Real-time Scheduling System Research Articles

On the convergence of the holistic analysis for EDF distributed systems

Dynamic scheduling techniques, and EDF (Earliest Deadline First) in particular, have demonstrated their ability to increase the schedulability of real time systems compared to fixed-priority scheduling. In distributed systems, the scheduling policies of the processing nodes tend to be the same as in stand-alone systems and, although few EDF networks exist, it is foreseen that dynamic scheduling will gradually develop into real-time networks. There are some response time analysis techniques for EDF scheduled distributed systems, mostly derived from the holistic analysis developed by Spuri. A major factor influencing the response time is the release jitter of each task, which is the maximum variation suffered by the release time of the task jobs. The convergence of the holistic analysis in the context of EDF distributed systems with shared resources had not been studied until now. There is a circular dependency between the task release jitter values, response times and the preemption level ceilings of shared resources. In this paper we present an extension of Spuri's algorithm and we demonstrate that its iterative formulas are non-decreasing, even in the presence of shared resources. This result enables us to assert that the new algorithm converges towards a solution for the response times of the tasks and messages in a distributed system.1A more extensive version of this work is available as a technical report in: http://hdl.handle.net/10902/5309.1

Improving schedulability and energy efficiency for window-constrained real-time systems with reliability requirement

For real-time embedded systems, schedulability, energy efficiency, Quality of Service (QoS), and reliability are four highly co-related important design concerns. In this paper, we explore combining these four dimensions of design issues to achieve better schedulability and energy efficiency for real-time systems while satisfying the QoS and reliability requirements. The QoS requirements are deterministically quantified with the window-constraints, which require that at least m out of each non-overlapped window of k consecutive jobs of a real-time task meet their deadlines. Our contributions mainly consists of two parts: in the first part, we propose two off-line approaches for the management of appropriate mandatory/optional job partitioning with the purpose of improving the schedulability for real-time systems with window-constraints; based on it, in the second part, we proposed advanced techniques to reduce the energy consumption of the real-time systems while satisfying the reliability requirements. The results of extensive experiments demonstrate that our proposed techniques significantly outperform previous approaches in both schedulability and energy efficiency for real-time embedded systems while satisfying the window-constraints and reliability requirements.

Adaptive resource synchronization in hierarchical real-time systems

In this paper we outline the Adaptive Resource Allocation Protocol (ARAP) as an improved resource synchronization algorithm for hierarchically scheduled real-time systems. ARAP exploits knowledge about task utilization, using a proportional-integral-derivative (PID) controller, to estimate required resource bandwidth and improve scheduling decisions. Our analysis and experiments with RTSIM show that ARAP provides better temporal isolation and resource utilization during periods of transient overload compared to state-of-the-art resource synchronization algorithms. Implemented as part of VxWorks , the results are confirmed using an avionic system, for which ARAP substantially reduced the number of hard real-time deadline misses.

Research and Implementation of Real-Time Scheduling Management of WIP in the Workshop Based on RFID

According to the problem of being hard to achieve precise management of production in the actual production environment, RFID tags are used to identity production materials and RFID readers are placed at some important workstations and warehouse channels, to gain products information in real-time, automatically and accurately. On the basis, the paper uses J2EE programming to design the real-time scheduling management system of WIP (work-in-process), and realizes the closed-loop control of production. The system has been successfully applied to Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, and satisfactory results are obtained.

Adaptive scheduling of real-time systems cosupplied by renewable and nonrenewable energy sources

Energy management is an important issue in today's real-time systems due to the high costs of energy supplying. Using renewable, like wave, wind, and solar energy sources seem promising methods to address this issue. However, because of the existing contrast between the critical nature of hard real-time systems and the unpredictable nature of renewable energies, some supplementary energy source like electricity grid or battery is needed. In this paper, we consider hard real-time systems with two renewable and nonrenewable energy sources. In order to reduce the costs, we present two dynamic voltage scaling controllers to minimize the energy attained from the latter source. In order to handle variations of the environmental energy and workload, the model predictive control approach is employed. One nonlinear approach beside one fast linear piecewise affine explicit controller are proposed. The efficacies of the proposed approaches have been investigated through extensive simulations. Comparisons to an ideal clairvoyant controller as a baseline show that, in the studied scenarios, the proposed controllers guarantee at least 78% of the baseline performance.

