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

Systematic Evaluation of Sandboxed Software Deployment for Real-time Software on the Example of a Self-Driving Heavy Vehicle

Companies developing and maintaining software-only products like web shops aim for establishing persistent links to their software running in the field. Monitoring data from real usage scenarios allows for a number of improvements in the software life-cycle, such as quick identification and solution of issues, and elicitation of requirements from previously unexpected usage. While the processes of continuous integration, continuous deployment, and continuous experimentation using sandboxing technologies are becoming well established in said software-only products, adopting similar practices for the automotive domain is more complex mainly due to real-time and safety constraints. In this paper, we systematically evaluate sandboxed software deployment in the context of a self-driving heavy vehicle that participated in the 2016 Grand Cooperative Driving Challenge (GCDC) in The Netherlands. We measured the system’s scheduling precision after deploying applications in four different execution environments. Our results indicate that there is no significant difference in performance and overhead when sandboxed environments are used compared to natively deployed software. Thus, recent trends in software architecting, packaging, and maintenance using microservices encapsulated in sandboxes will help to realize similar software and system engineering for cyber-physical systems

Systematic Evaluation of Sandboxed Software Deployment for Real-time Software on the Example of a Self-Driving Heavy Vehicle

Companies developing and maintaining software-only products like web shops aim for establishing persistent links to their software running in the field. Monitoring data from real usage scenarios allows for a number of improvements in the software life-cycle, such as quick identification and solution of issues, and elicitation of requirements from previously unexpected usage. While the processes of continuous integration, continuous deployment, and continuous experimentation using sandboxing technologies are becoming well established in said software-only products, adopting similar practices for the automotive domain is more complex mainly due to real-time and safety constraints. In this paper, we systematically evaluate sandboxed software deployment in the context of a self-driving heavy vehicle that participated in the 2016 Grand Cooperative Driving Challenge (GCDC) in The Netherlands. We measured the system’s scheduling precision after deploying applications in four different execution environments. Our results indicate that there is no significant difference in performance and overhead when sandboxed environments are used compared to natively deployed software. Thus, recent trends in software architecting, packaging, and maintenance using microservices encapsulated in sandboxes will help to realize similar software and system engineering for cyber-physical systems