Real-time Guarantees Research Articles

Towards fault-tolerance of IMA with safe dynamic reconfiguration

Integrated Modular Avionics (IMA) is essential to modern avionics. It increases the possibilities for reuse of software and hardware resources by system integrators, through the use of standardized communication interfaces and operating system services. Meanwhile, the safety requirements of DO-297 dictate that the system architecture must prevent common cause failures and that a single failure cannot disable any critical function. As a result, critical functions have to be allocated redundantly to additional resources at integration-time. In the spirit of IMA, it may be desirable to pool together these resources so that they can be allocated to any critical function at run-time. For this, a way to redefine the communication between individual allocations of functions is necessary. In this paper, we demonstrate and evaluate a prototypical implementation of a message router that allows us to dynamically reconfigure the communication between the allocated functions, using only standardized communication interfaces and operating system services of ARINC 653. We discuss the safety implications of such an approach and how it may be possible to mitigate them, evaluate the feasibility of our approach using a combination of end-to-end delay measurements and on-target tracing, and verify our assumptions about the individual factors contributing to the end-to-end delay using a discrete event simulation. We find that the approach is feasible, but the usefulness for critical functions is limited by the communications overhead from routing the messages, insufficient real-time guarantees of standardized operating system services, and missing global time synchronization.

Real-time scheduling for parallel tasks with resource reclamation

This paper considers the real-time scheduling of a parallel task with reclaiming computing resources, which can be utilized for soft real-time tasks or switching to low-energy mode to save energy. Existing works allocate a rectangular piece of computing resources based on the worst-case characterizations of the task to guarantee the deadline, which inherently incurs severe resource wasting due to coarse-grained resource allocation. To address this resource-wasting problem, this paper proposes the ladder-like resource allocation (i.e., a series of rectangular pieces of computing resources). To characterize the ladder-like resource allocation, we present two concepts called resource distribution and allocation vector, which serve as the interfaces between hard and soft real-time tasks. For the former, we derive schedulability tests under the given two interfaces; for the latter, we discuss the methods of determining the two interfaces to reclaim computing resources. This paper is the first work to fully explore the concept of ladder-like resource allocation and its potential consequences on computing resources, soft real-time tasks, and energy. Experiments demonstrate that the proposed approach can effectively reclaim more computing resources than existing approaches while maintaining hard real-time guarantees.

Time-triggered scheduling of mixed-critical flows at end-system in asynchronous AFDX avionic network

Avionics Full-DupleX (AFDX) is a switched Ethernet-based network used in modern commercial airplanes for the transmission of command and control avionics flows. These critical flows require deterministic guarantees leading to a lightly loaded network. Aircraft manufacturers envision to carry additional non avionics flows (i.e. video, audio, service) to take advantage of the spare bandwidth. However, it is then compulsory to preserve the real-time guarantees of avionics flows in terms of bounded jitter at the transmitter output and of bounded end-to-end latency. Depending on the type of additional traffic, Quality of Service (QoS) end-to-end guarantees may be offered to the additional flows of lower criticality in terms of reduced delay or bounded jitter for instance. These guarantees can be ensured by scheduling policies at transmitter and switch level. However, an important safety-related constraint is the asynchronous design of avionics distributed systems that prohibits the use of a network-wide synchronization of end systems and switches. Thus, time-triggered networking such as emerging Time-Triggered Ethernet (TTEthernet) or Time-Sensitive Networking (TSN) cannot be leveraged. This paper underlines the benefits of only scheduling flows at the transmitter, which is compatible with the asynchronous safety constraint of avionics systems. We show that it is possible to build a table schedule that carries both critical avionics flows and additional video flows that meet their timing and bandwidth allocation constraints. Therefore, we design scheduling strategies for efficient distribution of flows in the table scheduling whose performance is compared to the optimal schedule minimizing the emission lag of additional flows. Proposed heuristics are constructed such as to favor the network responsiveness for avionics flows thanks to slot over-provisioning. Extensive results on an A350 AFDX industrial configuration show that for a transmitter load of up to 70% of the available bandwidth, the uniform allocation heuristic provides a performance close to optimal in terms of minimum emission lag for additional flows, so offering a practical configuration heuristic to industry.

