Over the last few years, digital twins (DTs) have attracted increasing attention and uptake in both industry and academia. While several definitions exist for a DT, most of these focus on an exact virtual replica (often called the virtual entity (VE)) of a real-world object or process, which typically consists of several executable models interacting with each other. Furthermore, due to the connection and synchronization with their real-world physical counterpart, DTs evolve continuously across their lifecycle. Often, however, details of construction and internal structure of DTs are left un-or underspecified. Over time, both these factors (un(der)specification and real-time changes due to synchronization) might lead to misuse, undesirable behavior, or runtime issues, like errors, and performance problems. This hinders the (re)use of DTs and/or its components for the intended purpose or any other future purposes. In this paper, we propose a new approach that helps to overcome the above sketched issues. We do so, in a case-driven way, by addressing a DT of an autonomously driving truck, developed by several researchers over a longer period of time, and with input of several MSc and PhD students. As it turns out, this DT lacks overall complete documentation. We demonstrate how logging can be used to learn the actual runtime behavior of a DT and show how this behavior can differ from its intended behavior at design stage. We explore different passive model learning techniques, such as state merging and process mining, to automate the process of obtaining behavioral models of the DT. In addition, we showcase how the learned behavioral model of the DT can be analyzed further to detect underlying causes of perceived runtime issues in DTs.

Digital twins (DTs) are often defined as a pairing of a physical entity and a corresponding virtual entity (VE), mimicking certain aspects of the former depending on the use-case. In recent years, this concept has facilitated numerous use-cases ranging from design to validation and predictive maintenance of large and small high-tech systems. Various heterogeneous cross-domain models are essential for such systems, and model-driven engineering plays a pivotal role in the design, development, and maintenance of these models. We believe models and model-driven engineering play a similarly crucial role in the context of a VE of a DT. Due to the rapidly growing popularity of DTs and their use in diverse domains and use-cases, the methodologies, tools, and practices for designing, developing, and maintaining the corresponding VEs differ vastly. To better understand these differences and similarities, we performed a semi-structured interview research with 19 professionals from industry and academia who are closely associated with different lifecycle stages of digital twins. In this paper, we present our analysis and findings from this study, which is based on seven research questions. In general, we identified an overall lack of uniformity in terms of the understanding of digital twins and used tools, techniques, and methodologies for the development and maintenance of the corresponding VEs. Furthermore, considering that digital twins are software intensive systems, we recognize a significant growth potential for adopting more software engineering practices, processes, and expertise in various stages of a digital twin’s lifecycle.

Thermal management is gaining importance since it is a promising method for increasing the reliability and lifespan of mobile devices. Although the temperature can be decreased by reducing processor speed, one must take care not to increase the processing times too much; violations of deadline constraints must be prevented. This article focuses on the tradeoff between performance and device temperature. We first analyze this tradeoff and show how to determine the optimal lower bound for the maximum temperature for a given set of jobs with known workloads and deadlines. To do so, we use a thermal model, which describes how future decisions impact temperature dynamics. Then, we introduce a processor scheduling algorithm that computes the resource allocation that achieves this lower bound. Consequently, our algorithm finds the optimal resource allocation for the purpose of minimizing the maximum processor temperature for a set of jobs with known workloads and deadlines. Our experimental validation shows that our thermal management algorithm can achieve a reduction of up to 15°C (42%) of the maximum temperature when the workload is high, where a previously proposed method achieved a reduction of up to 10°C (25%). Another advantage of our method is that it decreases the variance in the temperature profile by 16% compared to previously proposed methods.

In this paper I address the divide that has emerged between the field of performance evaluation and the field of computer and communication system design. After looking back briefly on the history of the field, I subsequently reflect on the reasons why the field of performance evaluation has become so isolated. I then continue with a set of eight recommendations, based on my experience in performing projects with industry, that will help in reconnecting, and that will result in a better uptake of the newest techniques and tools in the field of design of computer and communication systems. Following these recommendations will probably push scientists a little out of their comfort zone, however, I feel the potential extra reward of seeing our work truly applied is more than worth it.

