Lev Greenberg

Determinate composition of FMUs for co-simulation

In this paper, we explain how to achieve deterministic execution of FMUs (Functional Mockup Units) under the FMI (Functional Mockup Interface) standard. In particular, we focus on co-simulation, where an FMU either contains its own internal simulation algorithm or serves as a gateway to a simulation tool. We give conditions on the design of FMUs and master algorithms (which orchestrate the execution of FMUs) to achieve deterministic co-simulation. We show that with the current version of the standard, these conditions demand capabilities from FMUs that are optional in the standard and rarely provided by an FMU in practice. When FMUs lacking these required capabilities are used to compose a model, many basic modeling capabilities become unachievable, including simple discrete-event simulation and variable-step-size numerical integration algorithms. We propose a small extension to the standard and a policy for designing FMUs that enables deterministic execution for a much broader class of models. The extension enables a master algorithm to query an FMU for the time of events that are expected in the future. We show that a model can be executed deterministically if all FMUs in the model are either memoryless or implement one of rollback or step-size prediction. We show further that such a model can contain at most one “legacy” FMU that is not memoryless and provides neither rollback nor step-size prediction.

Requirements for hybrid cosimulation standards

This paper defines a suite of requirements for future hybrid cosimulation standards, and specifically provides guidance for development of a hybrid cosimulation version of the Functional Mockup Interface (FMI). A cosimulation standard defines interfaces that enable diverse simulation tools to interoperate. Specifically, one tool defines a component that forms part of a simulation model in another tool. We focus on components with inputs and outputs that are functions of time, and specifically on mixtures of discrete events and continuous time signals. This hybrid mixture is not well supported by existing cosimulation standards, and specifically not by FMI 2.0, for reasons that are explained in this paper. The paper defines a suite of test components, giving a mathematical model of an ideal behavior, plus a discussion of practical implementation considerations. The discussion includes acceptance criteria by which we can determine whether a standard supports definition of each component. In addition, we define a set of test compositions that define requirements for coordination between components, including consistent handling of timed events.

Pluggable Analysis Viewpoints for Design Space Exploration

Abstract Viewpoint modeling is an effective approach for analyzing and designing complex systems. Splitting various elements and corresponding constraints into different perspectives of interests, enables separation of concerns such as domains of expertise, levels of abstraction, and stages in lifecycle. Specifically, in Systems Engineering different viewpoints could include functional requirements, physical architecture, safety, geometry, timing, scenarios, etc. Despite partial interdependences, the models are usually developed independently by different parties, using different tools and languages. However, the essence of Systems Engineering requires repetitive integration of many viewpoints in order to find feasible designs and to make good architectural decisions, e.g., in each mapping between consecutive levels of abstraction and in each design space exploration. This integration into one consistent model becomes a significant challenge from both modeling and information management perspectives. In this paper we suggest (1) a unique modular algebraic viewpoint representation robust to design evolution and suitable for generation of the integrated optimization/analysis models, and (2) an underlying ontology-based approach for consistent integration of local viewpoint concepts into the unified design space model. We show an example of an optimization model with different combinations of partially interdependent Analysis Viewpoints. Using the proposed modeling and information management approaches the underlying viewpoints equations can be applied without modification, making the approach pluggable.

Big data analysis of cloud storage logs using spark

We use Apache Spark analytics to investigate the logs of an operational cloud object store service to understand how it is being used. This investigation involves going over very large amounts of historical data (PBs of records in some cases) collected over long periods of time retroactively. Existing tools, such as Elasticsearch-Logstash-Kibana (ELK), are mainly used for presenting short-term metrics and can-not perform advanced analytics such as machine learning. A possible solution is to save for long periods only certain aggregations or calculations produced from the raw log data, such as averages or histograms, however these must be decided in advance, and cannot be changed retroactively since the raw data has already been discarded. Spark allows us to gain insights going over historical data collected over long periods of time and to apply the historical models on online data in a simple and efficient way.

Using a High-Level Test Generation Expert System for Testing In-Car Networks

The rising size and complexity of in-car networks call for more advanced and scalable verification solutions. We propose a verification methodology for in-car networks based on a system level test generator tool used for creating massive random biased stimuli, and on coverage and checking monitors. The test generator is an expert system based on an ontology of testing knowledge. A significant challenge is the continuous nature of the stimuli needed to represent the physical environment and the state of the internal components controlled by the vehicles electronic systems. We report on applying our methodology to an example in-car network simulator.

Development and Verification of Complex Hybrid Systems Using Synthesizable Monitors

Using simulation monitors that are formally defined and automatically synthesized is already part of the standard methodology of hardware design and verification. However, this is not yet the case in the domain of systems engineering for cyber-physical systems. The growing trend towards model-based systems engineering is making the use of simulation monitors more relevant and possible. Recent related work focuses almost exclusively on the aspects of requirements specification. In this work, we explain how monitors can play a much more pervasive role in systems engineering, going beyond merely checking requirements. We describe how monitors can be used along the entire product lifecycle, from early design alternative analysis to final field testing. This work also covers the special considerations that must be addressed when designing a monitor specification language, specifically in the context of systems engineering. Our focus is on the practical issues related to the use of monitors and describes a prototype monitor specification and synthesis platform applied to the hybrid simulation of an automotive subsystem.

A new test-generation methodology for system-level verification of production processes

The continuing growth in the complexity of production processes is driven mainly by the integration of smart and cheap devices, such as sensors and custom hardware or software components. This naturally leads to higher complexity in fault detection and management, and, therefore to a higher demand for sophisticated quality control tools. A production process is commonly modeled prior to its physical construction to enable early testing. Many simulation platforms were developed to assess the widely varying aspects of the production process, including physical behavior, hardware-software functionality, and performance. However, the efficacy of simulation for the verification of modeled processes is still largely limited by manual operation and observation. We propose a massive random-biased, ontology-based, test-generation methodology for system-level verification of production processes. The methodology has been successfully applied for simulation-based processor hardware verification and proved to be a cost-effective solution. We show that it can be similarly beneficial in the verification of production processes and control.

System simulation from operational data

System simulation is a valuable tool to unveil inefficiencies and to test new strategies when implementing and revising systems. Often, simulations are parameterized using offline data and heuristic knowledge. Operational data, i.e., data gained through experimentation and observation, can greatly improve the fidelity between the actual system and the simulation. In a traffic scenario, for example, different road conditions or vehicle types can impact the outcome of the simulation and have to be considered during the modeling stage. This paper proposes using machine learning techniques to generate high fidelity simulation models. A traffic simulation case study exemplifies this approach by generating a model for the SUMO traffic simulator from vehicular telemetry data.