A Foundation for Real-time Applications onFunction-as-a-Service

Serverless (or Function-as-a-Service) compute model enables new applications with dynamic scaling. However, all current Serverless systems are best-effort, and as we prove this means they cannot guarantee hard real-time deadlines, rendering them unsuitable for such real-time applications. We analyze a proposed extension of the Serverless model that adds a guaranteed invocation rate to the serverless model called Real-time Serverless. This approach aims to meet real-time deadlines with dynamically allocated function invocations. We first prove that the Serverless model does not support real-time guarantees. Next, we analyze Real-time Serverless, showing it can guarantee application real-time deadlines for rate-monotonic real-time workloads. Further, we derive bounds on the required invocation rate to meet any set of workload runtimes and periods. Subsequently, we explore an application technique, pre-invocation, and show that it can reduce the required guaranteed invocation rate. We derive bounds for the feasible rate guarantee reduction, and corresponding overhead in wasted compute resources. Finally, we apply the theoretical results to improve the experience quality of a distributed virtual reality/ augmented reality application as well as simplify the application design and resource management.

Optimized QoS Routing in Software-Defined In-Vehicle Networks

To address the problems of low network centralized management, weak interaction, limited hardware scalability, compatibility issues and challenges in expansion in the static deployment of traditional in-vehicle networks (IVNs), a new IVN architecture is designed. At the same time, to better meet the IVN Quality of Service (QoS) requirements and improve the real-time guarantee of data transmission, the QoS routing optimization mechanism under the new architecture is established. First, a new IVN architecture including a forwarding plane, control plane and application plane is designed by introducing software defined network (SDN) technology and combining it with the IVN itself. Second, the end-to-end delay optimization model of IVN is established by introducing network calculus theory, defining system parameters, creating a network model, calculating latency, considering queuing and congestion. the traditional routing algorithm is improved, and the DBROA algorithm has been proposed, which enhances the performance of QoS routing by introducing features such as distributed routing decisions, beacon mechanisms, optimization algorithms, and adaptability. This improvement allows it to better meet the QoS requirements of various applications and services, thereby enhancing existing QoS routing algorithms. Finally, an IVN routing optimization system is built and implemented, and the performance of different algorithms is compared and analyzed. The experimental results show that compared with the traditional Dijkstra and ECMP algorithms, the DBROA algorithm can effectively reduce the data forwarding delay and packet loss rate, improve the overall performance of IVN, and provide better QoS guarantees for IVN real-time data transmission.

Intelligent logistics system based on visual detection of fruit freshness

Because of its tremendous potential for financial advantage, the academic and business communities have begun to focus a significant amount of attention on the intelligent logistics system. For the purpose of addressing the issue of quality supervision of fruit while it is being transported logistically, this study presents a proposal for an intelligent logistics system that is based on the detection of fruit freshness. The physical, chemical, and image processing techniques that are utilized at various points during the logistics process make up the core detection methods utilized by this system. The ability of the system to perform monitoring of the freshness of the fruit in real time guarantees that the fruit will be transported in a manner that is both secure and effective. The results of the analysis show that this system plays an essential part in a number of steps, including fruit harvesting, fruit sorting, transportation planning, and fruit delivery right to consumers front doors.

Real-Time Guarantees in Routerless Networks-on-Chip

This article considers the use of routerless networks-on-chip as an alternative on-chip interconnect for multi-processor systems requiring hard real-time guarantees for inter-processor communication. It presents a novel analytical framework that can provide latency upper bounds to real-time packet flows sent over routerless networks-on-chip, and it uses that framework to evaluate the ability of such networks to provide real-time guarantees. Extensive comparative analysis is provided, considering different architectures for routerless networks and a state-of-the-art wormhole network based on priority-preemptive routers as a baseline.

