RT-Bench: an Extensible Benchmark Framework for the Analysis and Management of Real-Time Applications

Benchmarking is crucial for testing and validating any system, even more so in real-time systems. Typical real-time applications adhere to well-understood abstractions: they exhibit a periodic behavior, operate on a well-defined working set, and strive for stable response time avoiding non-predicable factors such as page faults. Unfortunately, available benchmark suites fail to reflect key characteristics of real-time applications. Practitioners and researchers must resort to either benchmark heavily approximated real-time environments, or to re-engineer available benchmarks to add -- if possible -- the sought-after features. Additionally, the measuring and logging capabilities provided by most benchmark suites are not tailored "out-of-the-box" to real-time environments, and changing basic parameters such as the scheduling policy often becomes a tiring and error-prone exercise. In this paper, we present RT-bench, an open-source framework adding standard real-time features to virtually any existing benchmark. Furthermore, RT-bench provides an easy-to-use, unified command line interface to customize key aspects of the real-time execution of a set of benchmarks. Our framework is guided by four main criteria: 1) cohesive interface, 2) support for periodic application behavior and deadline semantics, 3) controllable memory footprint, and 4) extensibility and portability. We have integrated within the framework applications from the widely used SD-VBS and IsolBench suites. We showcase a set of use-cases that are representative of typical real-time system evaluation scenarios and that can be easily conducted via RT-Bench.Comment: 11 pages, 12 figures; code available at https://gitlab.com/rt-bench/rt-bench, documentation available at https://rt-bench.gitlab.io/rt-bench

MemPol: policing core memory bandwidth from outside of the cores

National Science FoundationAccepted manuscrip

Edge Generation Scheduling for DAG Tasks using Deep Reinforcement Learning

Directed acyclic graph (DAG) tasks are currently adopted in the real-time domain to model complex applications from the automotive, avionics, and industrial domain that implement their functionalities through chains of intercommunicating tasks. This paper studies the problem of scheduling real-time DAG tasks by presenting a novel schedulability test based on the concept of trivial schedulability. Using this schedulability test, we propose a new DAG scheduling framework (edge generation scheduling -- EGS) that attempts to minimize the DAG width by iteratively generating edges while guaranteeing the deadline constraint. We study how to efficiently solve the problem of generating edges by developing a deep reinforcement learning algorithm combined with a graph representation neural network to learn an efficient edge generation policy for EGS. We evaluate the effectiveness of the proposed algorithm by comparing it with state-of-the-art DAG scheduling heuristics and an optimal mixed-integer linear programming baseline. Experimental results show that the proposed algorithm outperforms the state-of-the-art by requiring fewer processors to schedule the same DAG tasks.Comment: Under revie

Deep Vein Thrombosis and Pulmonary Embolism: Two Complications of COVID-19 Pneumonia?

Coronavirus disease 19 (COVID-19) is a worldwide infection which was recently declared a global health emergency by the WHO Emergency Committee. The most common symptoms are fever and cough, which can progress to pneumonia, acute respiratory distress syndrome (ARDS) and/or end-organ failure. Risk factors associated with ARDS and death are older age, comorbidities (e.g., hypertension, diabetes, hyperlipidaemia), neutrophilia, and organ and coagulation dysfunction. Disseminated intravascular coagulation and coagulopathy can contribute to death. Anticoagulant treatment is associated with decreased mortality in severe COVID-19 pneumonia. In this report we describe two patients with COVID-19 pneumonia who developed venous thromboembolism

Abdominal Pain: A Real Challenge in Novel COVID-19 Infection

COVID-19 (coronavirus disease 19) is an infectious disease caused by coronavirus SARS-CoV-2. Since its detection in China at the end of 2019, the novel coronavirus has rapidly spread throughout the world and has caused an international public health emergency. The most common manifestation is flu-like symptoms. Mild infections usually improve within a few days, but COVID-19 can cause severe pneumonia with acute respiratory distress syndrome and death. Gastrointestinal symptoms are less common but possible and more difficult to recognize as part of a COVID-19 syndrome. In line with the current opinion of the WHO, we strongly believe that preventive measures and early diagnosis of COVID-19 are crucial to interrupt virus spread and avoid local outbreaks. We report the cases of COVID-19 patients admitted to our Emergency Department who complained of gastrointestinal symptoms at admission

COVID-19 and cutaneous manifestations: Two cases and a review of the literature

COVID-19 can affect multiple organs, including skin. A wide range of skin manifestations have been reported in literature. Six main phenotypes have been identified: i) urticarial rash, ii) confluent erythematous/maculopapular/morbilliform rash, iii) papulovesicular exanthem, iv) a chilblain-like acral pattern, v) a livedo reticularis/racemosa-like pattern, and vi) a purpuric vasculitic pattern. The pathogenetic mechanism is still not completely clear, but a role of hyperactive immune response, complement activation and microvascular injury have been postulated. The only correlation between the cutaneous phenotype and the severity of COVID-19 has been observed in the case of chilblain-like acral lesions, that is generally associated with the benign/subclinical course of COVID-19. Herein, we report two cases of SARS-CoV- 2 infection in patients who developed cutaneous manifestations that completely solved with systemic steroids and antihistamines. The first case is a female patient not vaccinated for SARS-CoV-2 with COVID-19 associated pneumonia, while the second case is a vaccinated female patient with only skin manifestations

