Real-time Scheduling System Research Articles

A simulation-based scheduling system for real-time optimization and decision making support

This paper presents an industrial application of simulation-based optimization (SBO) in the scheduling and real-time rescheduling of a complex machining line in an automotive manufacturer in Sweden. Apart from generating schedules that are robust and adaptive, the scheduler must be able to carry out rescheduling in real time in order to cope with the system uncertainty effectively. A real-time scheduling system is therefore needed to support not only the work of the production planner but also the operators on the shop floor by re-generating feasible schedules when required. This paper describes such a real-time scheduling system, which is in essence a SBO system integrated with the shop floor database system. The scheduling system, called OPTIMISE scheduling system (OSS), uses real-time data from the production line and sends back expert suggestions directly to the operators through Personal Digital Assistants (PDAs). The user interface helps in generating new schedules and enables the users to easily monitor the production progress through visualization of production status and allows them to forecast and display target performance measures. Initial results from this industrial application have shown that such a novel scheduling system can help both in improving the line throughput efficiently and simultaneously supporting real-time decision making.

Guest Editorial Special Section on Real-Time and (Networked) Embedded Systems—II

The eight papers in this special section is divided into three groups. The first group contains papers dealing with the fusion of control theory and scheduling of real-time systems, and the second group deals with the timing analysis of real-time networks. The third group of papers investigates design, modeling, and timing analysis of embedded real-time systems.

Predictability of Fixed-Job Priority schedulers on heterogeneous multiprocessor real-time systems

The multiprocessor Fixed-Job Priority (FJP) scheduling of real-time systems is studied. An important property for the schedulability analysis, the predictability (regardless to the execution times), is studied for heterogeneous multiprocessor platforms. Our main contribution is to show that any FJP schedulers are predictable on unrelated platforms. A convenient consequence is the fact that any FJP schedulers are predictable on uniform multiprocessors.

FIT: A Flexible, Lightweight, and Real-Time Scheduling System for Wireless Sensor Platforms

We propose FIT, a flexible, lightweight, and real-time scheduling system for wireless sensor platforms. There are three salient features of FIT. First, its two-tier hierarchical framework supports customizable application-specific scheduling policies, hence, FIT is very flexible. Second, FIT is lightweight in terms of minimizing the thread number to reduce preemptions and memory consumption while at the same time ensuring system schedulability. We propose a novel Minimum Thread Scheduling Policy (MTSP) exploration algorithm within FIT to achieve this goal. Finally, FIT provides a detailed real-time schedulability analysis method to help check if application's temporal requirements can be met. We implemented FIT on MicaZ motes and carried out extensive evaluations. Results demonstrate that FIT is indeed flexible and lightweight for implementing real-time applications, at the same time, the schedulability analysis provided can predict the real-time behavior. FIT is a promising scheduling system for implementing complex real-time applications in sensor networks.

Handling mixed sets of tasks in combined offline and online scheduled real-time systems

Many industrial applications with real-time demands are composed of mixed sets of tasks with a variety of requirements. These can be in the form of standard timing constraints, such as period and deadline, or complex, e.g., to express application specific or nontemporal constraints, reliability, performance, etc. As many algorithms focus on specific sets of task types and constraints only, system design has to focus on those supported by a particular algorithm, at the expense of the rest. In this paper, we present a method to deal with a combination of mixed sets of tasks and constraints: periodic tasks with complex and simple constraints, soft and firm aperiodic, and sporadic tasks. We propose the use of an offline scheduler to manage complex timing and resource constraints of periodic tasks and transform these into a simple EDF model with start-times and deadlines. At run-time, the execution of the offline scheduled tasks is flexibly shifted in order to allow for feasible inclusion of dynamically arriving sporadic and aperiodic tasks. Sporadic tasks are guaranteed offline based on their worst-case activation frequencies. At run-time, this pessimism is reduced by the online algorithm which uses the exact knowledge about sporadic arrivals to reclaim resources and improve response times and acceptance of firm aperiodic tasks.