RTGPU: Real-Time GPU Scheduling of Hard Deadline Parallel Tasks With Fine-Grain Utilization

Many emerging cyber-physical systems, such as autonomous vehicles and robots, rely heavily on artificial intelligence and machine learning algorithms to perform important system operations. Since these highly parallel applications are computationally intensive, they need to be accelerated by graphics processing units (GPUs) to meet stringent timing constraints. However, despite the wide adoption of GPUs, efficiently scheduling multiple GPU applications while providing rigorous real-time guarantees remains a challenge. Each GPU application has multiple CPU execution and memory copy segments, with GPU kernels running on different hardware resources. Because of the complicated interactions between heterogeneous segments of parallel tasks, high schedulability is hard to achieve with conventional approaches. This paper proposes RTGPU, which combines fine-grain GPU partitioning on the system side with a novel scheduling algorithm on the theory side. Through system and theory co-design, RTGPU achieves superior system throughput and real-time schedulability. In this paper, we start by building a model for the CPU and memory copy segments. Leveraging persistent threads, we then implement fine-grained GPU partitioning with improved performance through interleaved execution. To reap the benefits of fine-grained GPU partitioning and schedule multiple parallel GPU applications, we propose a novel real-time scheduling algorithm based on federated scheduling and grid search with uniprocessor fixed-priority scheduling. Our approach provides real-time guarantees to meet hard deadlines, and achieves over 11% improvement in system throughput and up to 57% schedulability improvement compared with previous work. We validate and evaluate RTGPU on NVIDIA GTX1080Ti GPU systems. Our system side techniques can be applied on mainstream NVIDIA GPUs, and the proposed scheduling theory can be used in general heterogeneous computing platforms which have a similar task execution pattern.

WatchDog: Real-time Vehicle Tracking on Geo-distributed Edge Nodes

Vehicle tracking, a core application to smart city video analytics, is becoming more widely deployed than ever before thanks to the increasing number of traffic cameras and recent advances in computer vision and machine-learning. Due to the constraints of bandwidth, latency, and privacy concerns, tracking tasks are more preferable to run on edge devices sitting close to the cameras. However, edge devices are provisioned with a fixed amount of computing budget, making them incompetent to adapt to time-varying and imbalanced tracking workloads caused by traffic dynamics. In coping with this challenge, we propose WatchDog, a real-time vehicle tracking system that fully utilizes edge nodes across the road network. WatchDog leverages computer vision tasks with different resource-accuracy tradeoffs, and decomposes and schedules tracking tasks judiciously across edge devices based on the current workload to maximize the number of tasks while ensuring a provable response time-bound at each edge device. Extensive evaluations have been conducted using real-world city-wide vehicle trajectory datasets, achieving exceptional tracking performance with a real-time guarantee.

Privacy in the Era of 5G, IoT, Big Data, and Machine Learning

We have today a number of technologies that, when combined, can support unprecedented applications and significantly enhance existing applications. These technologies include 5G cellular networks, big data, machine learning, and the Internet of Things (IoT). The combination of those technologies allows us to: a) increase our capacity for pervasive, fine-grained, and continuous data gathering and for the effective and efficient processing of these data (even with real-time guarantees); b) generate knowledge from data and continuously evolve this knowledge, thus supporting recommendation systems and decision processes—also accompanied by suitable explanations; and c) make devices, control systems, and cyberphysical systems intelligent and autonomous.