In recent years, digital twin (DT) technology has moved to the center of attention of many researchers and engineers. Commonly, a digital twin is defined based on a virtual entity (VE) that exhibits similar behavior to its physical counterpart, and that is coupled to this physical entity (PE). The VE thus forms a core part of any digital twin. While VEs may differ vastly—from ones based on simple simulation to high-fidelity virtual mirroring of the corresponding PE—they are typically composed of multiple models that may originate from multiple domains, address different aspects, and are expressed and processed using different tools and languages. Furthermore, the use of time series data—whether historical or real-time or both—from the PE distinguishes VEs from mere simulations. As a consequence of the modeling landscape complexity and the data aspect of VEs, the design of a digital twin and specifically of the VE as part of it represents several challenges. In this paper, we present our vision for the development, evolution, maintenance, and verification of such virtual entities for digital twins.

The Award is for outstanding work, recently published or in the public domain, which advances the analysis or modeling of thermal or thermomechanical effects in electronic equipment or components, including experiments aimed specifically at the validation of numerical models. The award is in the form of a plaque and a $1000 cash prize. The Award was established by the family and friends of Harvey ...

One of the key challenges in the design of a Safety-Critical Cyber-Physical Systems is Requirements Analysis. Current Requirements Analysis approaches range from informal, human-centered ones that are hard to automate, to formal approaches that often lack freedom of expression. Furthermore, most approaches are general-purpose and do not focus on a particular domain, which makes identifying the specific requirements of a given domain less trivial. To overcome these challenges, this paper presents aDSL, a Domain-Specific Language and toolset for Requirement Analysis of Safety-Critical Cyber-Physical Systems. The approach comprises a mixture of informal and formal elements to enable both automation and freedom of expression; a number of stakeholders introduce and negotiate about their requirements. The aDSL language is used to precisely, concisely and unambiguously describe all such requirements. We have validated aDSL, using simulation techniques and actors that represent the stakeholders, on a case in the agro-machines domain. The proposed approach allows the discovery of requirements in a semi-automatic way.

Predicting the device temperature is crucial for high performance mobile devices since a high temperature reduces the device reliability and lifetime, and increases the power dissipation per processing activity. For these reasons, thermal models are used to predict the temperature and schedule the workloads according to these predictions. This means that more accurate predictions can improve the reliability, lifetime and energy-efficiency of devices. We introduce two different generic methods to extend a thermal model to improve the prediction accuracy. The first method is to extend a thermal model with a Kalman filter. This approach enables a device to adapt to environmental changes more easily and to reduce the effect of noise by combining sensor data and dynamic behavior of the system. However, it assumes every random variable to be normally distributed. The second method is to extend a thermal model with a particle filter. In addition to the ability of adapting better to environmental changes, this approach enables a device to approximate any arbitrary distribution to reduce the effect of noise. Both methods are applicable to any dynamic thermal model to improve its prediction accuracy. Our experimental results show that the new methods indeed improve the prediction accuracy.

In this paper we study the applicability of generative adversarial networks (GANs) for the description and generation of workloads for data centers. GANs are advanced neural networks that can learn complex likelihood functions and can sample from them. The field of workload modeling is concerned with describing and generating realistic workloads for performance evaluation of computer systems, in this paper, specifically for data centers. The characterization of the workload of modern data centers is crucial in order to study the effect of changing workloads on the performance of such data centers. Previously, a number of statistical fitting techniques have been used to characterize data center workloads. This paper explores whether GANs are sufficiently capable to automatically learn such characterisations from multidimensional data sets. We describe the design and evaluation of a GAN, thereby using real-world data center traces. The learned model is evaluated by comparison to previously proposed fitting techniques. We find that the resulting GAN is very well able to reproduce a realistic data center workload. Furthermore, the approach does not require (a priori) knowledge or assumptions about the underlying models themselves, which can be seen as an advantage. It is shown that the learning approach does reach comparable quality to other fitting techniques, although still at much higher computational costs.

Most modern mobile embedded devices have the ability to increase their computational power typically at the cost of increased heat dissipation. This may result in temperatures above the design limit, especially if active cooling is inapplicable. Thus, it is necessary to consider processor temperature while scheduling tasks. This means estimating the change in temperature due to changed workload is crucial for high performance mobile embedded devices. To address this challenge, we first introduce a model to estimate the temperature and classify the system dependent model parameters. Then, to determine these parameters, we develop a new method, which can be applied on any mobile embedded device. The only requirement for our new method is learning the device characteristics by processing a certain task while recording the temperature with built-in sensors. Our results show that our method can achieve high accuracy within a short testing period.

