider's business objective, help them to maximize the revenue in the context of procurement auctions. Furthermore, the impact of resource overbooking onto the utilization level of cloud data centers has been analyzed by means of extensive simulations.