Towards Hard Real-Time and Energy-Efficient Virtualization for Many-Core Embedded Systems

In safety-critical computing systems, the I/O virtualization must simultaneously satisfy different requirements, including time-predictability, performance, and energy-efficiency. However, these requirements are challenging to achieve due to complex I/O access path and resource management at the system level, lack of support from preemptive scheduling at I/O hardware level, and missing an effective energy management method. In this paper, we propose a new framework, I/O-GUARD, which reconstructs the system architecture of I/O virtualization, bringing a dedicated hardware hypervisor to handle resource management throughout the system. The hypervisor improves system real-time performance by enabling preemptive scheduling in I/O virtualization with both analytical and experimental real-time guarantees. Furthermore, we also present a dedicated energy management unit to adjust <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I/O-GUARD</i> 's dynamic energy using frequency scaling. Associated with that, a frequency identification algorithm is proposed to find the appropriate executing frequency at run-time. As shown in experiments, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I/O-GUARD</i> simultaneously improves the predictability, performance and energy-efficiency compared to the state-of-the-art I/O virtualization.

Evaluating Edge Computing and Compression for Remote Cuff-Less Blood Pressure Monitoring

Remote health monitoring systems play an important role in the healthcare sector. Edge computing is a key enabler for realizing these systems, where it is required to collect big data while providing real-time guarantees. In this study, we focus on remote cuff-less blood pressure (BP) monitoring through electrocardiogram (ECG) as a case study to evaluate the benefits of edge computing and compression. First, we investigate the state-of-the-art algorithms for BP estimation and ECG compression. Second, we develop a system to measure the ECG, estimate the BP, and store the results in the cloud with three different configurations: (i) estimation in the edge, (ii) estimation in the cloud, and (iii) estimation in the cloud with compressed transmission. Third, we evaluate the three approaches in terms of application latency, transmitted data volume, and power usage. In experiments with batches of 64 ECG samples, the edge computing approach has reduced average application latency by 15%, average power usage by 19%, and total transmitted volume by 85%, confirming that edge computing improves system performance significantly. Compressed transmission proved to be an alternative when network bandwidth is limited and edge computing is impractical.

Edge Computing Offloading Method Based on Deep Reinforcement Learning for Gas Pipeline Leak Detection

Traditional gas pipeline leak detection methods require task offload decisions in the cloud, which has low real time performance. The emergence of edge computing provides a solution by enabling offload decisions directly at the edge server, improving real-time performance; however, energy is the new bottleneck. Therefore, focusing on the gas transmission pipeline leakage detection scenario in real time, a novel detection algorithm that combines the benefits of both the heuristic algorithm and the advantage actor critic (AAC) algorithm is proposed in this paper. It aims at optimization with the goal of real-time guarantee of pipeline mapping analysis tasks and maximizing the survival time of portable gas leak detectors. Since the computing power of portable detection devices is limited, as they are powered by batteries, the main problem to be solved in this study is how to take into account the node energy overhead while guaranteeing the system performance requirements. By introducing the idea of edge computing and taking the mapping relationship between resource occupation and energy consumption as the starting point, the optimization model is established, with the goal to optimize the total system cost (TSC). This is composed of the node’s transmission energy consumption, local computing energy consumption, and residual electricity weight. In order to minimize TSC, the algorithm uses the AAC network to make task scheduling decisions and judge whether tasks need to be offloaded, and uses heuristic strategies and the Cauchy–Buniakowsky–Schwarz inequality to determine the allocation of communication resources. The experiments show that the proposed algorithm in this paper can meet the real-time requirements of the detector, and achieve lower energy consumption. The proposed algorithm saves approximately 56% of the system energy compared to the Deep Q Network (DQN) algorithm. Compared with the artificial gorilla troops Optimizer (GTO), the black widow optimization algorithm (BWOA), the exploration-enhanced grey wolf optimizer (EEGWO), the African vultures optimization algorithm (AVOA), and the driving training-based optimization (DTBO), it saves 21%, 38%, 30%, 31%, and 44% of energy consumption, respectively. Compared to the fully local computing and fully offloading algorithms, it saves 50% and 30%, respectively. Meanwhile, the task completion rate of this algorithm reaches 96.3%, which is the best real-time performance among these algorithms.

