Showing papers presented at "International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing in 2019"

On the adequacy of SDN and TSN for Industry 4.0

Abstract: Industry 4.0, Industrial Internet of Things, Cyber-Physical Production Systems and Smart Factories are closely related emerging concepts expected to drive significant improvements in industrial production systems, with gains in efficiency, cost and customer satisfaction. These concepts are intimately associated with highly distributed and cooperative architectures that rely, naturally, on the network infrastructure. However, traditional industrial communication technologies hardly provide the required level of integration, flexibility and performance. Seeking a solution to this mismatch, we assess two technologies that appeared recently in the industrial realm, namely IEEE 802.1 Time-Sensitive Networking (TSN) and Software-Defined Networking (SDN). TSN and SDN are fundamentally different, thus having distinct strengths and weaknesses. This paper reviews their fundamental operation principles, evaluating them qualitatively against the specific requirements posed by Industry 4.0.

URMILA: A Performance and Mobility-Aware Fog/Edge Resource Management Middleware

Abstract: Fog/'Edge computing is increasingly used to support a wide range of latency-sensitive Internet of Things (IoT) applications due to its elastic computing capabilities that are offered closer to the users Despite this promise, IoT applications with user mobility face many challenges since offloading the application functionality from the edge to the fog may not always be feasible due to the intermittent connectivity to the fog, and could require application migration among fog nodes due to user mobility Likewise, executing the applications exclusively on the edge may not be feasible due to resource constraints and battery drain To address these challenges, this paper describes URMILA, a resource management middleware that makes effective tradeoffs between using fog and edge resources while ensuring that the latency requirements of the IoT applications are met We evaluate URMILA in the context of a real-world use case on an emulated but realistic IoT testbed

An Experimental Analysis of the Xen and KVM Latencies

Abstract: The recent developments in virtualisation technologies have made feasible the execution of complex and performance critical applications in virtual machines. When such applications are characterised by real-time constraints, the virtual machines must be scheduled predictably over the physical cores. Several works in real-time literature have analysed such a scenario, proposing advance scheduling and design techniques to respect the application constraints. However, most of the previous works focused on scheduling algorithms and theoretical analysis, without considering important implementation details such as the latencies introduced by the virtualisation mechanism. The paper, which can be seen as a complement for such works, investigates the latencies introduced by two of the most widely used hypervisors, Xen and KVM, so that previous theoretical analysis and algorithms can be used in practice.

Simulation Framework for Clock Synchronization in Time Sensitive Networking

Abstract: Hard real-time systems like industrial control applications have strict temporal requirements. Many hard real-time systems depend on a global time base for coordinating access to shared resources and for time-stamping events. Hence, the Time Sensitive Networking (TSN) task group introduces a fault-tolerant and robust clock synchronization mechanism (i.e. IEEE 802.1AsRev) that results in a synchronized network. This paper presents a simulation framework for IEEE 802.1AsRev to evaluate the reliability of the global time base in TSN-based systems. The simulation models are developed on top of our existing TSN models that support the time-based features of TSN (e.g. IEEE 802.1Qbv and IEEE 802.1Qci standards). Moreover, the evaluation of different TSN synchronization modules such as Best Master Clock Algorithm (BMCA), synchronization and peer delay measurement are carried out in our simulation framework. We also study the behavior of IEEE 802.1AsRev in the presence of either a node failure or a link failure using an example scenario of a train communication network. The experimental results validate the correctness and applicability of TSN clock synchronization for modern cyber physical systems with demanding timing requirements.

Augmenting Learning Components for Safety in Resource Constrained Autonomous Robots

Abstract: Learning enabled components (LECs) trained using data-driven algorithms are increasingly being used in autonomous robots commonly found in factories, hospitals, and educational laboratories. However, these LECs do not provide any safety guarantees, and testing them is challenging. In this paper, we introduce a framework that performs weighted simplex strategy based supervised safety control, resource management and confidence estimation of autonomous robots. Specifically, we describe two weighted simplex strategies: (a) simple weighted simplex strategy (SW-Simplex) that computes a weighted controller output by comparing the decisions between a safety supervisor and an LEC, and (b) a context-sensitive weighted simplex strategy (CSW-Simplex) that computes a context-aware weighted controller output. We use reinforcement learning to learn the contextual weights. We also introduce a system monitor that uses the current state information and a Bayesian network model learned from past data to estimate the probability of the robotic system staying in the safe working region. To aid resource constrained robots in performing complex computations of these weighted simplex strategies, we describe a resource manager that offloads tasks to an available fog nodes. The paper also describes a hardware testbed called DeepNNCar, which is a low cost resource-constrained RC car, built to perform autonomous driving. Using the hardware, we show that both SW-Simplex and CSW-Simplex have 40% and 60% fewer safety violations, while demonstrating higher optimized speed during indoor driving (~ 0.40 m/s) than the original system (using only LECs).

