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

Towards a centralized multicore automotive system

Today’s automotive systems are inundated with embedded electronics to host chassis, powertrain, infotainment, advanced driver assistance systems, and other modern vehicle functions. As many as 100 embedded microcontrollers execute hundreds of millions of lines of code in a single vehicle. To control the increasing complexity in vehicle electronics and services, automakers are planning to consolidate different on-board automotive functions as software tasks on centralized multicore hardware platforms. However, these vehicle software services have different and contrasting timing, safety, and security requirements. Existing vehicle operating systems are ill-equipped to provide all the required service guarantees on a single machine. A centralized automotive system aims to tackle this by assigning software tasks to multiple criticality domains or levels according to their consequences of failures, or international safety standards like ISO 26262. This research investigates several emerging challenges in time-critical systems for a centralized multicore automotive platform and proposes a novel vehicle operating system framework to address them. This thesis first introduces an integrated vehicle management system (VMS), called DriveOS™, for a PC-class multicore hardware platform. Its separation kernel design enables temporal and spatial isolation among critical and non-critical vehicle services in different domains on the same machine. Time- and safety-critical vehicle functions are implemented in a sandboxed Real-time Operating System (OS) domain, and non-critical software is developed in a sandboxed general-purpose OS (e.g., Linux, Android) domain. To leverage the advantages of model-driven vehicle function development, DriveOS provides a multi-domain application framework in Simulink. This thesis also presents a real-time task pipeline scheduling algorithm in multiprocessors for communication between connected vehicle services with end-to-end guarantees. The benefits and performance of the overall automotive system framework are demonstrated with hardware-in-the-loop testing using real-world applications, car datasets and simulated benchmarks, and with an early-stage deployment in a production-grade luxury electric vehicle

PAStime: Progress-Aware Scheduling for Time-Critical Computing

Over-estimation of worst-case execution times (WCETs) of real-time tasks leads to poor resource utilization. In a mixed-criticality system (MCS), the over-provisioning of CPU time to accommodate the WCETs of highly critical tasks may lead to degraded service for less critical tasks. In this paper we present PAStime, a novel approach to monitor and adapt the runtime progress of highly time-critical applications, to allow for improved service to lower criticality tasks. In PAStime, CPU time is allocated to time-critical tasks according to the delays they experience as they progress through their control flow graphs. This ensures that as much time as possible is made available to improve the Quality-of-Service of less critical tasks, while high-criticality tasks are compensated after their delays. This paper describes the integration of PAStime with Adaptive Mixed-criticality (AMC) scheduling. The LO-mode budget of a high-criticality task is adjusted according to the delay observed at execution checkpoints. This is the first implementation of AMC in the scheduling framework of LITMUS^RT, which is extended with our PAStime runtime policy and tested with real-time Linux applications such as object classification and detection. We observe in our experimental evaluation that AMC-PAStime significantly improves the utilization of the low-criticality tasks while guaranteeing service to high-criticality tasks

Boomerang: real-time I/O meets legacy systems

This paper presents Boomerang, an I/O system that integrates a legacy non-real-time OS with one that is customized for timing-sensitive tasks. A relatively small RTOS benefits from the pre-existing libraries, drivers and services of the legacy system. Additionally, timing-critical tasks are isolated from less critical tasks by securely partitioning machine resources among the separate OSes. Boomerang guarantees end-to-end processing delays on input data that requires outputs to be generated within specific time bounds.We show how to construct composable task pipelines in Boomerang that combine functionality spanning a custom RTOS and a legacy Linux system. By dedicating time-critical I/O to the RTOS, we ensure that complementary services provided by Linux are sufficiently predictable to meet end-to-end service guarantees. While Boomerang benefits from spatial isolation, it also outperforms a standalone Linux system using deadline-based CPU reservations for pipeline tasks. We also show how Boomerang outperforms a virtualized system called ACRN, designed for automotive systems.https://ieeexplore.ieee.org/document/9113119Published versio