Know your enemy: benchmarking and experimenting with insight as a goal

Available benchmark suites are used to provide realistic workloads and to understand their run-time characteristics. However, they do not necessarily target the same platforms and often offer a diverse set of metrics, leading to the lack of a knowledge base that could be used for both systems and theoretical research. RT-Bench, a new benchmark framework environment, tries to address these issues by providing a uniform interface and metrics while maintaining portability. This demo illustrates how to leverage this framework and its recently added features to improve the understanding of the benchmarks’ interaction with its system.National Science FoundationAccepted manuscrip

Factors influencing implementation of ehealth technologies to support informal dementia care:Umbrella review

Background: The worldwide increase in community-dwelling people with dementia underscores the need for innovative eHealth technologies that aim to provide support to both patients and their informal caregivers in the home setting. However, sustainable implementation of eHealth technologies within this target group can be difficult. Objective: The goal of this study was to gain a thorough understanding of why it is often difficult to implement eHealth technologies in practice, even though numerous technologies are designed to support people with dementia and their informal caregivers at home. In particular, our study aimed to (1) provide an overview of technologies that have been used and studied in the context of informal dementia care and (2) explore factors influencing the implementation of these technologies. Methods: Following an umbrella review design, five different databases were searched (PubMed, PsycINFO, Medline, Scopus, and Cochrane) for (systematic) reviews. Among 2205 reviews retrieved, 21 were included in our analysis based on our screening and selection procedure. A combination of deductive and inductive thematic analyses was performed, using the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework for organizing the findings. Results: We identified technologies designed to be used “by informal caregivers,” “by people with dementia,” and “with people with dementia.” Within those groups, most of the represented technologies included, respectively: (i) devices for in-home monitoring of lifestyle, health, and safety; (ii) technologies for supporting memory, orientation, and day structure; and (iii) technologies to facilitate communication between the informal caregiver and person with dementia. Most of the identified factors influencing implementation related to the condition of dementia, characteristics of the technology, expected/perceived value of users, and characteristics of the informal caregiver. Considerably less information has been reported on factors related to the implementing organization and technology supplier, wider institutional and sociocultural context of policy and regulations, and continued adaptation of technology over time. Conclusions: Our study offers a comprehensive overview of eHealth technologies in the context of informal dementia care and contributes to gaining a better understanding of a broad range of factors influencing their implementation. Our results uncovered a knowledge gap regarding success factors for implementation related to the organizational and broader context and continuous adaptation over the long term. Although future research is needed, the current findings can help researchers and stakeholders in improving the development and implementation of eHealth technologies to support informal dementia care

On the interplay of computation and memory regulation in multicore real-time systems

The ever-increasing demand for high performance in the time-critical embedded domain has pushed the adoption of powerful yet unpredictable heterogeneous Systems-on-a-Chip. The shared memory subsystem, which is known to be a major source of unpredictability, has been extensively studied, and many mitigation techniques have been proposed. Among them, performance-counter-based regulation techniques have seen widespread adoption. However, the problem of combining performance-based regulation with time-domain isolation has not received enough attention. In this article, we discuss our current work-in-progress on SHCReg (Software Hardware Co-design Regulator). First, we assess the limitations and benefits of combined CPU and memory budgeting. Next, we outline a full-stack hardware/software codesign architecture that aims at improving the interplay between CPU and memory isolation for mixed-criticality tasks running on the same core.National Science FoundationAccepted manuscrip

A real-time virtio-based framework for predictable inter-VM communication

Ensuring real-time properties on current heterogeneous multiprocessor systems on a chip is a challenging task. Furthermore, online artificial intelligent applications –which are routinely deployed on such chips– pose increasing pressure on the memory subsystem that becomes a source of unpredictability. Although techniques have been proposed to restore independent access to memory for concurrently executing virtual machines (VM), providing predictable inter-VM communication remains challenging. In this work, we tackle the problem of predictably transferring data between virtual machines and virtualized hardware resources on multiprocessor systems on chips under consideration of memory interference. We design a "broker-based" real-time communication framework for otherwise isolated virtual machines, provide a virtio-based reference implementation on top of the Jailhouse hypervisor, assess its overheads for FreeRTOS virtual machines, and formally analyze its communication flow schedulability under consideration of the implementation overheads. Furthermore, we define a methodology to assess the maximum DRAM memory saturation empirically, evaluate the framework's performance and compare it with the theoretical schedulability.Accepted manuscrip