Time-efficient offloading for machine learning tasks between embedded systems and fog nodes

Abstract: The Internet of Things (IoT) and Machine Learning (ML) introduce embedded systems to many new roles and functions, but the current status quo of using these technologies together can be improved. The status quo has embedded systems offloading all of their ML functionality to an external device, but this can lead to unpredictable throughput due to network instability. We propose to run low-complexity ML models on the embedded system itself and distribute the workload when it has been measured to bypass a Worst-Case Execution Time (WCET) threshold.

A Practical Degradation Model for Mixed-Criticality Systems

Abstract: Existing Mixed Criticality System (MCS) task models consider criticality as the relative importance of a task and use it to achieve graceful degradation of the system either by suspending or degrading tasks with relatively lower criticality level than the overloading task. Graceful degradation based on this notion of criticality as a relative parameter may not always be desired. In this paper, we propose a Context-Aware Mixed Criticality System (CA-MCS) model with which each task can be specified with multiple degraded budgets. To handle a system overload due to timing faults i.e. budget overrun of a task, tasks that need to be degraded are chosen irrespective of their criticality level. However, to handle system overload when multiple tasks overrun their budgets, a specific degraded budget of the task chosen for degradation is decided based on the criticality level of the overrun tasks. Experiments performed in a realistic automotive testbed confirm the benefit of CA-MCS model when compared with the state-of-the art degradation strategies. Results show that contextaware degradation gives the ability to degrade the performance in a controlled manner by effectively isolating the effects of degradation between applications. Finally, we derive a sufficient schedulability test under fixed priority scheduling scheme for the CA-MCS model.

Optimal Scheduling of Precedence-constrained Task Graphs on Heterogeneous Distributed Systems with Shared Buses

Abstract: Real-time applications in today's distributed cyber-physical control systems are often represented as Precedence-constrained Task Graphs (PTGs) and increasingly implemented on heterogeneous platforms to cater to their high performance demands. Optimal scheduling solutions for such systems can provide advantages in terms of performance, reliability, cost etc. However, existing research works dealing with the optimal scheduling of PTGs, typically assume platforms consisting of homogeneous processing elements which interact through a fully connected network of homogeneous communication channels. In this work, we propose an Integer Linear Programming based optimal solution strategy for scheduling PTGs executing on a distributed platform composed of heterogeneous processing elements and inter-connected through a set of heterogeneous shared buses. Through the real-world case study of an automotive cruise controller, we generate an optimal schedule using our proposed scheme in order to demonstrate its generic applicability. Conducted experiments on benchmark PTGs reveal the practical efficacy of our scheme.

On the Design of Fault- Tolerance in a Decentralized Software Platform for Power Systems

Abstract: The vision of the ‘Smart Grid’ assumes a distributed real-time embedded system that implements various monitoring and control functions. As the reliability of the power grid is critical to modern society, the software supporting the grid must support fault tolerance and resilience in the resulting cyber-physical system. This paper describes the fault-tolerance features of a software framework called Resilient Information Architecture Platform for Smart Grid (RIAPS). The framework supports various mechanisms for fault detection and mitigation and works in concert with the applications that implement the grid-specific functions. The paper discusses the design philosophy for and the implementation of the fault tolerance features and presents an application example to show how it can be used to build highly resilient systems.

Untangling the Intricacies of Thread Synchronization in the PREEMPT_RT Linux Kernel

Abstract: This article proposes an automata-based model for describing and validating the behavior of threads in the Linux PREEMPT_RT kernel, on a single-core system. The automata model defines the events and how they influence the timeline of threads' execution, comprising the preemption control, interrupt handlers, interrupt control, scheduling and locking. This article also presents the extension of the Linux trace features that enable the trace of the kernel events used in the modeling. The model and the tracing tool are used, initially, to validate the model, but preliminary results were enough to point to two problems in the Linux kernel. Finally, the analysis of the events involved in the activation of the highest priority thread is presented in terms of necessary and sufficient conditions, describing the delays occurred in this operation in the same granularity used by kernel developers, showing how it is possible to take advantage of the model for analyzing the thread wake-up latency, without any need for watching the corresponding kernel code.

