Software Concerns for Execution on Heterogeneous Platforms

Context: Heterogeneous computing, i.e., computing performed on different types of execution units, such as CPUs, GPUs, FPGAs, has shown to be a feasible path towards higher performance and less energy consumption. Heterogeneous platforms are specialized on specific types of computation, e.g., parallel computing. However, this approach imposes a number of challenges on the software side. One of such challenges is related to software deployment, in which applications must be prepared to be executed in different target architectures. Further, the approach demands a robust inter-process communication solution, since these systems inherently distribute computation. Objective: The objective of this thesis is twofold. First, to provide an overview of the state-of-the-art of software deployment on heterogeneous platforms, with emphasis to goals, concerns, challenges, and identification of topics of importance for further research. Second, to investigate the communication problem and propose a novel method that improves inter-process communication in distributed systems. Method: Six papers were written as results of four studies: (i) a literature review in the form of a systematic mapping study on software deployment on heterogeneous platforms; (ii) a systematic evaluation of deployment methods in the context of a self-driving heavy vehicle; (iii) an investigation on data marshalling approaches and how they perform in the context of a cyber-physical system; and (iv) a novel message restructuring approach, also in the context of cyber-physical systems. Results and Conclusions: The mapping study discussed the (i) concerns on the topic such as scheduling and software quality; the (ii) approaches available, such as frameworks; and the (iii) architecture solutions used, such as styles and principles. In the second study, we found that the performance decay is negligible when using sandboxed environments for deployment. In the third and fourth studies, we proposed and evaluated a data marshalling approach that decreases bandwidth consumption. Future work: We intend to identify challenges that are currently faced in an industrial setting. In particular, a migration from a non-heterogenous platform to a heterogeneous platform can be studied in the context of a modern software development process, such as DevOps

Design Criteria to Architect Continuous Experimentation for Self-Driving Vehicles

The software powering today's vehicles surpasses mechatronics as the dominating engineering challenge due to its fast evolving and innovative nature. In addition, the software and system architecture for upcoming vehicles with automated driving functionality is already processing ~750MB/s - corresponding to over 180 simultaneous 4K-video streams from popular video-on-demand services. Hence, self-driving cars will run so much software to resemble "small data centers on wheels" rather than just transportation vehicles. Continuous Integration, Deployment, and Experimentation have been successfully adopted for software-only products as enabling methodology for feedback-based software development. For example, a popular search engine conducts ~250 experiments each day to improve the software based on its users' behavior. This work investigates design criteria for the software architecture and the corresponding software development and deployment process for complex cyber-physical systems, with the goal of enabling Continuous Experimentation as a way to achieve continuous software evolution. Our research involved reviewing related literature on the topic to extract relevant design requirements. The study is concluded by describing the software development and deployment process and software architecture adopted by our self-driving vehicle laboratory, both based on the extracted criteria.Comment: Copyright 2017 IEEE. Paper submitted and accepted at the 2017 IEEE International Conference on Software Architecture. 8 pages, 2 figures. Published in IEEE Xplore Digital Library, URL: http://ieeexplore.ieee.org/abstract/document/7930218

Real-Time Containers: A Survey

Container-based virtualization has gained a significant importance in a deployment of software applications in cloud-based environments. The technology fully relies on operating system features and does not require a virtualization layer (hypervisor) that introduces a performance degradation. Container-based virtualization allows to co-locate multiple isolated containers on a single computation node as well as to decompose an application into multiple containers distributed among several hosts (e.g., in fog computing layer). Such a technology seems very promising in other domains as well, e.g., in industrial automation, automotive, and aviation industry where mixed criticality containerized applications from various vendors can be co-located on shared resources. However, such industrial domains often require real-time behavior (i.e, a capability to meet predefined deadlines). These capabilities are not fully supported by the container-based virtualization yet. In this work, we provide a systematic literature survey study that summarizes the effort of the research community on bringing real-time properties in container-based virtualization. We categorize existing work into main research areas and identify possible immature points of the technology