Compositional Verification for Hierarchical Scheduling of Real-Time Systems

Hierarchical Scheduling (HS) techniques achieve resource partitioning among a set of real-time applications, providing reduction of complexity, confinement of failure modes, and temporal isolation among system applications. This facilitates compositional analysis for architectural verification and plays a crucial role in all industrial areas where high-performance microprocessors allow growing integration of multiple applications on a single platform. We propose a compositional approach to formal specification and schedulability analysis of real-time applications running under a Time Division Multiplexing (TDM) global scheduler and preemptive Fixed Priority (FP) local schedulers, according to the ARINC-653 standard. As a characterizing trait, each application is made of periodic, sporadic, and jittering tasks with offsets, jitters, and nondeterministic execution times, encompassing intra-application synchronizations through semaphores and mailboxes and interapplication communications among periodic tasks through message passing. The approach leverages the assumption of a TDM partitioning to enable compositional design and analysis based on the model of preemptive Time Petri Nets (pTPNs), which is expressly extended with a concept of Required Interface (RI) that specifies the embedding environment of an application through sequencing and timing constraints. This enables exact verification of intra-application constraints and approximate but safe verification of interapplication constraints. Experimentation illustrates results and validates their applicability on two challenging workloads in the field of safety-critical avionic systems.

A Strategy for Managing Complexity of the Global Market and Prototype Real-Time Scheduler for LEGO Supply Chain

The paper describes main features of a strategy for managing complexity of the global market and real-time scheduling multi-agent system designed for the LEGO Company. The design is based on Multi-Agent Technology Group (MATech) own strategy blueprint and multi-agent platform, which provide real-time adaptive event-driven scheduling to replenish products to LEGO Branded Retail stores. The prototype system has been used to schedule 20 US-based LEGO retail outlets for a yearlong trial period and has achieved the following results: • Reduction of lost sale from 40% to 16%; • Increase in service level from 66% to 86%; • Increase in profitability 56% to 81%. The results show a considerable potential value for full scale LEGO supply chain multi-agent solution which would be able to dynamically and adaptively re-schedule deliveries in real time.

Elastic scheduling in real-time systems

Elastic scheduling is designed for real-time systems with variable load.It adjusts the task attributes dynamically to meet the flexibility requirements of system,and it is an effective task scheduling strategy.Concerning the research results and problems of elastic scheduling,an overview of elastic scheduling was given out,and the research progress of the elastic periodic task model,scheduling model and the elastic scheduling algorithm were analyzed.The existing problems in research were investigated,and possible research areas in future were suggested.

Compositional real-time scheduling framework for periodic reward-based task model

As the size and complexity of embedded software systems increase, compositional real-time scheduling framework is widely accepted as means to build large and complex systems. A compositional real-time scheduling framework proposes to decompose a system into independent subsystems and provides ways to assemble them into a flexible hierarchical real-time scheduling system while guaranteeing the internal real-time requirements of each subsystem. In this paper, we consider the imprecise reward-based periodic task model in compositional scheduling framework. Thus, we introduce the imprecise periodic resource model to characterize the imprecise resource allocations provided by the system to a single component, and the interface model to abstract the imprecise real-time requirements of the component. The schedulability of mandatory parts is also analyzed to meet the minimum requirement of tasks. Finally, we provide a scheduling algorithm to guarantee a certain amount of reward, which makes it feasible to efficiently compose multiple imprecise components.

QCSP Based Schedulability Test of Real-Time System

The problem of schedulability test of real-time task in scheduling system has been addressed. In such as real-time scheduling problems with uncertain interrupt operation, Quantified Constraint Satisfaction Problems (QCSP) were introduced to model and solve CSP involving uncertainty or uncontrollability on the value taken by some variables. In this paper, we propose to a novel approach to schedulability test based on the QCSP model. Based on the QCSP model of scheduler and interrupter in the real-time scheduling system, we transform the schedulability test into the satisfability problem. Finally, we design the algorithm QCSP-SchTest for judging the satisfability of a schedule S(α). This leads to a new schedulability test method without considering the specific scheduling algorithm or strategy.

Limited Preemptive Scheduling for Real-Time Systems. A Survey

The question whether preemptive algorithms are better than nonpreemptive ones for scheduling a set of real-time tasks has been debated for a long time in the research community. In fact, especially under fixed priority systems, each approach has advantages and disadvantages, and no one dominates the other when both predictability and efficiency have to be taken into account in the system design. Recently, limited preemption models have been proposed as a viable alternative between the two extreme cases of fully preemptive and nonpreemptive scheduling. This paper presents a survey of the existing approaches for reducing preemptions and compares them under different metrics, providing both qualitative and quantitative performance evaluations.