Towards a Realistic Simulation Framework for Vehicular Platooning Applications

Abstract: Cooperative vehicle platooning applications increasingly demand realistic simulation tools to ease their validation, and to bridge the gap between development and real-word deployment. However, their complexity and cost, often hinders its validation in the real-world. In this paper we propose a realistic simulation framework for vehicular platoons that integrates Gazebo with OMNeT++ over Robot Operating System (ROS) to support the simulation of realistic scenarios of autonomous vehicular platoons and their cooperative control.

Quantifying Performance Determinism in Virtualized Mixed-Criticality Systems

Abstract: Virtualization has been a leading technology for managing shared infrastructures. Virtualization reduces infrastructure cost, enhances security, provides isolation and flexibility for migrating/upgrading systems. However, as virtualization is being pushed into defense and mission-critical applications, new challenges are emerging. For instance, U.S. Department-of-Defense (DoD)s recent initiative to deploy a cloud-based Joint-Enterprise Defense Infrastructure (JEDI) brings in several questions, mainly, how to ensure deterministic performance for mission-critical applications in a virtualized environment. Similar to avionic systems with mixed-criticality tasks, mission-critical applications and systems require lengthy software certifications that assess the determinism and guaranteed execution time of such applications. Unfortunately, we observe several challenges on leveraging cloud for time-critical jobs. Thus, in this paper, we characterize and investigate the determinism of performance for applications on cloud systems and compare between state-of-the-art schedulers and real-time implementations.

Semi-Federated Scheduling of Mixed-Criticality System for Sporadic DAG Tasks

Abstract: DAG task model is a general parallel task model that has been widely concerned and studied by researchers The combination of mixed-criticality and DAG task model makes it difficult to analyze system behaviors Under federated mixed-criticality scheduling algorithm, tasks are physically isolated with regard to computation resources, which leads to lower analysis complexity and better performance However, federated mixed-criticality scheduling algorithm suffers resource waste as in federated scheduling, and almost half of processor resources can be wasted in extreme cases In this paper, we address the problem and propose a novel semi-federated mixed-criticality algorithm (SFMC) SFMC combines semi-federated scheduling with mixed-criticality systems, whose original architecture is changed to a dual-hierarchical one When analyzing the combined system, we first allocate finer-grained processor resources to each MC DAG task, then we prove the correctness of the SFMC algorithm in both normal and critical states The proposed algorithm is evaluated on randomly generated independent DAG task sets based on OpenMP benchmarks Experiment results present that our algorithm has better performance on schedulability than the federated mixed-criticality scheduling algorithm

Adapting the Concept of Artificial DNA and Hormone System to a classical AUTOSAR Environment

Abstract: Embedded systems are growing very complex because of the increasing chip integration density, larger number of chips in distributed applications and demanding application fields e.g. in autonomous cars. Bio-inspired techniques like self-organization are a key feature to handle the increasing complexity of embedded systems. In biology the structure and organization of a system is coded in its DNA, while dynamic control flows are regulated by the hormone system. We adapted these concepts to embedded systems using an artificial DNA (ADNA) and an artificial hormone system (AHS). Based on these concepts, highly reliable, robust and flexible systems can be created. These properties predestine the ADNA and AHS for the use in future automotive applications. However, computational resources and communication bandwidth are often limited in automotive environments. Furthermore, in many critical areas, classical AUTOSAR in combination with CAN bus is used as a static operating system. Nevertheless, in this paper we show that the dynamic concept of ADNA and AHS can be successfully applied to a static system like AUTOSAR and the available computational resources are more than sufficient for automotive applications. The major bottleneck becomes the CAN bus communication when implemented on top of AUTOSAR's communication stack as this limits the maximum achievable throughput for a single device to provide bandwidth for numerous different participants. Implementing the CAN bus communication directly through AUTOSAR's CAN driver mostly removed this problem. Keywords: Artificial DNA, artificial hormone system, self-organization, automotive, CAN bus, AUTOSAR

Hatebefi: Hybrid Applications Testbed for Fault Injection

Abstract: Hybrid testbeds are popular for testing distributed software systems, like network protocols and distributed applications, since the beginning of the 2000s. Combining physical and virtual resources for testing these networked computer systems allows to leverage the advantages and mitigate the disadvantages of either one. However, hybrid testbeds introduce novel challenges, e.g. regarding plausibility, heterogeneity, and controllability. To counter these challenges, we introduce the Hybrid Applications Testbed for Fault Injection (Hatebefi) which combines two approaches: On the one hand, Hatebefi aims to increase the plausibility by integrating hybrid testbeds with domain-specific simulators (e.g. for traffic simulation). This integration also addresses the heterogeneity of contemporary distributed applications. On the other hand, our framework allows for efficient creation and execution of complex test scenarios with a high degree of controllability by offering an event-based execution model. To demonstrate the feasibility of our approach, we implemented a basic set of arbitrarily combinable events to cover the most common scenarios. Both features combined pave the way to test distributed software systems, like Internet of Things applications involving connected vehicles or smart cities.

The Colored Refresh Server for DRAM

Abstract: Bounding each task's worst-case execution time (WCET) accurately is essential for real-time systems to determine if all deadlines can be met. Yet, access latencies to Dynamic Random Access Memory (DRAM) vary significantly due to DRAM refresh, which blocks access to memory cells. Variations further increase as DRAM density grows. This work contributes the “Colored Refresh Server” (CRS), a uniprocessor scheduling paradigm that partitions DRAM in two distinctly colored groups such that refreshes of one color occur in parallel to the execution of real-time tasks of the other color. By executing tasks in phase with periodic DRAM refreshes with opposing colors, memory requests no longer suffer from refresh interference. Experimental results confirm that refresh overhead is completely hidden and memory throughput enhanced.

Short Paper: Towards An Edge-Located Time-Series Database

Abstract: Smart infrastructure demands resilient data storage, and emerging applications execute queries on this data over time. Typically, time-series databases serve these queries; however, cloud-based time-series storage can be prohibitively expensive. As smart devices proliferate, the amount of computing power and memory available in our connected infrastructure provides the opportunity to move resilient time-series data storage and analytics to the edge. This paper proposes time-series storage in a Distributed Hash Table (DHT), and a novel key-generation technique that provides time-indexed reads and writes for key-value pairs. Experimental results show this technique meets demands for smart infrastructure situations.

Interfacing to Time-Triggered Communication Systems

Abstract: Time-triggered communication facilitates the construction of multi-component real-time systems whose components are in control of their temporal behavior. However, the interface of a time-triggered communication system has to be accessed with care, to avoid that the temporal independence of components gets lost. This paper shows two interfacing strategies, one for asynchonous interface access (in two variants, one being the new Rate-Bounded Non-Blocking Communication protocol) and one for time-aware, synchronized interface access, that allow components to maintain temporal independence. The paper describes and compares the interfacing strategies.

Toward Resilient Stream Processing on Clouds using Moving Target Defense

Abstract: Big data platforms have grown popular for real-time stream processing on distributed clusters and clouds. However, execution of sensitive streaming applications on shared computing resources increases their vulnerabilities, and may lead to data leaks and injection of spurious logic that can compromise these applications. Here, we adopt Moving Target Defense (MTD) techniques into Fast Data platforms, and propose MTD strategies by which we can mitigate these attacks. Our strategies target the platform, application and data layers, which make these reusable, rather than the OS, virtual machine, or hardware layers, which are environment specific. We use Apache Storm as the canonical distributed stream processing platform for designing our MTD strategies, and offer a preliminary evaluation that indicates the feasibility and evaluates the performance overheads.

Towards Contact-less Vital Sign Monitoring using a COTS Resource-Constrained Multi-Core System - an Experience report -

Abstract: Contact-less vital-sign monitoring is enabling in situations where traditional contact-based vital sign monitoring is prohibitive, such as neonatal and burn care where electrodes and attachments risk patient infection or complications To make vital-sign monitoring available for the consumer market, these algorithms shall use COTS cameras and run on COTS systems In this paper, we report upon the results of an experiment to improve the performance of a contact-less respiration algorithm on a resource-constrained COTS multi-core system, in our case a Raspberry Pi 3 running Linux, using a state-of-the-art tool to visualize the behavior of a system The improvements are an increase of the CPU utilization from 72% to almost 94% and an increase of the throughput of the algorithm by more than 45%

Linearization based Safety Verification of a Glucose Control Protocol

Abstract: Medical cyber-physical systems in which multiple medical devices co-ordinate with each other and provide closed-loop control to the patient, have come into prominence in the recent past. One of the main challenges for such systems is guaranteeing their safety even in the presence of significant physiological variabilities among patients. Formal verification based on well-established models of patient physiology has emerged as a potential solution to this problem. However, such techniques face a significant hurdle in terms of scalability due to two main reasons; non-linearity in the physiological models, and large variations in the model parameters due to intra and inter-patient variabilities. In this work, we considered a case-study system of pre-operative and intra-operative care for diabetic patients based on a well-established insulin-infusion protocol. The system comprises a physiological model of the glucose-insulin regulatory system based on Dallaman's model integrated with a proportional-derivative controller that encodes the insulin-infusion protocol. Towards addressing the verification scalability problem, we present a solution for this case-study based on well-known model linearization techniques. We also calculated the error in linearization and incorporated the error into the linearized model. We have constructed both hybrid system model and the corresponding linearized model along with the error and have verified them using dReach and SAL verification tools, respectively. The non-linear model remained non-verifiable for a depth of 8 even after running the verification for more than 20 hours. However, the linearized model was found to be fully verifiable for all the cases and also 2x times faster than the non-linear model for a depth of 7. Therefore, safety of the nonlinear model can be verified with some approximation using the corresponding linearized model.

Improving the performance of a Publish-Subscribe message broker

Abstract: The Arrowhead Framework, a SOA-based framework for IoT applications, provides the Event Handler system: a publish/subscribe broker implemented with REST/HTTP(S). However, the existing implementation of the Event Handler suffers from message latency problems that are not acceptable for industrial applications. Thus, this paper describes the refactoring process of this system that enabled it to reach acceptable levels of latency.

Packet Priority Assignment for Wireless Control Systems of multiple Physical Systems

Abstract: Wireless control systems (WCSs) have gained much attention lately, due to their easy deployment and flexibility compared to wired control systems. However, this comes at the cost of possibly increased network delay and packet losses, that can significantly impact the control system performance, and possibly its stability. Such problems become even more relevant if the network is shared among different control systems, and thus becomes a scarce resource, like in Industrial Internet of Things applications. In this paper, we describe how to assign packet priorities dynamically when there are many physical systems sharing a given network, aiming at minimizing the performance degradation of the WCS. Towards that, we present a network model including both delay and packet losses, both of which are very important for the control system performance. Our solution is evaluated over two different use cases to show the generality of the approach: the WCS for a set of inverted pendula, and the WCS for small modular reactors in a nuclear power plant. The results show that the proposed approach allows for a more stable performance even in presence of highly nonlinear systems, sensitive to time-varying delays, as well as in presence of high network interference.

Optimal Load-Balancing Association Scheme for C-RANs with Mobile IoT Devices

Abstract: In this paper, we study an optimal load balancing association scheme for a C-RAN (Cloud-based Radio Access Network) with mobile IoT (Internet of Things) devices. In C-RANs, short-range low-complex RRHs (Remote Radio Heads) are densely deployed to provide coverage. However, due to their limited processing power, it is critical to evenly distribute the traffic load. In addition, when user devices are mobile, frequent handovers can generate significant amount of control messages. In this regard, we propose an optimal load balancing association scheme between RRHs and mobile devices that minimizes the handovers as well. We first formulate an optimal load-balancing problem. Then, we revise the formulation such that an occurrence of handovers is penalized. We implement a discrete-time network simulator to evaluate and compare the performance of the proposed algorithm to conventional load balancing schemes. The evaluation results show that the proposed scheme can evenly distribute the traffic load, while minimizing the handovers.

Incorporating Robustness and Resilience into Mixed-Criticality Scheduling Theory

Abstract: Mixed-criticality scheduling theory (MCSh) was developed to allow for more resource-efficient implementation of systems comprising different components that need to have their correctness validated at different levels of assurance. As originally defined, MCSh deals exclusively with pre-runtime verification of such systems; hence many mixed-criticality scheduling algorithms that have been developed tend to exhibit rather poor survivability characteristics during run-time. (E.g., MCSh allows for less-important (“Lo-criticality”) workloads to be completely discarded in the event that run-time behavior is not compliant with the assumptions under which the correctness of the LO-criticality workload should be verified.) Here we seek to extend MCSh to incorporate survivability considerations, by proposing quantitative metrics for the robustness and resilience of mixed-criticality scheduling algorithms. Such metrics allow us to make quantitative assertions regarding the survivability characteristics of mixed-criticality scheduling algorithms, and to compare different algorithms from the perspective of their survivability. We propose that MCSh seek to develop scheduling algorithms that possess superior survivability characteristics, thereby obtaining algorithms with better survivability properties than current ones (which, since they have been developed within a survivability-agnostic framework, tend to focus exclusively on pre-runtime verification and ignore survivability issues entirely).

Fast. Efficient Performance Predictions for Big Data Applications

Abstract: In recent years we observe a rapid growth in the deployment of machine learning workloads on big data analytics frameworks like Apache Spark and Apache Flink. These workloads are typically represented as graphs, run on shared infrastructures and often have much more demanding resource requirements than those traditionally found in typical enterprise settings. However, predicting the execution times of the workloads is important as they often run on shared public or private infrastructures and, thus, their execution is greatly affected by the resource sharing, the hardware infrastructure utilized as well as the choice of the configuration parameters provided by the frameworks. In this work, we propose a fast and efficient performance prediction system to address the challenge of predicting the execution times of big data workloads, exploiting the fact that workloads are represented as processing graphs and often share similar structures and parameters. Thus, we can use the performance models we have built for already deployed workloads, to estimate the end-to-end execution time for a new workload. Previous works assume that a large number of profiling runs can be utilized for building the prediction models. However, this assumption is not always valid and more elaborate mechanisms need to be applied. Our detailed experimental evaluation on our local Spark cluster illustrates that our approach can predict accurately the execution time of a wide range of Spark workloads.

A Quantitative Approach for Calculating Model Assurance Levels

Abstract: Model-based engineering (MBE) in industry is on the rise However, improvements are still needed on the oversight and management of MBE efforts Frequently, program managers and high level decision makers do not have background in MBE to understand models, the value the models are providing, and if they are successfully achieving their MBE goals To address these concerns in our previous research we developed a rating scale for models, called Model Assurance Levels (MALs) A MAL level is used to concisely express the assurance the model is providing to the program, as well as, risks associated with the model However, for MAL level to be effective and meaningful, they need to be consistently determined regardless of who is performing the assessment Therefore, this paper describes a quantitative and repeatable approach for computing MAL levels for software models

Towards an Artificial DNA for the Use in Dynamic Environments

Abstract: In the last decade, bio-inspired techniques like self-organization and emergence have been in the focus of several research projects to deal with the challenge to develop, to configure and to maintain highly distributed and embedded systems. In biology the structure and organization of a system is coded in its DNA, and several dynamic control flows are regulated by the hormone system. We adapted these concepts to embedded systems using an artificial DNA (ADNA) and an artificial hormone system (AHS). Based on these concepts, highly reliable, robust and flexible systems can be created. These properties predestine the ADNA and AHS for the use in future automotive applications. We showed in recent publications several examples for the use of the ADNAI AHS approach dealing with automotive applications running on the processors of a single distributed system of processors. In this contribution, we go one step further and demonstrate how to use the ADNA/AHS approach for applications running on the processors of different distributed systems (like driving cars) of processors which merge and separate at run-time.

Demonstration of a Time-predictable Flight Controller on a Multicore Processor

Abstract: Unmanned aerial vehicles, or drones, have drawn extensive attention during the last decade together with the maturity of the technology. Often, real-time requirements are needed when they are deployed for critical missions. The increasing computational demand for drones leads to a shift towards multicore architectures. However, the timing analysis of multicore systems is a challenging task due to the timing interference between processor cores. In this demonstration, we deploy a parallelized flight controller system on a multicore architecture. We provide timing analysis of the system, and test it with a processor-in-the-loop setup, which includes a flight simulator connected to the flight controller running on a time-predictable multicore platform.

Improving Multiprocessor Real-Time Systems with Bursty Inputs under Global EDF using Shapers

Abstract: We propose an approach to calculate delay bound for multiprocessor real-time systems scheduled by GEDF. Different from most existing analysis techniques analyzing sporadic tasks, we consider bursty tasks which have more general arrival patterns. In detail, we use shapers to eliminate burst in original system inputs and generate sporadic job sequences, and then calculate the delay bound of each task. To further improve our approach, we design a heuristic algorithm to make as more tasks as possible to meet their deadlines by adjusting settings of shapers. Experiments show that the proposed algorithm can lead to improvement of acceptance ratio and the delay bound derived is much smaller than that by compared existing work.