We present a practical solution for deep packet inspection for IEC-104 SCADA traffic, which can be used in monitoring approaches to ensure the dependable operation of critical systems. We re-implement an outdated parser and extend it to also parse the content of individual IEC-104 packets and to extract information relevant for monitoring and securing the physical processes being controlled. The deep packet inspection framework Spicy was used for the implementation, which allows for easy extensibility in the future. To illustrate the feasibility of the proposed solution, the throughput obtained when using the parser in combination with the monitoring tool Zeek has been evaluated for traces of different lengths. The traces have been captured in an operating electrical distribution field station with a single RTU.

A Cyber-Physical System (CPS) comprises the integration of computation, software, networking, and physical processes. Consequently, CPS models extend traditional embedded system models with an increased support for hybrid and heterogeneous models, networking, time synchronization, and especially interoperability. To assist engineers in designing CPSs, we have developed aDSL, a Domain-Specific Language (DSL) that comes with fully-automated tool support and is tailored to interoperability of CPS. The aDSL tool support includes: (i) interactive model description with input validation; (ii) the computation of possible operation modes of subsystems and parts; and, (iii) checking the adherence to requirements for various design alternatives and finding the Pareto optimal designs given these requirements. Moreover, aDSL generates intuitive visualizations throughout the toolchain which help design engineers to better understand the implications of design decisions and communicate them to stakeholders. aDSL has been applied to an agricultural tractor-trailer system case study in which aDSL quickly evaluated 48 designs and rendered all the visualizations of the results.

In recent work, we proposed a new specification language for power management strategies as an extension to our AnyLogic-based simulation framework for the trade-off analysis of power and performance in data centres. In this paper, we study the quality of such advanced power management strategies based on both power and performance measurement data collected during system operation. These strategies take a wide variety of state variables into account. In order to ensure the quality of new strategies, they are studied for stability, efficiency, adaptability and robustness; these qualities will be formally defined. This paper presents an evaluation approach for these qualities for several power management strategies inspired by strategies presented in the literature (and extensions thereof). We show that the choice of power management strategy depends both on which qualities are given the highest priority and on the used state information. The new power management strategies show significant reductions in energy consumption in our case of up to 54% energy (compared to an “always on” strategy) for a typical data centre workload for a small 30-server cluster.

Data centres are playing a pivotal role in all cloud-based services (e-commerce, social networks, financial services, e-government, etc.). The performance of data centres is crucial for the acceptance of all these services by end-users. It is important to carefully design data centres with both performance and energy considerations in mind, as data centres are also known to use large amounts of electrical energy. For that purpose we have developed a modular simulation model (based on ANYLOGIC) that can be used to study performance-energy trade-offs in data centre design. Key to such studies is the availability of a workload model. In this paper we present a workload characterisation model and algorithm using modern-day data science techniques, building on top of JuPYTER NOTEBOOK and the PRoFiDo platform. We present the method and show its versatility on a case study with real-world traces of 20 million entries, provided by the Dutch company better. be.

Power distribution networks are often controlled using the communication protocol IEC 60870-5-104 (IEC-104). While a specification exists, not every device implementing this protocol, actually follows this specification. We present mealy104, a tool that infers finite-state automata from IEC-104 implementations and use it on a real device implementing IEC-104, comparing it to the protocol standard. We use the tool to show that implementations do deviate from the specification.

We present an online monitoring tool for SCADA systems based on the network monitor Bro, which can be used locally at field stations. The tool generates alerts when suspicious and erroneous commands and sensor readings are detected. It can hence been seen as a local Intrusion Detection System, as well as an safety enhancement. It maintains a model of the local system, which is updated with incoming packets containing sensor readings and commands. Focusing on the protocol IEC-104, a parser was developed and the packet content was directly fed into the system model. Adaptive policies are implemented in Bro, which formulate physical constraints and safety requirements and allow to check whether SCADA traffic complies to these rules in real time. A case study with a real IEC-104 traffic trace shows the feasibility of our approach.

When working with space systems the keyword is resources. For a satellite in orbit all resources are sparse and the most critical resource of all is power. It is therefore crucial to have detailed knowledge on how much power is available for an energy harvesting satellite in orbit at every time – especially when in eclipse, where it draws its power from onboard batteries. This paper addresses this problem by a two-step procedure to perform task scheduling for low-earth-orbit (LEO) satellites exploiting formal methods. It combines cost-optimal reachability analyses of priced timed automata networks with a realistic kinetic battery model capable of capturing capacity limits as well as stochastic fluctuations. The procedure is in use for the automatic and resource-optimal day-ahead scheduling of GomX-3, a power-hungry nanosatellite currently orbiting the earth. We explain how this approach has overcome existing problems, has led to improved designs, and has provided new insights.