Real-Time Industrial Automation Process Control using Network-Enabled RFID

The emerging ubiquitous computing technology that belongs to the group of automatic identification technologies and has become popular is the Radio Frequency Identification System involving electromagnetic theory for identifying human beings, animals, and most importantly useful in various fields like drug discovery, tracking and distribution of various materials, and data warehouse management. In recent times, it is found that the ubiquitous technology has been extended to numerous industrial applications involving real-time decisions. This system termed as “networks of RFID systems” helps in controlling various parameters and systems in realtime environment. Scheduling of real-time systems is important since it helps in satisfying the timing constraints where tasks in real-time systems are decided on the basis of deadline, occurrence period and slack time. Few algorithms based on these criteria are Rate Monotonic (RM), Earliest Deadline First (EDF), and Least-Laxity-First (LLF) algorithms. EDF and LLF are considered to be dynamic in nature while RM is static in nature. To judge the efficiency of the algorithms two conditions like “overloaded” and “under loaded” are taken into consideration. In this article, an effort is made to combine the features of both static as well as dynamic algorithms. in order to control the real-time industrial processes. Hence the major issue of concern in this paper is to resolve two issues, onedetermine the algorithm in terms of timing performance of different sub-processes so as to properly schedule them and other, how the timing constraints determine the behavior of the system need to be specified and thereby, to ensure that the algorithms meet the constraints under both overloaded and under loaded conditions.

An Intelligent Trucking Operations Management System

The high complexity of operations within truck transport markets hinders the prosperity of very small and small truckers and fleet operators. In that respect, strategies and technologies supporting operations management have become paramount for implementing investments in the said context. Goal of this paper is to propose a novel and fully integrated suite for trucking operations planning and real-time fleet management. For this purpose, the suite is built on the foundation of a multi-agent based intelligent and high-performance planning and real-time scheduling system, a novel management frontend and an innovative type of middleware equipped with unified adapters for local and internet based modules of the suite.

Development of machine learning‐based real time scheduling systems: using ensemble based on wrapper feature selection approach

There are two items that significantly enhance the generalisation ability (i.e. classification accuracy) of machine learning‐based classifiers: feature selection (including parameter optimisation) and an ensemble of the classifiers. Accordingly, the objective in this study is to develop an ensemble of classifiers based on a genetic algorithm (GA) wrapper feature selection approach for real time scheduling (RTS). The proposed approach can better enhance the generalisation ability of the RTS knowledge base (i.e. classifier) in comparison with three classical machine learning‐based classifier RTS systems, including the GA‐based wrapper feature selection mechanism, in terms of the prediction accuracy of 10‐fold cross validation as measured according to all the performance criteria. The proposed ensemble classifier RTS also provides better system performance than the three machine learning‐based RTS systems, including the GA‐based wrapper feature selection mechanism and heuristic dispatching rules, under all the performance criteria, over a long period in a flexible manufacturing system (FMS) case study.

Heuristic Model for Dynamic Single Machine Group Scheduling in Laser Cutting Job Shop to Minimize the Makespan

In today’s highly competitive market, laser cutting which has a characteristic of “make to order” and high product variety is under pressure to reduce costs, to increase productivity and to respond to the rapidly changing demands from customers. To maintain the competitive advantage, companies need to have a real-time dynamic scheduling system, which can handle large combinations of jobs, allowing sequencing of jobs to achieve multi-objective goals. Motivated by a real-life scheduling problem in a sheet metal processing company in Malaysia, this research addressed single machine scheduling problem with sequence-dependent setup times and group technology assumption to minimize makespan and with the secondary objective of minimizing setup times. The focus of this paper is on developing a simple heuristic algorithm based dynamic scheduling system. This algorithm has been coded in vb.net and is integrated with a database system. The scheduling system developed is verified and validated by comparing to the actual production run. Results show that the algorithm model can find good solutions within short computational time.

Globally scheduled real-time multiprocessor systems with GPUs

Graphics processing units, GPUs, are powerful processors that can offer significant performance advantages over traditional CPUs. The last decade has seen rapid advancement in GPU computational power and generality. Recent technologies make it possible to use GPUs as co-processors to CPUs. The performance advantages of GPUs can be great, often outperforming traditional CPUs by orders of magnitude. While the motivations for developing systems with GPUs are clear, little research in the real-time systems field has been done to integrate GPUs into real-time multiprocessor systems. We present two real-time analysis methods, addressing real-world platform constraints, for such an integration into a soft real-time multiprocessor system and show that a GPU can be exploited to achieve greater levels of total system performance.

Applications of Intelligent Evolutionary Algorithms in Optimal Automation System Design

This paper proposes an intelligent evolutionary algorithm that can be applied in the design of optimal automation systems, and employs a multimodal six-bar mechanism optimization design, job shop production scheduling for the fishing equipment industry, and dynamic real-time production scheduling system design cases to show how the technique developed in this paper is highly effective at resolving optimal automation system design problems. Major breakthroughs in artificial intelligence continue to be made in the wake of advanced information technology developments, and the field of intelligent evolutionary algorithms has attracted a particularly large amount of attention from researchers and users in the artificial intelligence community. The successful optimization of automation system design requires interdisciplinary integration, and further requires the use of actual cases, verification, and improvement to ensure implementation in real-world applications.

Study of SOM-based intelligent multi-controller for real-time scheduling

Earlier studies have indicated that the use of multiple scheduling rules (MSRs) for various zones in a system can significantly enhance the production performance over the performance obtained with the use of a single scheduling rule (SSR) over a given scheduling interval for all machines in the system through a multi-pass simulation approach for a real time scheduling (RTS) problem. However, if a classical machine learning approach is used, an RTS knowledge base (KB) can be developed using the appropriate MSR strategy (this method is called an intelligent multi-controller in this study) as obtained from training examples. A classical machine learning approach main disadvantage is that the classes (scheduling decision variables) to which training examples are assigned must be pre-defined. Hence, developing an RTS KB by the classical machine learning approach to generate training examples becomes an intolerably time consuming task because the MSRs for the next scheduling period must be pre-determined. To address this issue, this study proposes an intelligent multi-controller that consists of three main mechanisms: (1) a simulation-based training example generation mechanism, (2) a data preprocessing mechanism, and (3) a self-organizing map (SOM)-based MSR selection mechanism. The results reveal that over a long period of time this approach provides better system performance based on various performance criteria than the system performance of the machine learning-based RTS based on the SSR approach for two different types of manufacturing systems (FMS and FAB). Hence, the proposed intelligent multi-controller approach is efficient enough to be incorporated into the operation of an RTS system.

Cluster scheduling for real-time systems: utilization bounds and run-time overhead

Cluster scheduling, where processors are grouped into clusters and the tasks that are allocated to one cluster are scheduled by a global scheduler, has attracted attention in multiprocessor real-time systems research recently. In this paper, assuming that an optimal global scheduler is adopted within each cluster, we investigate the worst-case utilization bounds for cluster scheduling with different task allocation/partitioning heuristics. First, we develop a lower limit on the utilization bounds for cluster scheduling with any reasonable task allocation scheme. Then, the lower limit is shown to be the exact utilization bound for cluster scheduling with the worst-fit task allocation scheme. For other task allocation heuristics (such as first-fit, best-fit, first-fit decreasing, best-fit decreasing and worst-fit decreasing), higher utilization bounds are derived for systems with both homogeneous clusters (where each cluster has the same number of processors) and heterogeneous clusters (where clusters have different number of processors). In addition, focusing on an efficient optimal global scheduler, namely the boundary-fair (Bfair) algorithm, we propose a period-aware task allocation heuristic with the goal of reducing the scheduling overhead (e.g., the number of scheduling points, context switches and task migrations). Simulation results indicate that the percentage of task sets that can be scheduled is significantly improved under cluster scheduling even for small-size clusters, compared to that of the partitioned scheduling. Moreover, when comparing to the simple generic task allocation scheme (e.g., first-fit), the proposed period-aware task allocation heuristic markedly reduces the scheduling overhead of cluster scheduling with the Bfair scheduler.

Using Fixed Priority Pre-emptive Scheduling in Real-Time Systems

For real-time applications, task scheduling is a problem of paramount importance. Several scheduling algorithms were proposed in the literature, starting from static scheduling or cyclic executives which provide very deterministic yet inflexible behaviour, to the so called best-effort scheduling, which facilitates maximum run-time flexibility but allows only probabilistic predictions of run-time performance presenting a non-predictable and nondeterministic solution. Between these two extremes lies fixed priority scheduling algorithms, such as Rate Monotonic, that is not so efficient for real-time purposes but exhibits a predictable approach because scheduling is doing offline and guarantees regarding process deadlines could be obtained using appropriate analysis methods. This paper investigates the use of Rate Monotonic algorithm by making adjustments in order to make it more suitable for real-time applications. The factors that motivate the interest for fixed priority scheduling algorithms such Rate Monotonic when doing with real-time systems lies in its associated analysis that could be oriented in two directions: schedulability analysis and analysis of process interactions. The analyzing process is carried out using a previously implemented framework that allows modelling, simulation and schedulability analysis for a set of real-time system tasks, and some of the results obtained are presented.