Abstract An execution trace is a model of a single system behavior. Execution traces occur everywhere in the system’s lifecycle as they can typically be produced by executable models, by prototypes of (sub)systems, and by the system itself during its operation. An execution trace can be visualized and analyzed with various techniques, providing insight into the dynamic behavior, performance, bottlenecks, etc., of the system. In this paper, we present the Trace tool of the Eclipse Trace4cps project for the visualization and analysis of execution traces. A prominent application is the trace-based performance engineering of embedded or cyber-physical systems. Performance is an important system quality, as it can give a competitive advantage. Reasoning about system-level performance in such systems, however, is hard due to its cross-cutting nature. We show how the Trace tool can support this by various examples. Performance engineering is not the only application of the Trace tool, however: it supports system analysis in a wide range of situations.

Product family development facilitates reuse across all phases of systems engineering; in case of model-based systems engineering, this reuse involves the models as well. Introducing a model-based way of working is challenging, especially for product family development. This paper describes a case of introducing a modelbased way of working in brown-field product family development. We explain how we developed a master model, i.e. a library of model elements, to predict and optimize the productivity of a family of industrial production systems. Using this master model, we construct models of existing and yet-to-be-developed product family members by configuring and combining the appropriate library elements. We use system and model execution traces to validate the productivity models. For this, we developed a master transformation, i.e. a library of execution trace transformation rules, to unify system and model execution traces. Besides the master model and the master transformation, we present lessons learned regarding the introducing a model-based way of working. This proves both technically and organizationally complex, especially for brown-field product family development, but besides the intended prediction and optimization, it brings benefits with respect to capturing domain knowledge and system validation.

System-Performance Engineering (SysPE) encompasses modeling formalisms, methods, techniques, and industrial practices to design systems for performance, where performance is taken integrally into account during the whole system life cycle. Industrial SysPE state of practice is generally model-based. Due to the rapidly increasing complexity of systems, there is a need to develop and establish model-driven methods and techniques. To structure the field of SysPE, we identify (1) industrial challenges motivating the importance of SysPE, (2) scientific challenges that need to be addressed to establish model-driven SysPE, (3) important focus areas for SysPE and (4) best practices. We conducted a survey to collect feedback on our views. The responses were used to update and validate the identified challenges, focus areas, and best practices. The final result is presented in this paper. Interesting observations are that industry sees a need for better design-space exploration support, more than for additional performance modeling and analysis techniques. Also tools and integral methods for SysPE need attention. From the identified focus areas, scheduling and supervisory control is seen as lacking established best practices.

Industrial embedded platforms are often used to execute stream-processing applications, from which the results are used by actuators. On average, these stream-processing applications should at least meet the required throughput of their actuators, which poses a real-time requirement on the system. To avoid extra costs and delays, it is desired to estimate during the early design phase if a combination of an embedded platform and a stream-processing application can achieve the required throughput. The throughput of a stream-processing application executed on different embedded platforms can be predicted by modeling them using static or measurement based analysis. However, during the early design phase it can be desirable to have a model that allows a large set of embedded platforms to be considered, where embedded platforms with predictive instructions are supported. This paper presents a gray-box approach applicable during the early design phase to perform cross-platform throughput predictions for industrial stream-processing applications and their embedded platforms. A three step regression-based approach is presented, which uses an expression based on Amdahl's law for the discrete scaling of workload over cores and a large database with CPU performance scores to perform cross-platform throughput predictions. Validation, with a limited set of platforms, showed the usability of the approach. The pragmatic approach is based on a prototype industrial digital image processing application for a printer from Océ, which is also used to present the approach.

The pre-build development of warehouse systems leads from a specific customer request to a specific customer quotation. This involves a process of configuring a warehouse system using a sequence of steps that contain increasingly more details. Simulation is a helpful tool in analyzing warehouse design alternatives, but setting up a detailed simulation is too expensive early in the development process. We show that configurable simulation models can be applied early in the development process with a good cost/benefit ratio. We present a warehouse simulation model that can be configured with customer and topology information and decision algorithms. We show that the simulation results are similar to those of detailed simulations while a warehouse simulation can be configured with little effort and the simulations run fast enough to support sensitivity analysis and design-space exploration.

This paper provides an overview of the architecture for self-organizing, co-operative and robust Building Automation Systems (BAS) proposed by the EC funded FP7 SCUBA1project. We describe the current situation in monitoring and control systems and outline the typical stakeholders involved in the case of building automation systems. We derive seven typical use cases which will be demonstrated and evaluated on pilot sites. From these use cases the project designed an architecture relying on six main modules that realize the design, commissioning and operation of self-organizing, co-operative, robust BAS.