Continuous Experimentation for Automotive Software on the Example of a Heavy Commercial Vehicle in Daily Operation

As the automotive industry focuses its attention more and more towards the software functionality of vehicles, techniques to deliver new software value at a fast pace are needed. Continuous Experimentation, a practice coming from the web-based systems world, is one of such techniques. It enables researchers and developers to use real-world data to verify their hypothesis and steer the software evolution based on performances and user preferences, reducing the reliance on simulations and guesswork. Several challenges prevent the verbatim adoption of this practice on automotive cyber-physical systems, e.g., safety concerns and limitations from computational resources; nonetheless, the automotive field is starting to take interest in this technique. This work aims at demonstrating and evaluating a prototypical Continuous Experimentation infrastructure, implemented on a distributed computational system housed in a commercial truck tractor that is used in daily operations by a logistic company on public roads. The system comprises computing units and sensors, and software deployment and data retrieval are only possible remotely via a mobile data connection due to the commercial interests of the logistics company. This study shows that the proposed experimentation process resulted in the development team being able to base software development choices on the real-world data collected during the experimental procedure. Additionally, a set of previously identified design criteria to enable Continuous Experimentation on automotive systems was discussed and their validity confirmed in the light of the presented work.Comment: Paper accepted to the 14th European Conference on Software Architecture (ECSA 2020). 16 pages, 5 figure

A Process-oriented Approach for Migrating Software to Heterogeneous Platforms

Context: Heterogeneous computing, i.e., computing performed on processors of different types - such as combination of CPUs and GPUs, or CPUs and FPGAs - has shown to be a feasible path towards higher performance and less energy consumption. However, this approach imposes a number of challenges on the software side that must be addressed in order to achieve the aforementioned advantages.Objective: The objective of this thesis is to improve the process of software deployment on heterogeneous platforms. Through a detailed analysis of the state-of-the-art and state-of-the-practice, we aim to provide a reasoning framework for engineers to migrate software to be executed on such platforms.Method: To achieve our goal, we conducted: (i) a literature review in the form of a systematic mapping study on software deployment on heterogeneous platforms; (ii) a multiple case study in industry that highlights the main challenges and concerns in the state-of-the-practice in the area; and (iii) a study in which we propose and evaluate a decision framework to guide engineers in migrating software for execution on heterogeneous platforms, with a case study in the automotive domain.Results: In the mapping study, we provided a thorough classification of the identified concerns and approaches to deploying software on heterogeneous platforms. Among other findings, we discovered a lack of holistic approaches that include development processes, as well as few validation studies in industrial contexts. In the second study, we discovered and analyzed common practices and challenges that companies face when using heterogeneous platforms. One of such challenges is related to the lack of approaches that cover the software development lifecycle. In the third study, we proposed a decision framework that guides engineers in the process of reasoning for migrating software for execution on heterogeneous platforms. It consists of five stages (assessing, re-architecting, developing, deploying, evaluating), each containing a set of aspects to be addressed through the answers to predefined questions.Conclusions: This thesis addresses a gap that was identified in both theory and practice concerning the lack of holistic approaches to migrate software for execution on heterogeneous platforms. Our proposed approach addresses the problem through systematic guidance for engineers.Future work: In the future, we intend to further refine the proposed framework through case studies in domains other than automotive. We will explore its integration with existing software engineering processes in industrial contexts, performing in-depth analysis of the required adaptations and providing detailed solutions within the stages of the framework

