Sri Kanajan

Period optimization for hard real-time distributed automotive systems

The complexity and physical distribution of modern active-safety automotive applications requires the use of distributed architectures. These architectures consist of multiple electronic control units (ECUs) connected with standardized buses. The most common configuration features periodic activation of tasks and messages coupled with run-time priority-based scheduling. The correct deployment of applications on such architectures requires end-to- end latency deadlines to be met. This is challenging since deadlines must be enforced across a set of ECUs and buses, each of which supports multiple functionality. The need for accommodating legacy tasks and messages further complicates the scenario. In this work, we automatically assign task and message periods for distributed automotive systems. This is accomplished by leveraging schedulability analysis within a convex optimization framework to simultaneously assign periods and satisfy end-to-end latency constraints. Our approach is applied to an industrial case study as well as an example taken from the literature and is shown to be both effective and efficient.

Extensible and scalable time triggered scheduling

The objective of this paper is to present how to design a system that can accommodate additional functionality with either no changes to the design or adding architectural modules without changing the implementation of the legacy functionality. This objective is very relevant to industrial domains where an architecture is designed before the full range of functionalities to support is known. We focus on an important aspect of the design of automotive systems: the scheduling problem for hard real time distributed embedded systems. Two metrics are used to capture the design goals. The metrics are optimized subject to a set of constraints within a mathematical programming framework. The cost of modifying a legacy system is characterized at an electrical control unit (ECU) component level. Results obtained in automotive applications show that the optimization framework is effective in reducing development and re-verification efforts after incremental design changes.

A formal approach to fault tree synthesis for the analysis of distributed fault tolerant systems

Designing cost-sensitive real-time control systems for safety-critical applications requires a careful analysis of both performance versus cost aspects and fault coverage of fault tolerant solutions. This further complicates the difficult task of deploying the embedded software that implements the control algorithms on a possibly distributed execution platform (for instance in automotive applications). In this paper, we present a novel technique for constructing a fault tree that models how component faults may lead to system failure. The fault tree enables us to use existing commercial analysis tools to assess a number of dependability metrics of the system. Our approach is centered on a model of computation, Fault Tolerant Data Flow (FTDF), that enables the integration of formal verification techniques. This new analysis capability is added to an existing design framework, also based on FTDF, that enables a synthesis-based, correct-by-construction, design methodology for the deployment of real-time feedback control systems in safety critical applications.

Exploring Trade-off’s Between Centralized versus Decentralized Automotive Architectures Using a Virtual Integration Environment

The large variety of architectural dimensions in automotive electronics design, for example, bus protocols, number of nodes, sensors and actuators interconnections and power distribution topologies, makes architecture design task a very complex but crucial design step especially for OEMs. This situation motivates the need for a design environment that accommodates the integration of a variety of models in a manner that enables the exploration of design alternatives in an efficient and seamless fashion. Exploring these design alternatives in a virtual environment and evaluating them with respect to metrics such as cost, latency, flexibility and reliability provide an important competitive advantage to OEMs and help minimize integration risks later in the design cycle. In particular, the choice of the degree of decentralization of the architecture has become a crucial issue in automotive electronics. In this paper, we demonstrate how a rigorous methodology (platform-based design) and the Metropolis framework can be used to find the balance between centralized and decentralized architectures

Towards a Methodology for the Quantitative Evaluation of Automotive Architectures

Architecture design is a critical stage of the electronics/controls/software (ECS)-based vehicle design flow. Traditional approaches relying on component-level design and analysis are no longer effective as they do not always allow for the quantitative evaluation of properties arising from the composition of subsystems. This paper presents a system level architecture design methodology that is supported by tools and methods for the quantitative evaluation of key metrics of interest related to timing, dependability and cost. An example of its application to a by-wire system case study is presented, and the challenges faced in its application in the context of the actual development process are discussed

Automatic Model Generation for Black Box Real-Time Systems

Embedded systems are often assembled from black box components. System-level analyses, including verification and timing analysis, typically assume the system description, such as RTL or source code, as an input. There is therefore a need to automatically generate formal models of black box components to facilitate analysis. We propose a new method to generate models of real-time embedded systems based on machine learning from execution traces, under a given hypothesis about the systems model of computation. Our technique is based on a novel formulation of the model generation problem as learning a dependency graph that indicates partial ordering between tasks. Tests based on an industry case study demonstrate that the learning algorithm can scale up and that the deduced system model accurately reflects dependencies between tasks in the original design. These dependencies help us formally prove properties of the system and also extract data dependencies that are not explicitly stated in the specifications of black box components

An Initial Study on Monetary Cost Evaluation for the Design of Automotive Electrical Architectures

One of the many challenges facing electronic 1 system architects is how to provide a cost estimate related to design decisions over the entire life-cycle and product line of the architecture. Various cost modeling techniques may be used to perform this estimation. However, the estimation is often done in an ad-hoc manner, based on specific design scenarios or business assumptions. This situation may yield an unfair comparison of architectural alternatives due to the limited scope of the evaluation. A preferred estimation method would involve rigorous cost modeling based on architectural design cost drivers similar to those used in the manufacturing (e.g. process-based technical cost modeling) or in the enterprise software domain (e.g. COCOMO). This paper describes an initial study of a cost model associated with automotive electronic system architecture. The models intended use is to evaluate system cost drivers in response to various architectural decisions (e.g. choosing a communication bus topology or mapping a function to hardware). The primary cost driver categories explored are design and development, part fabrication, assembly and in-service costs. The preliminary version of this cost model focuses on describing the key influences on cost, but not the entire mathematical model. The paper presents the cost model with the help of influence diagrams and illustrates the use of the cost modeling methodology through an automotive case study – a steer-by-wire system. As future work, we propose to build a cost model and supporting methodology that accounts for architecture evolution to address the issue of evolving architecture requirements as well as when and where to employ new technology in the architecture.

A Conceptual Data Model for the Architecture Exploration of Automotive Distributed Embedded Architectures

As design complexities increase exponentially, automotive designers need integrated tool environments enabling system-level analyses of alternative architectural solutions. Hence, a huge amount of heterogeneous design data must be made available easily and quickly for the analysis. In this paper, we introduce the AETM (architecture exploration tools and methods) data model, a key enabler for an integrated model-based tool environment. The data model encompasses several important aspects of the design of any embedded architecture (not only an automotive one), as it supports design capture at different levels of abstraction (e.g., functional, logical, and physical) and across application domains.