ASCENT: Communication Scheduling for SDF on Bufferless Software-Defined NoC

Bufferless software-defined network-on-chip (NoC) is a promising alternative to conventional dynamic routing as it offers predictable data movement with real-time guarantees. Existing time-division multiplexing (TDM)-based mechanisms for predictability assume the worst-case communication pattern (e.g., all-to-all) and compute a fixed schedule wherein the cores can only communicate during the allocated time slots. These approaches lead to low application throughput as they cannot adapt to application characteristics. In this article, we present an application specific, non-TDM-based communication scheduling mechanism for bufferless software-defined NoCs. We choose the synchronous dataflow (SDF) model of computation to represent the input streaming applications. We propose ASCENT, a novel offline approach that takes the SDF-specified streaming application and the NoC architecture as input, exploits the task interactions and the timing information in the SDF, and generates the task-to-core mapping and communication schedule that is represented compactly in hardware. ASCENT achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.8\times $ </tex-math></inline-formula> better performance on average than existing TDM-based NoCs and manages to achieve the performance of an ideal dynamically routed NoC, yet ensuring predictability.

Monte Carlo Noise Reduction Algorithm Based on Deep Neural Network in Efficient Indoor Scene Rendering System

Because of its flexibility and universality, Monte Carlo integral has become the preferred algorithm of most realistic image synthesis. However, the quality of rendered images is often affected by the estimated variance, which is mainly reflected in image noise visually. To reduce the variance, Monte Carlo rendering systems often require extensive sampling, which also causes a lot of time spent trying to render noiseless images. For this problem, we propose a Monte Carlo noise reduction algorithm based on deep neural networks and apply it to the efficient rendering of an indoor scene. The algorithm can reduce noise in real time. In order to solve the gradient disappearance problem of deep convolutional neural network, the residual structure of the network is added to the original convolutional network. The proposed algorithm can achieve better noise reduction quality in the real-time guarantee.

Edge robotics: are we ready? an experimental evaluation of current vision and future directions

Cloud-based robotics systems leverage a wide range of Information Technologies (IT) to offer tangible benefits like cost reduction, powerful computational capabilities, data offloading, etc. However, the centralized nature of cloud computing is not well-suited for a multitude of Operational Technologies (OT) nowadays used in robotics systems that require strict real-time guarantees and security. Edge computing and fog computing are complementary approaches that aim at mitigating some of these challenges by providing computing capabilities closer to the users. The goal of this work is hence threefold: i) to analyze the current edge computing and fog computing landscape in the context of robotics systems, ii) to experimentally evaluate an end-to-end robotics system based on solutions proposed in the literature, and iii) to experimentally identify current benefits and open challenges of edge computing and fog computing. Results show that, in the case of an exemplary delivery application comprising two mobile robots, the robot coordination and range can be improved by consuming real-time radio information available at the edge. However, our evaluation highlights that the existing software, wireless and virtualization technologies still require substantial evolution to fully support edge-based robotics systems.

Group Mobility Model for Complex Multimission Cooperation of UAV Swarm

In the unmanned aerial vehicle (UAV) swarm combat system, multiple UAVs’ collaborative operations can solve the bottleneck of the limited capability of a single UAV when they carry out complicated missions in complex combat scenarios. As one of the critical technologies of UAV collaborative operation, the mobility model is the basic infrastructure that plays an important role for UAV networking, routing, and task scheduling, especially in high dynamic and real-time scenarios. Focused on real-time guarantee and complex mission cooperative execution, a multilevel reference node mobility model based on the reference node strategy, namely, the ML-RNGM model, is proposed. In this model, the task decomposition and task correlation of UAV cluster execution are realized by using the multilayer task scheduling model. Based on the gravity model of spatial interaction and the correlation between tasks, the reference node selection algorithm is proposed to select the appropriate reference node in the process of node movement. This model can improve the real-time performance of individual tasks and the overall mission group carried out by UAVs. Meanwhile, this model can enhance the connectivity between UAVs when they are performing the same mission group. Finally, OMNeT++ is used to simulate the ML-RNGM model with three experiments, including the different number of nodes and clusters. Within the three experiments, the ML-RNGM model is compared with the random class mobility model, the reference class mobility model, and the associated class mobility model for the network connectivity rate, the average end-to-end delay, and the overhead caused by algorithms. The experimental results show that the ML-RNGM model achieves an obvious improvement in network connectivity and real-time performance for missions and tasks.

