Response Time Analysis for Sporadic Server based Budget Scheduling in Real Time Virtualization Environments

Virtualization techniques for embedded real-time systems typically employ TDMA scheduling to achieve temporal isolation among different virtualized applications. Recent work already introduced sporadic server based solutions relying on budgets instead of a fixed TDMA schedule. While providing better average-case response times for IRQs and tasks, a formal response time analysis for the worst-case is still missing. In order to confirm the advantage of a sporadic server based budget scheduling, this paper provides a worst-case response time analysis. To improve the sporadic server based budget scheduling even more, we provide a background scheduling implementation which will also be covered by the formal analysis. We show correctness of the analysis approach and compare it against TDMA based systems. In addition to that, we provide response time measurements from a working hypervisor implementation on an ARM based development board

Real-Time Virtualization and Cloud Computing

In recent years, we have observed three major trends in the development of complex real-time embedded systems. First, to reduce cost and enhance flexibility, multiple systems are sharing common computing platforms via virtualization technology, instead of being deployed separately on physically isolated hosts. Second, multi-core processors are increasingly being used in real-time systems. Third, developers are exploring the possibilities of deploying real-time applications as virtual machines in a public cloud. The integration of real-time systems as virtual machines (VMs) atop common multi-core platforms in a public cloud raises significant new research challenges in meeting the real-time latency requirements of applications. In order to address the challenges of running real-time VMs in the cloud, we first present RT-Xen, a novel real-time scheduling framework within the popular Xen hypervisor. We start with single-core scheduling in RT-Xen, and present the first work that empirically studies and compares different real-time scheduling schemes on a same platform. We then introduce RT-Xen 2.0, which focuses on multi-core scheduling and spanning multiple design spaces, including priority schemes, server schemes, and scheduling policies. Experimental results demonstrate that when combined with compositional scheduling theory, RT-Xen can deliver real-time performance to an application running in a VM, while the default credit scheduler cannot. After that, we present RT-OpenStack, a cloud management system designed to support co-hosting real-time and non-real-time VMs in a cloud. RT-OpenStack studies the problem of running real-time VMs together with non-real-time VMs in a public cloud. Leveraging the resource interface and real-time scheduling provided by RT-Xen, RT-OpenStack provides real-time performance guarantees to real-time VMs, while achieving high resource utilization by allowing non-real-time VMs to share the remaining CPU resources through a novel VM-to-host mapping scheme. Finally, we present RTCA, a real-time communication architecture for VMs sharing a same host, which maintains low latency for high priority inter-domain communication (IDC) traffic in the face of low priority IDC traffic

Distributed real-time fault tolerance in a virtualized separation kernel

Computers are increasingly being placed in scenarios where a computer error could result in the loss of human life or significant financial loss. Fault tolerant techniques must be employed to prevent an error from resulting in a fault causing such losses. Two types of errors that are common in real-time and embedded system are soft errors, i.e. data bit corruption, and timing errors, such as missed deadlines. Purely software based techniques to address these types of errors have the advantage of not requiring specialized hardware and are able to use more readily available commercial off-the-shelf hardware. Timing errors are addressed using Adaptive Mixed-Criticality, a scheduling technique where higher criticality tasks are given precedence over those of lower criticality when it is impossible to guarantee the schedulability of all tasks. While mixed-criticality scheduling has gained attention in recent years, most approaches assume a periodic task model and that the system has a single criticality level which dictates the available budget to all tasks. In practice these assumptions do not hold: different types of tasks are better served by different scheduling approaches and only a subset of high critical tasks might require additional capacity to meet deadlines. In the latter case, this occurs when a process has experienced a fault and requires additional capacity to perform the recovery. In this thesis, soft errors are addressed using a novel real-time fault tolerance method based on a virtualized separation kernel. Instead of executing redundant copies of an application on separate machines, the applications are consolidated onto one multi-core processor and use hardware virtualization extensions to partition the applications. This allows new recovery schemes to be explored. In addition, the maximum recovery time is sufficiently bounded to ensure recovery occurs in a timely manner without affecting the normal execution of the application. A virtualized separation kernel in combination with Adaptive Mixed-Criticality techniques creates a fault tolerant system that predictably detects and recovers from timing and soft errors