Programming tools for intelligent systems

Les outils de programmation sont des programmes informatiques qui aident les humains à programmer des ordinateurs. Les outils sont de toutes formes et tailles, par exemple les éditeurs, les compilateurs, les débogueurs et les profileurs. Chacun de ces outils facilite une tâche principale dans le flux de travail de programmation qui consomme des ressources cognitives lorsqu’il est effectué manuellement. Dans cette thèse, nous explorons plusieurs outils qui facilitent le processus de construction de systèmes intelligents et qui réduisent l’effort cognitif requis pour concevoir, développer, tester et déployer des systèmes logiciels intelligents. Tout d’abord, nous introduisons un environnement de développement intégré (EDI) pour la programmation d’applications Robot Operating System (ROS), appelé Hatchery (Chapter 2). Deuxièmement, nous décrivons Kotlin∇, un système de langage et de type pour la programmation différenciable, un paradigme émergent dans l’apprentissage automatique (Chapter 3). Troisièmement, nous proposons un nouvel algorithme pour tester automatiquement les programmes différenciables, en nous inspirant des techniques de tests contradictoires et métamorphiques (Chapter 4), et démontrons son efficacité empirique dans le cadre de la régression. Quatrièmement, nous explorons une infrastructure de conteneurs basée sur Docker, qui permet un déploiement reproductible des applications ROS sur la plateforme Duckietown (Chapter 5). Enfin, nous réfléchissons à l’état actuel des outils de programmation pour ces applications et spéculons à quoi pourrait ressembler la programmation de systèmes intelligents à l’avenir (Chapter 6).Programming tools are computer programs which help humans program computers. Tools come in all shapes and forms, from editors and compilers to debuggers and profilers. Each of these tools facilitates a core task in the programming workflow which consumes cognitive resources when performed manually. In this thesis, we explore several tools that facilitate the process of building intelligent systems, and which reduce the cognitive effort required to design, develop, test and deploy intelligent software systems. First, we introduce an integrated development environment (IDE) for programming Robot Operating System (ROS) applications, called Hatchery (Chapter 2). Second, we describe Kotlin∇, a language and type system for differentiable programming, an emerging paradigm in machine learning (Chapter 3). Third, we propose a new algorithm for automatically testing differentiable programs, drawing inspiration from techniques in adversarial and metamorphic testing (Chapter 4), and demonstrate its empirical efficiency in the regression setting. Fourth, we explore a container infrastructure based on Docker, which enables reproducible deployment of ROS applications on the Duckietown platform (Chapter 5). Finally, we reflect on the current state of programming tools for these applications and speculate what intelligent systems programming might look like in the future (Chapter 6)

Exploring multimedia and interactive technologies

The goal of multimedia design strategies and innovation is to produce meaningful learning environments that relate to and build upon what the learner already knows and what the learner seeks. The multimedia tools used to achieve knowledge transfer should activate recall or prior knowledge and help the learner alter and encode new structures. Traditionally, multimedia has been localized to specific delivery systems and demographics based on the government, industry, or academic concentration. The presenter will explore the introduction of immersive telecommunications technologies, constructivist learning methodologies, and adult learning models to standardize networking and multimedia-based services and products capable of adapting to wired and wireless environments, different devices and conditions on a global scale

Security Analysis of Web and Embedded Applications

As we put more trust in the computer systems we use the need for securityis increasing. And while security features like HTTPS are becomingcommonplace on the web, securing applications remains dicult. This thesisfocuses on analyzing dierent computer ecosystems to detect vulnerabilitiesand develop countermeasures. This includesweb browsers,web applications,and cyber-physical systems such as Android Automotive.For web browsers, we analyze how new security features might solve aproblem but introduce new ones. We show this by performing a systematicanalysis of the new Content Security Policy (CSP) directive navigate-to.In our research, we nd that it does introduce new vulnerabilities, to whichwe recommend countermeasures. We also create AutoNav, a tool capable ofautomatically suggesting navigation policies for this directive.To improve the security of web applications, we develop a novel blackboxmethod by combining the strengths of dierent black-box methods. Weimplement this in our scanner Black Widow, which we compare with otherleading web application scanners. Black Widow both improves the coverageof the web application and nds more vulnerabilities, including ones inPrestashop, WordPress, and HotCRP.For embedded systems,We analyze the new attack vectors introduced bycombining a phone OS with vehicle APIs and nd new attacks pertaining tosafety, privacy, and availability. Furthermore, we create AutoTame, which isdesigned to analyze third-party apps for vehicles for the vulnerabilities wefound