Improving efficiency of persistent storage access in embedded Linux

Real-time embedded systems increasingly need to process and store large volumes of persistent data, requiring fast, timely and predictable storage. Traditional methods of accessing storage using general-purpose operating system-based file systems do not provide the performance and timing predictability needed. This paper firstly examines the speed and consistency of SSD operations in an embedded Linux system, identifying areas where inefficiencies in the storage stack cause issues for performance and predictability. Secondly, the CharIO storage device driver is proposed to bypass Linux file systems and the kernel block layer, in order to increase performance, and provide improved timing predictability

BlueIO: A Scalable Real-Time Hardware I/O Virtualization System for Many-core Embedded Systems

In safety-critical systems, time predictability is vital. This extends to I/O operations which require predictability, timing-accuracy, parallel access, scalability, and isolation. Currently, existing approaches cannot achieve all these requirements at the same time. In this paper, we propose a framework of hardware framework for real-time I/O virtualization termed BlueIO to meet all these requirements simultaneously. BlueIO integrates the functionalities of I/O virtualization, low layer I/O drivers and a clock cycle level timing-accurate I/O controller (using the GPIOCP. BlueIO provides this functionality in the hardware layer, supporting abstract virtualized access to I/O from the software domain. The hardware implementation includes I/O virtualization and I/O drivers, provides isolation and parallel (concurrent) access to I/O operations and improves I/O performance. Furthermore, the approach includes the previously proposed GPIOCP to guarantee that I/O operations will occur at a specific clock cycle (i.e. be timing-accurate and predictable). In this paper, we present a hardware consumption analysis of BlueIO, in order to show that it linearly scales with the number of CPUs and I/O devices, which is evidenced by our implementation in VLSI and FPGA. We also describe the design and implementation of BlueIO, and demonstrate how a BlueIO-based system can be exploited to meet real-time requirements with significant improvements in I/O performance and a low running cost on different OSs

Architecting Time-Critical Big-Data Systems

Current infrastructures for developing big-data applications are able to process –via big-data analytics- huge amounts of data, using clusters of machines that collaborate to perform parallel computations. However, current infrastructures were not designed to work with the requirements of time-critical applications; they are more focused on general-purpose applications rather than time-critical ones. Addressing this issue from the perspective of the real-time systems community, this paper considers time-critical big-data. It deals with the definition of a time-critical big-data system from the point of view of requirements, analyzing the specific characteristics of some popular big-data applications. This analysis is complemented by the challenges stemmed from the infrastructures that support the applications, proposing an architecture and offering initial performance patterns that connect application costs with infrastructure performance

Errata for three papers (2004-05) on fixed-priority scheduling with self-suspensions

The purpose of this article is to (i) highlight the flaws in three previously published works [3][2][7] on the worst-case response time analysis for tasks with self-suspensions and (ii) provide straightfor- ward fixes for those flaws, hence rendering the ana- lysis safe