We address the problem of synthesizing safety-critical embedded and cyber-physical system architectures to minimize a cost function while guaranteeing the desired reliability. We represent a system architecture as a configurable graph in which both the nodes (components) and edges (interconnections) may fail. We then propose a compact analytical formalism to efficiently reason about the reliability of the overall system based on the failure probabilities of the components, and provide expressions of the design constraints that avoid exhaustive enumeration of failure cases on all possible graph configurations. Based on these constraints, we cast the synthesis problem as an optimization problem and propose monolithic and iterative optimization schemes to decrease the problem complexity. We implement the proposed algorithms in the ArchEx framework, leveraging a pattern-based specification language to facilitate problem formulation. Design problems from aircraft electric power distribution networks and reconfigurable industrial manufacturing systems illustrate the effectiveness of our approach.

Deep learning (DL) algorithms have already proved their effectiveness on a wide variety of application domains, including speech recognition, natural language processing, and image classification. To foster their pervasive adoption in applications where low latency, privacy issues and data bandwidth are paramount, the current trend is to perform inference tasks at the edge. This requires deployment of DL algorithms on low-energy and resource-constrained computing nodes, often heterogenous and parallel, that are usually more complex to program and to manage without adequate support and experience. In this paper, we present ALOHA, an integrated tool flow that tries to facilitate the design of DL applications and their porting on embedded heterogenous architectures. The proposed tool flow aims at automating different design steps and reducing development costs. ALOHA considers hardware-related variables and security, power efficiency, and adaptivity aspects during the whole development process, from pre-training hyperparameter optimization and algorithm configuration to deployment.

Cyber-Physical Systems (CPS) are embedded computational collaborating devices, capable of sensing and controlling physical elements and, often, responding to humans. Designing and managing systems able to respond to different, concurrent requirements during operation is not straightforward, and introduce the need of proper support at design-time and run-time. The Cross-layer modEl-based fRamework for multi-oBjective dEsign of Reconfigurable systems in unceRtain hybRid envirOnments (CERBERO) EU project has developed a design environment for adaptive CPS. CERBERO approach leverages on model-based methodologies including different technologies and tools developed to cover design and operation from user interactions down to low level computing layer implementation.

Model-based design methodologies are commonly used in industry for the development of complex cyber-physical systems (CPSs). There are many different languages, tools, and formalisms for model-based design, each with its strengths and weaknesses. Instead of accepting some weaknesses of a particular tool, an alternative is to embrace heterogeneity, and to develop tool integration platforms and protocols to leverage the strengths from different environments. A fairly recent attempt in this direction is the functional mock-up interface (FMI) standard that includes support for co-simulation. Although this standard has reached acceptance in industry, it provides only limited support for simulating systems that mix continuous and discrete behavior, which are typical of CPS. This paper identifies the representation of time as a key problem, because the FMI representation does not support well the discrete events that typically occur at the cyber-physical boundary. We analyze alternatives for representing time in hybrid co-simulation and conclude that a superdense model of time using integers only solves many of these problems. We show how an execution engine can pick an adequate time resolution, and how disparities between time representations internal to co-simulated components and the resulting effects of time quantization can be managed. We propose a concrete extension to the FMI standard for supporting hybrid co-simulation that includes integer time, automatic choice of time resolution, and the use of absent signals. We explain how these extensions can be implemented modularly within the frameworks of existing simulation environments.

Given a large variety of resources and billing contracts offered by today’s cloud providers, customers face a nontrivial optimization challenge for their application workloads. A number of works are dealing with either billing contracts selection optimization or resource types selection. We argue that the largest cost savings to elastic workloads result from jointly optimizing heterogeneous resources and billing contracts selection. To this end, we introduce a novel cloud control and management framework and formulate a novel optimization problem called Heterogeneous Resource Reservation (HRR). We evaluate our solution through a thorough simulation study using publicly available cloud workload data as well as internal anonymous customer data. For these data our approach attain dramatic cost savings compared to the current state of the art.

No abstract available.

In the last few years, besides the concepts of embedded and interconnected systems, also the notion of Cyber-Physical Systems (CPS) has emerged: embedded computational collaborating devices, capable of sensing and controlling physical elements and, often, responding to humans. The continuous interaction between physical and computing layers makes their design and maintenance extremely complex. Uncertainty management and runtime reconfigurability, to mention the most relevant ones, are rarely tackled by available toolchains. In this context, the Cross-layer modEl-based fRamework for multi-oBjective dEsign of Reconfigurable systems in unceRtain hybRid envirOnments (CERBERO) EU project aims at developing a design environment for CPS based of two pillars: 1) a cross-layer model-based approach to describe, optimize, and analyze the system and all its different views concurrently and 2) an advanced adaptivity support based on a multi-layer autonomous engine. In this work, we describe the components and the required developments for seamless design of reusable and reconfigurable CPS and System of Systems in uncertain hybrid environments.

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.

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.

Creation and management of large complex systems of systems (SoS) can be a daunting task for even the most experienced engineers. Architecting these systems requires considerable domain expertise and next generation tools to seamlessly link requirements elicitation, through to modeling and simulation, and finally through to implementation. The resulting SoS can be so complex that it is virtually impossible for a single person to understand all its interactions. An important aid to this process is the use of architecture patterns that provide blueprints for proven elements of a system that can be incorporated into the evolution of a system or service. This paper describes a novel approach in validating architecture patterns during the early phases of service or product design and development. We propose an architecture optimization method called Concise Modeling to be used in conjunction with architecture patterns to expedite SoS architecture exploration and analysis.

The past two decades have seen significant improvements in optimization modeling and software solvers for large-scale optimization problems, especially discrete problems. We feel that a critical feature of many of these systems is being overlooked. That is, the process control engineer adjusts process parameters while only considering the local efficiency or not considering process efficiency at all. Production control engineers, while optimizing the global system performance, consider process parameters as given and fixed, i.e., unchangeable. Combining the optimization of the process parameters with a global system view can significantly improve the overall system performance. In practice, "hot jobs" are treated in this ad hoc manner, making sure that all resources are available and operate at peak efficiency (minimum production time) for these critical products. This phenomenon occurs not only in manufacturing but also in many other industries. This modeling part of the optimization problem can be even more important than "optimal versus heuristic"-based solution decisions made. In this paper, we present an aggregative high-fidelity modeling approach and illustrate the formulation of parameter variability in three different domains: manufacturing, air travel, and food processing.