Reliability Analysis of the Proactive Transmission of Replicated Frames Mechanism over Time-Sensitive Networking.

The Time-Sensitive Networking (TSN) Task Group has standardised different mechanisms to provide Ethernet with hard real-time guarantees and reliability in layer 2 of the network architecture. Specifically, TSN proposes using space redundancy to increase the reliability of Ethernet networks, but using space redundancy to tolerate temporary faults is not a cost-effective solution. For this reason, we propose to use time redundancy to tolerate temporary faults in the links of TSN-based networks. Specifically, in previous works we proposed the Proactive Transmission of Replicated Frames (PTRF) mechanism to tolerate temporary faults in the links. Now, in this work we present a series of models of TSN and PTRF developed using PRISM, a probabilistic model checker that can be used to evaluate the reliability of systems. After that, we carry out a parametric sensitivity analysis of the reliability achievable by TSN and PTRF and we show that we can increase the reliability of TSN-based networks using PTRF to tolerate temporary faults in the links of TSN networks. This is the first work that presents a quantitative analysis of the reliability of TSN networks.

Online design of green urban garden landscape based on machine learning and computer simulation technology

With the development of machine learning and computer simulation technology, green urban garden landscapes have also made breakthrough progress in modeling. Based on the existing basic technology and its shortcomings, this article discusses the timeliness of the Agent model, defines the time granularity of simulation machine learning, and establishes an Agent model related to the time granularity of machine learning. The corresponding time management mechanism under real-time simulation is constructed, and the switching of machine learning time granularity, real-time guarantee and message interaction mechanism are studied. We use the new model to model the spatial entities to illustrate the modeling method, and design the message interaction between the spatial entities. We analyze the parametric works in the design world from the aspects of nature, art and computer, and summarize the general law of parametric design. Computer-aided design includes the study of traditional calculation-aided design methods. This paper analyzes its existing problems and proposes new methods of parametric design and the generation of computer geometric algorithms. We compare with Auto CAD, which is commonly used in garden and landscape design, and summarize their advantages and disadvantages. The application of the machine learning Grasshopper parameter platform to the design process of actual projects fully reflects its scientific, rational and advanced nature. • The article discusses the timeliness of the Agent model. • We establish an Agent model related to the time granularity of machine learning. • We propose a new method of parametric design.

Protection switching schemes and mapping strategies for fail-operational hard real-time NoCs

Communication infrastructures designed for mixed-critical MPSoCs must provide isolation of traffic, hard real-time guarantees, and fault-tolerance. In previous work, we proposed the combination of protection-switching with a hybrid Time-Division-Multiplexed (TDM) and packet-switched Network-on-Chip (NoC) to achieve all three goals. In this paper, we present an FPGA implementation of such a NoC with all its features. We give synthesis results for the hybrid NoC, including the network interface, and show that our router uses over 32% fewer LUTs and registers than a competitive state-of-the-art router for mixed-critical MPSoC. We then explore different channel and task mapping strategies for critical applications which use protection switching and evaluate the effect these mappings have on the best-effort (BE) traffic in the system. Results show, that spreading out the critical traffic rather than naively dividing the system in critical and non-critical application domains is advantageous or even necessary in many cases and can allow for up to 13% more BE traffic. We give a comprehensive trade-off analysis of three protection switching schemes—1:n, 1:1, and 1+1—and show that 1+1 protection has less than half the worst case latency for critical traffic that 1:n and 1:1 protection have. At the same time, 1+1 protection, on average, only causes a 1.18% earlier saturation rate for BE traffic, which we consider to be affordable. We conclude that 1+1 protection is ideally suited for use in mixed-critical systems with high safety requirements.