Towards Compositional Mixed-Criticality Real-Time Scheduling in Open Systems

Although many cyber-physical systems are both mixed-criticality system and compositional system, there are little work on intersection of mixed-criticality system and compositional system. We propose novel concepts for task-level criticality mode and reconsider temporal isolation in terms of compositional mixed-criticality scheduling

Mixed-Criticality Scheduling on Multiprocessors using Task Grouping

Real-time systems are increasingly running a mix of tasks with different criticality levels: for instance, unmanned aerial vehicle has multiple software functions with different safety criticality levels, but runs them on a single, shared computational platform. In addition, these systems are increasingly deployed on multiprocessor platforms because this can help to reduce their cost, space, weight, and power consumption. To assure the safety of such systems, several mixed-criticality scheduling algorithms have been developed that can provide mixed-criticality timing guarantees. However, most existing algorithms have two important limitations: they do not guarantee strong isolation among the high-criticality tasks, and they offer poor real-time performance for the low-criticality tasks

A Survey of Research into Mixed Criticality Systems

This survey covers research into mixed criticality systems that has been published since Vestal’s seminal paper in 2007, up until the end of 2016. The survey is organised along the lines of the major research areas within this topic. These include single processor analysis (including fixed priority and EDF scheduling, shared resources and static and synchronous scheduling), multiprocessor analysis, realistic models, and systems issues. The survey also explores the relationship between research into mixed criticality systems and other topics such as hard and soft time constraints, fault tolerant scheduling, hierarchical scheduling, cyber physical systems, probabilistic real-time systems, and industrial safety standards

Multi-Mode Virtualization for Soft Real-Time Systems

Real-time virtualization is an emerging technology for embedded systems integration and latency-sensitive cloud applications. Earlier real-time virtualization platforms require offline configuration of the scheduling parameters of virtual machines (VMs) based on their worst-case workloads, but this static approach results in pessimistic resource allocation when the workloads in the VMs change dynamically. Here, we present Multi-Mode-Xen (M2-Xen), a real-time virtualization platform for dynamic real-time systems where VMs can operate in modes with different CPU resource requirements at run-time. M2-Xen has three salient capabilities: (1) dynamic allocation of CPU resources among VMs in response to their mode changes, (2) overload avoidance at both the VM and host levels during mode transitions, and (3) fast mode transitions between different modes. M2-Xen has been implemented within Xen 4.8 using the real-time deferrable server (RTDS) scheduler. Experimental results show that M2-Xen maintains real-time performance in different modes, avoids overload during mode changes, and performs fast mode transitions

A Rely-Guarantee Specification of Mixed-Criticality Scheduling

The application considered is mixed-criticality scheduling. The core formal approaches used are Rely-Guarantee conditions and the Timeband framework; these are applied to give a layered description of job scheduling which includes resilience to jobs overrunning their expected execution time. A novel formal modelling idea is proposed to handle the relationship between actual time and its approximation in hardware clocks.Comment: This paper will appear in a Festschrift - on publication we will insert a pointer to the boo

Platform-based Plug and Play of Automotive Safety Features - Challenges and Directions

Optional software-based features are increasingly becoming an important cost driver in automotive systems. These include features pertaining to active safety, infotainment, etc. Currently, these optional features are integrated into the vehicles at the factory during assembly. This severely restricts the flexibility of the customer to select and use features on-demand and therefore, the customer will either have to be satisfied with an available set of feature options or pre-order a car with the required features from the manufacturer resulting in considerable delay. In order to increase flexibility and reduce the delay, it is necessary to provide the option to configure the vehicle on-demand at the dealership or remotely. In this paper, we present our vision and challenges involved in developing a platform infrastructure that allows on-demand deployment of automotive safety features and ensures their correct execution