Food Imports in India: Prospects, Issues and Way Forward

India is one of the fastest growing markets in the world. Among the various sectors contributing to the growth of the economy, food is one of the fastest growing sectors owing to factors such as a large population base, rising middle-class, increase in per-capita income, and greater consumer awareness. Demand for imported food products is increasing due to factors such as reduction in tariffs, changes in consumer preferences and growing adaptability to international cuisine. Several initiatives have been taken by the Indian government in recent years to improve the ease of doing business and reduce the compliance burden through use of technology. Despite these initiatives, India’s ranking compared to other countries in some selective indicators such as documentary and border compliance and logistics performance is quite low. To improve ease of doing business, reduce cost and time taken in importing food products, and improve India’s ranking in logistics performance indicators, there is an urgent need for backend process and information technology (IT) integration across the agencies involved in the import clearance process. Given this background, the objective of this paper is to (a) provide an overview of the food import clearance process in India, (b) identify issues in the import process and (c) make recommendations on how to streamline the process using technology and automation. The paper is based on a survey of key stakeholders engaged in the food import clearance process. Based on a primary survey of 150 stakeholders in the United Kingdom and India, this paper finds that while India is an attractive market for importing food; low penetration of technology in the food import clearance process, lack of inter-agency coordination, and lack of risk management systems impedes the ease of importing food products into India. This paper recommends that in order to enhance ease of doing business, especially for SMEs, there is need to reduce procedural barriers by implementing technology and automation-oriented solutions, and a robust risk management system. In addition, there is a need to conduct continuous regulatory impact analysis to assess the time and cost reduction in importing food into India.JEL classification: F10, F13, L66, O3

An innate pathogen sensing strategy involving ubiquitination of bacterial surface proteins.

Sensing of pathogens by ubiquitination is a critical arm of cellular immunity. However, universal ubiquitination targets on microbes remain unidentified. Here, using in vitro, ex vivo, and in vivo studies, we identify the first protein-based ubiquitination substrates on phylogenetically diverse bacteria by unveiling a strategy that uses recognition of degron-like motifs. Such motifs form a new class of intra-cytosolic pathogen-associated molecular patterns (PAMPs). Their incorporation enabled recognition of nonubiquitin targets by host ubiquitin ligases. We find that SCFFBW7 E3 ligase, supported by the regulatory kinase, glycogen synthase kinase 3β, is crucial for effective pathogen detection and clearance. This provides a mechanistic explanation for enhanced risk of infections in patients with chronic lymphocytic leukemia bearing mutations in F-box and WD repeat domain containing 7 protein. We conclude that exploitation of this generic pathogen sensing strategy allows conservation of host resources and boosts antimicrobial immunity

[Solution] End-to-end Scheduling of Real-time Task Pipelines on Multiprocessors

Task pipelines are common in today’s embedded systems, as data moves from source to sink in sensing-processing-actuation task chains. A real-time task pipeline is constructed by connecting a series of periodic tasks with data buffers. In a time-critical system, end-to-end timing and data-transfer properties of a task pipeline must be guaranteed. A guarantee could be mathematically expressed by assigning constraints to the tasks of a pipeline. However, deriving task scheduling parameters to meet end-to-end guarantees is an NP-hard constraint optimization problem. Hence, a traditional constraint solver is not a suitable runtime solution.In this paper, we present a heuristic constraint solver algorithm, CoPi, to derive the execution times and periods of pipelined tasks that meet the end-to-end constraints and schedulability requirements. We consider two upper bound constraints on a task pipeline: end-to-end delay and loss-rate. After satisfying these constraints, CoPi schedules a pipeline as a set of asynchronous and data independent periodic tasks, under the rate-monotonic scheduling algorithm. Simulations show that CoPi has a comparable pipeline acceptance ratio and significantly better runtime than open-source MINLPsolvers. Furthermore, we use CoPi to map multiple task pipelines to a multiprocessor system. We demonstrate that a partitioned multiprocessor scheduling algorithm coupled with CoPi accommodates dynamically appearing pipelines, while attempting to minimize task migrations