Scheduling policies and system software architectures for mixed-criticality computing

Mixed-criticality model of computation is being increasingly adopted in timing-sensitive systems. The model not only ensures that the most critical tasks in a system never fails, but also aims for better systems resource utilization in normal condition. In this report, we describe the widely used mixed-criticality task model and fixed-priority scheduling algorithms for the model in uniprocessors. Because of the necessity by the mixed-criticality task model and scheduling policies, isolation, both temporal and spatial, among tasks is one of the main requirements from the system design point of view. Different virtualization techniques have been used to design system software architecture with the goal of isolation. We discuss such a few system software architectures which are being and can be used for mixed-criticality model of computation

Secure Virtualization of Latency-Constrained Systems

Virtualization is a mature technology in server and desktop environments where multiple systems are consolidate onto a single physical hardware platform, increasing the utilization of todays multi-core systems as well as saving resources such as energy, space and costs compared to multiple single systems. Looking at embedded environments reveals that many systems use multiple separate computing systems inside, including requirements for real-time and isolation properties. For example, modern high-comfort cars use up to a hundred embedded computing systems. Consolidating such diverse configurations promises to save resources such as energy and weight. In my work I propose a secure software architecture that allows consolidating multiple embedded software systems with timing constraints. The base of the architecture builds a microkernel-based operating system that supports a variety of different virtualization approaches through a generic interface, supporting hardware-assisted virtualization and paravirtualization as well as multiple architectures. Studying guest systems with latency constraints with regards to virtualization showed that standard techniques such as high-frequency time-slicing are not a viable approach. Generally, guest systems are a combination of best-effort and real-time work and thus form a mixed-criticality system. Further analysis showed that such systems need to export relevant internal scheduling information to the hypervisor to support multiple guests with latency constraints. I propose a mechanism to export those relevant events that is secure, flexible, has good performance and is easy to use. The thesis concludes with an evaluation covering the virtualization approach on the ARM and x86 architectures and two guest operating systems, Linux and FreeRTOS, as well as evaluating the export mechanism

Qduino: a cyber-physical programming platform for multicore Systems-on-Chip

Emerging multicore Systems-on-Chip are enabling new cyber-physical applications such as autonomous drones, driverless cars and smart manufacturing using web-connected 3D printers. Common to those applications is a communicating task pipeline, to acquire and process sensor data and produce outputs that control actuators. As a result, these applications usually have timing requirements for both individual tasks and task pipelines formed for sensor data processing and actuation. Current cyber-physical programming platforms, such as Arduino and embedded Linux with the POSIX interface do not allow application developers to specify those timing requirements. Moreover, none of them provide the programming interface to schedule tasks and map them to processor cores, while managing I/O in a predictable manner, on multicore hardware platforms. Hence, this thesis presents the Qduino programming platform. Qduino adopts the simplicity of the Arduino API, with additional support for real-time multithreaded sketches on multicore architectures. Qduino allows application developers to specify timing properties of individual tasks as well as task pipelines at the design stage. To this end, we propose a mathematical framework to derive each task’s budget and period from the specified end-to-end timing requirements. The second part of the thesis is motivated by the observation that at the center of these pipelines are tasks that typically require complex software support, such as sensor data fusion or image processing algorithms. These features are usually developed by many man-year engineering efforts and thus commonly seen on General-Purpose Operating Systems (GPOS). Therefore, in order to support modern, intelligent cyber-physical applications, we enhance the Qduino platform’s extensibility by taking advantage of the Quest-V virtualized partitioning kernel. The platform’s usability is demonstrated by building a novel web-connected 3D printer and a prototypical autonomous drone framework in Qduino

High-Performance Cloud Computing: A View of Scientific Applications

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape