Tasks In Real-time Systems Research Articles

Estimation of timing characteristics in real-time computer systems using Petri nets

The work is devoted to the problem of determining the time characteristics of tasks in real-time systems, the success of which depends not only on their logical correctness, but also on the time for which they receive the result. Determining such time characteristics of the system at the design stage is a rather difficult problem. Its solution is currently based on the use of two main methods: theoretical calculations related to the calculation of the so-called feasibility criteria and modeling of the system's operation on models. Among the models, statistical models of mass service systems are the most widespread. However, in both the first and second options, it is impossible to obtain a guaranteed result, which significantly complicates the design process. Recently, it has been proposed to use the methods of researching models based on the use of the Petri net apparatus. The paper proposes a method for estimating the time characteristics of tasks in real-time systems by means of data analysis. Obtained by modeling the distribution of processor time between tasks according to the selected algorithms of the scheduler by using a model of the software system in the form of a Petri net. The paper proposes a method for estimating the time characteristics of tasks in real-time systems by means of data analysis. Obtained by modeling the distribution of processor time between tasks according to the selected algorithms of the scheduler by using a model of the software system in the form of a Petri net. The method guarantees obtaining the time characteristics of the tasks when choosing specific types of processor and scheduler, which is required to start the technical design of the real-time system. The use of the proposed mathematical model and a package of application programs allow, at the early stages of the development of a real-time system, to determine the real terms of tasks and choose the optimal option for the implementation of technical and lost support. The proposed mathematical models are based on determining the time characteristics of program execution by modeling the distribution of processor time between tasks, subject to the prior selection of planning algorithms and the characteristics of a complex of technical means. The research is based on the use of simulated statistical models and Petri net models, The use of the proposed tools allows you to significantly reduce the time of analysis of possible options for real-time system implementation, increases the quality of the project and significantly reduces the overall time and cost of the entire development. The paper examines in detail the process of modeling a multitasking computer system using a Petri net device. A concrete example shows the simulation of the operation of a complex of programs, including the development of simulation algorithms, the listing of the simulation program, and the analysis of the obtained results.

Local multi-scale feature aggregation network for real-time image dehazing

Haze causes visual degradation and obscures image information, which gravely affects the reliability of computer vision tasks in real-time systems. Leveraging an enormous number of learning parameters as the restoration costs, learning-based methods have gained significant success, but they are runtime intensive or memory inefficient. In this paper, we propose a local multi-scale feature aggregation network, called LMFA-Net, which has a lightweight model structure and can be used for real-time dehazing. By learning the local mapping relationship between the clean value of a haze image at a certain point and its surrounding local region, LMFA-Net can directly restore the final haze-free image. In particular, we adopt a novel multi-scale feature extraction sub-network (M-Net) to extract features from different scales. As a lightweight network, LMFA-Net can achieve fast and efficient dehazing. Extensive experiments demonstrate that our proposed LMFA-Net surpasses previous state-of-the-art lightweight dehazing methods in both quantitatively and qualitatively.

Legal Management of Network Information Security Based on Embedded Real-Time Task Processing.

Nowadays, with the continuous advancement of globalization, Internet technology has made an unprecedented leap, which makes the amount of data on the Internet grow exponentially. Data are not only diverse and numerous, but also spread fast and of high value. Therefore, the Internet is entering the era of big data. However, the openness of information network itself, the immaturity of technology, and the imperfection of network security law make it vulnerable to network attacks, resulting in the disclosure of network users' information and the theft of their privacy data. The formulation of network information security law to protect network information security is of great significance for the development of multidomain network information security under the background of big data and ensuring users' online security. Firstly, this article introduces the basic information of embedded real-time task system, analyzes the factors of real-time Linux based on kernel preemption, interrupts mechanism and policy planning, and summarizes the process of some embedded real-time task processing. Integrating legal management, this article puts forward three elements of network security control mechanism, namely, network control personnel, environment, and technology. Taking these three elements as the starting point of the construction of information security control mechanism, this article provides the steps of using evaluation model to evaluate network information security. Finally, it identifies the problems existing in the legal management of network information security and formulates the corresponding strategies. By combining embedded technology and real-time task processing technology, this study applies it to the field of network information security legal management, to promote its development.

Energy-Aware Nonpreemptive Scheduling of Mixed-Criticality Real-Time Task Systems

Energy-aware real-time scheduling for mixed-criticality (MC) systems with different criticality levels has drawn many researchers’ attentions. However, most of the studies focus on the preemptive MC task model and few studies consider the nonpreemptive MC task model, in which all jobs cannot be preempted until completion. In this article, we address the energy minimization problem for MC systems with nonpreemptive dynamic priority scheduling. First, we develop schedulability test of nonpreemptive earliest deadline first (NP-EDF) in single processor MC systems. Second, we extend the results to nonpreemptive earliest deadline first with virtual deadline (NP-EDFVD), which is the first attempt for nonpreemptive dynamic priority scheduling in single processor MC systems. Third, the energy-aware nonpreemptive scheduling algorithm (EANPS) based on NP-EDFVD is proposed to solve the energy minimization problem for MC systems with nonpreemptive dynamic priority scheduling. Finally, an industrial use-case and extensive simulations are used to validate the performance of the proposed algorithm, and the experimental results show that the EANPS algorithm consumes average 25.72% less energy than that of NP-EDFVD.

The Execution Efficiency of the Response Time Analysis under Different Task Sequencing Strategies

Tasks in real-time systems have strict time requirements and need to be completed before their deadlines. Whenever the real-time task set changes, the real-time system reruns the schedulability analysis method to determine whether the tasks in the new task set can be completed before their deadlines. Since new tasks can not be executed until the execution of the schedulability analysis method is completed, the execution efficiency of the schedulability analysis method directly affects the actual performance of the real-time system. Response time analysis (RTA) is a typical method for schedulability analysis. It calculates the response times of tasks and tests the schedulability of tasks by comparing the calculation results and the relative deadlines of tasks. We found that the execution efficiency of existing RTAs can be improved by reasonably sorting the calculation order of the tasks. A task sequencing strategy is proposed to improve the execution efficiency of existing RTAs.

Schedulability Analysis for Timed Automata With Tasks

Research on modeling and analysis of real-time computing systems has been done in two areas, model checking and real-time scheduling theory. In model checking, an expressive modeling formalism such as timed automata (TA) is used to model complex systems, but the analysis is typically very expensive due to state-space explosion. In real-time scheduling theory, the analysis techniques are highly efficient, but the models are often restrictive. In this paper, we aim to exploit the possibility of applying efficient analysis techniques rooted in real-time scheduling theory to analysis of real-time task systems modeled by timed automata with tasks (TAT). More specifically, we develop efficient techniques to analyze the feasibility of TAT-based task models (i.e., whether all tasks can meet their deadlines on single-processor) using demand bound functions (DBF), a widely used workload abstraction in real-time scheduling theory. Our proposed analysis method has a pseudo-polynomial time complexity if the number of clocks used to model each task is bounded by a constant, which is much lower than the exponential complexity of the traditional model-checking based analysis approach (also assuming the number of clocks is bounded by a constant). We apply dynamic programming techniques to implement the DBF-based analysis framework, and propose state space pruning techniques to accelerate the analysis process. Experimental results show that our DBF-based method can analyze a TAT system with 50 tasks within a few minutes, which significantly outperforms the state-of-the-art TAT-based schedulability analysis tool TIMES.

Optimized scheduling algorithm for soft Real-Time System using particle swarm optimization technique

Scheduling of tasks in Real-Time Systems is based on static or dynamic priority like earliest deadline first (EDF) and rate monotonic, respectively. The static scheduler does not give assurance of scheduling all tasks during the underload scenario, whereas dynamic scheduler performs poorly during an overload scenario. This paper has proposed a swarm intelligence-based scheduling algorithm that can overcome both the situations. This paper has used particle swarm optimization (PSO) based swarm technique to design the new scheduling approach. It considers each task as a particle and applied modified PSO technique to identify the most critical task to execute. The efficiency of the newly proposed method has been compared with existing EDF and ACO based scheduling algorithms considering two significant parameters, the success ratio and the effective CPU utilization. All three algorithms have been tested on the simulator with a Soft Real-time periodic task set on 500 timelines. It has been observed that during the underload scenario, the proposed algorithm performs equally to EDF and ACO based algorithms. During overload and highly overload situations, the proposed algorithm performs batter compared to EDF and ACO based algorithms.

Statically optimal dynamic soft real-time semi-partitioned scheduling

Semi-partitioned scheduling is an approach to multiprocessor real-time scheduling where most tasks are fixed to processors, while a small subset of tasks is allowed to migrate. This approach offers reduced overhead compared to global scheduling, and can reduce processor capacity loss compared to partitioned scheduling. Prior work has resulted in a number of semi-partitioned scheduling algorithms, but their correctness typically hinges on a complex intertwining of offline task assignment and online execution. This brittleness has resulted in few proposed semi-partitioned scheduling algorithms that support dynamic task systems, where tasks may join or leave the system at runtime, and few that are optimal in any sense. This paper introduces EDF-sc, the first semi-partitioned scheduling algorithm that is optimal for scheduling (static) soft real-time (SRT) sporadic task systems and allows tasks to dynamically join and leave. The SRT notion of optimality provided by EDF-sc requires deadline tardiness to be bounded for any task system that does not cause over-utilization. In the event that all tasks can be assigned as fixed, EDF-sc behaves exactly as partitioned EDF. Heuristics are provided that give EDF-sc the novel ability to stabilize the workload to approach the partitioned case as tasks join and leave the system.

Online Slack-Stealing Scheduling with Modified laEDF in Real-Time Systems

In hard real-time task systems where periodic and aperiodic tasks coexist, the object of task scheduling is to reduce the response time of the aperiodic tasks while meeting the deadline of periodic tasks. Total bandwidth server (TBS) and advanced TBS (ATBS) are used in dynamic priority systems. However, these methods are not optimal solutions because they use the worst-case execution time (WCET) or the estimation value of the actual execution time of the aperiodic tasks. This paper presents an online slack-stealing algorithm called SSML that can make significant response time reducing by modification of look-ahead earliest deadline first (laEDF) algorithm as the slack computation method. While the conventional slack-stealing method has a disadvantage that the slack amount of each frame must be calculated in advance, SSML calculates the slack when aperiodic tasks arrive. Our simulation results show that SSML outperforms the existing TBS based algorithms when the periodic task utilization is higher than 60%. Compared to ATBS with virtual release advancing (VRA), the proposed algorithm can reduce the response time up to about 75%. The performance advantage becomes much larger as the utilization increases. Moreover, it shows a small performance variation of response time for various task environments.

Flow Algorithms for Scheduling Computations in Integrated Modular Avionics

Algorithms for scheduling tasks in real-time systems with integrated modular architecture that are based on finding the maximum flow in a transportation network are proposed. Results of the experimental evaluation of these algorithms for a single processor and multiprocessor version of the computation scheduling problem are discussed.

A New Hybrid Genetic Algorithm-based Approach for Critical Multiprocessor Real-Time Scheduling with Low Power Optimization

This paper work deals with the presentation of an hybrid genetic approach which allocates and schedules, on a multiprocessor architecture, a system of real-time tasks while balancing the load on the processors. In addition, this hybrid heuristic approach takes into account the safety critical applications which is the focus of our work. We have carried out a performance analysis showing that the proposed hybrid genetic approach has results better than the classical GAs in terms of load balancing, minimum response time and good flexibility.

Energy-Efficient Scheduling for Real-Time Systems Based on Deep Q-Learning Model

Energy saving is a critical and challenging issue for real-time systems in embedded devices because of their limited energy supply. To reduce the energy consumption, a hybrid dynamic voltage and frequency scaling (DVFS) scheduling based on Q-learning (QL-HDS) was proposed by combining energy-efficient DVFS techniques. However, QL-HDS discretizes the system state parameters with a certain step size, resulting in a poor distinction of the system states. More importantly, it is difficult for QL-HDS to learn a system for various task sets with a Q-table and limited training sets. In this paper, an energy-efficient scheduling scheme based on deep Q-learning model is proposed for periodic tasks in real-time systems (DQL-EES). Specially, a deep Q-learning model is designed by combining a stacked auto-encoder and a Q-learning model. In the deep Q-learning model, the stacked auto-encoder is used to replace the Q-function for learning the Q-value of each DVFS technology for any system state. Furthermore, a training strategy is devised to learn the parameters of the deep Q-learning model based on the experience replay scheme. Finally, the performance of the proposed scheme is evaluated by comparison with QL-HDS on different simulation task sets. Results demonstrated that the proposed algorithm can save average <inline-formula><tex-math notation="LaTeX">$4.2\%$</tex-math></inline-formula> energy than QL-HDS.

Building real-time parallel task systems on multi-cores: A hierarchical scheduling approach

Abstract Parallel real-time scheduling on multi-core platforms is widely studied and focused in academic and industrial fields. DAG (directed acyclic graph) is a widely-used parallel model. But existing scheduling algorithms for DAG suffer schedulability analysis of significant inaccuracy and resource waste. In this paper, we propose a hierarchical scheduling framework for DAG tasks with constrained deadlines that it provides a much more efficient resource sharing solution without distinguishing the types of tasks. We have evaluated and implemented our proposed hierarchical scheduling and the results show that our proposed method has promising performance.

Analysis techniques for supporting hard real-time sporadic gang task systems

This paper first studies the problem of scheduling hard real-time sporadic gang task systems under global earliest-deadline-first on a multiprocessor platform, where a gang application’s threads need to be concurrently scheduled on distinct processors. A novel approach combining new lag-based reasoning and executing/non-executing gang interval analysis technique is introduced, which is able to characterize the parallelism-induced idleness, as a key challenge of analyzing gang task schedules. To the best of our knowledge, this approach yields the first utilization-based test for hard real-time gang task systems. To further handle the clustered scheduling scenario, we propose a partitioning scheme that enables a set of gang tasks to be efficiently assigned and scheduled among multiple clusters.

Using dynamic partial reconfiguration of FPGAs in real-Time systems

The use of hardware accelerators to implement computationally intensive tasks in real-time systems can lead to a reduction of the worst-case execution time (WCET). An additional potential benefit is that a WCET-analysis may be simpler to perform because hardware generally has a more time-predictable behavior than software. The dynamic partial reconfiguration (DPR) feature offered by modern FPGAs allows accelerators that are no longer needed to be replaced with new ones, leading to more efficient utilization of hardware resources. This paper presents an experimental evaluation of the potential benefits of using DPR to implement hardware accelerators in real-time systems, focusing on trade-offs between hardware utilization, worst-case performance, and speed-up over a pure software solution. Moreover, it also investigates the trade-off between the use of multiple specialized accelerators combined with DPR instead of the use of a more general accelerator, and the memory footprint of the partial-bit streams. The experiments show that DPR in combination with accelerators results in: (i) better utilization of the FPGA resources, (ii) performance that is comparable with non-reconfigurable solutions, and (iii) tighter WCET bounds.

Supporting Soft Real-Time Sporadic Task Systems on Uniform Heterogeneous Multiprocessors with No Utilization Loss

Uniform heterogeneous multicore architectures are becoming increasingly popular due to their potential of achieving high performance and energy efficiency compared to the homogeneous multicore architectures. In such systems, the real-time scheduling problem becomes more challenging because processors have different speeds. Prior research on uniform heterogeneous multiprocessor real-time scheduling has focused on hard real-time systems, where, significant processing capacity may have to be sacrificed in the worst-case to ensure that all deadlines are met. As meeting hard deadlines is overkill for many soft real-time systems in practice, this paper shows that on soft real-time uniform heterogeneous multiprocessors, bounded response times can be ensured for globally-scheduled sporadic task systems with no utilization loss. A GEDF-based scheduling algorithm, named as GEDF-H, is presented and response time bounds are established under both preemptive and non-preemptive GEDF-H scheduling. Extensive experiments show that the magnitude of the derived response time bound is reasonable, often smaller than four task relative deadlines. To the best of our knowledge, this paper is the first to show that soft real-time sporadic task systems can be supported on uniform heterogeneous multiprocessors without utilization loss under global scheduling, and with reasonable predicted response times.

A new sufficient schedulability analysis for hybrid scheduling

Abstract Earliest deadline first (EDF) and fixed priority (FP) are the most commonly used and studied scheduling algorithms for real-time systems. This paper focuses on combining the EDF and FP strategies in one system. We provide a new sufficient schedulability analysis for real-time hybrid task systems which are scheduled by EDF and FP. The proposed analysis has a polynomial time complexity and no restrictions on task parameters, where the relative deadline of each task could be less than, equal to, or greater than its period. By extensive experiments, we show that our proposed analysis significantly improves the acceptance ratio compared with the existing results of the sufficient schedulability test for hybrid scheduling systems.

Start time configuration for strictly periodic real-time task systems

In many digital control systems, it is required to perform computation in a strictly periodic fashion to provide high control performance. System designers need to assign time slots that are infinitely repeated given a strict period for each task such that the time slots of different tasks do not overlap. While previous work has studied how to decide if a system is schedulable with a certain time slot assignment, it is still an unexplored area of how to select time slots for strictly periodic tasks to make them schedulable. In this paper, we propose an efficient method to solve the above problem. Our method explores the relations among task periods to improve the possibility of finding feasible start time configurations. Finally, we conduct experiments with randomly generated workload to evaluate the performance of the proposed method.

Hybrid Scheduling Algorithm for Periodic Tasks in Real-Time Systems

Hybrid Scheduling Algorithm for Periodic Tasks in Real-Time Systems

Optimal Semi-Partitioned Scheduling in Soft Real-Time Systems

Semi-partitioned real-time scheduling algorithms extend partitioned ones by allowing a (usually small) subset of tasks to migrate. The first such algorithm to be proposed was directed at soft real-time (SRT) sporadic task systems where bounded deadline tardiness is acceptable. That algorithm, called EDF-fm, has the desirable property that migrations are boundary-limited, i.e., they can only occur at job boundaries. However, it is not optimal because per-task utilization restrictions are required. In this paper, a new optimal semi-partitioned scheduling algorithm for SRT sporadic task systems is proposed that eliminates such restrictions. This algorithm, called EDF-os, preserves the boundary-limited property. In overhead-aware schedulability experiments presented herein, EDF-os proved to be better than all other tested alternatives in terms of schedulability in almost all considered scenarios. It also proved capable of ensuring very low tardiness bounds, which were near zero in most considered scenarios.