GA-based learning bias selection mechanism for real-time scheduling systems

The use of machine learning technologies in order to develop knowledge bases (KBs) for real-time scheduling (RTS) problems has produced encouraging results in recent researches. However, few researches focus on the manner of selecting proper learning biases in the early developing stage of the RTS system to enhance the generalization ability of the resulting KBs. The selected learning bias usually assumes a set of proper system features that are known in advance. Moreover, the machine learning algorithm for developing scheduling KBs is predetermined. The purpose of this study is to develop a genetic algorithm (GA)-based learning bias selection mechanism to determine an appropriate learning bias that includes the machine learning algorithm, feature subset, and learning parameters. Three machine learning algorithms are considered: the back propagation neural network (BPNN), C4.5 decision tree (DT) learning, and support vector machines (SVMs). The proposed GA-based learning bias selection mechanism can search the best machine learning algorithm and simultaneously determine the optimal subset of features and the learning parameters used to build the RTS system KBs. In terms of the accuracy of prediction of unseen data under various performance criteria, it also offers better generalization ability as compared to the case where the learning bias selection mechanism is not used. Furthermore, the proposed approach to build RTS system KBs can improve the system performance as compared to other classifier KBs under various performance criteria over a long period.

Quality of Service Scheduling in Real-Time Systems

In this paper, we deal with dynamic scheduling components integrating new Quality of Service (QoS) functionalities into a Linux-based real-time operating system. In our approach, periodic tasks allow occasional deadline violations within given bounds specified according to the Skip-Over task model. Hence, every task has a minimal QoS guarantee which is expressed by the ratio of periodic task instances which must complete before their deadline. The work stated here provides two on-line scheduling algorithms, namely RLP and RLP/T, which enhance the existing Skip-Over algorithms. More specifically, the proposed algorithms aim at improving the actual QoS observed for periodic tasks (which is always greater or equal to the QoS guarantee). These novel scheduling techniques rely on the EDL (Earliest Deadline as Late as possible) scheduling strategy. Simulation results show the performance of RLP and RLP/T with respect to basic Skip-Over algorithms. Finally, we present the integration of these QoS scheduling services into CLEOPATRE open-source component library, a patch to Linux/RTAI.

Power saving and fault-tolerance in real-time critical embedded systems

In this paper, a method with the double purpose of reducing the consumption of energy and giving a deterministic guarantee on the fault tolerance of real-time embedded systems operating under the Rate Monotonic discipline is presented. A lower bound exists on the slack left free by tasks being executed at their worst-case execution time. This deterministic slack can be redistributed and used for any of the two purposes. The designer can set the trade-off point between them. In addition, more slack can be reclaimed when tasks are executed in less than their worst-case time. Fault-tolerance is achieved by using the slack to recompute the faulty task. Energy consumption is reduced by lowering the operating frequency of the processor as much as possible while meeting all time-constraints. This leads to a multifrequency method; simulations are carried out to test it versus two single frequency methods (nominal and reduced frequencies). This is done under different trade-off points and rates of faults' occurrence. The existence of an upper bound on the overhead caused by the transition time between frequencies in Rate Monotonic scheduled real-time systems is formally proved. The method can also be applied to multicore or multiprocessor systems.

Development of two-level decision tree-based real-time scheduling system under product mix variety environment

Most of the research on machine learning-based real-time scheduling (RTS) systems has been aimed toward product constant mix environments. However, in a product mix variety manufacturing environment, the scheduling knowledge base (KB) is dynamic; therefore, it would be interesting to develop a procedure that would automatically modify the scheduling knowledge when important changes occur in the manufacturing system. All of the machine learning-based RTS systems (including a KB refinement mechanism) proposed in earlier studies periodically require the addition of new training samples and regeneration of new KBs. Hence, previous approaches investigating machine learning-based RTS systems have been confronted with the training data overflow problem and an increase in the scheduling KB building time, which are unsuitable for RTS control. The objective of this paper is to develop a KB class selection mechanism that can be supported in various product mix ratio environments. Hence, the RTS KB is developed by a two-level decision tree (DT) learning approach. First, a suitable scheduling KB class is selected. Then, for each KB class, the best (proper) dispatching rule is selected for the next scheduling period. Here, the proposed two-level DT RTS system comprises five key components: (1) training samples generation mechanism, (2) GA/DT-based feature selection mechanism, (3) building a KB class label by a two-level self-organizing map, (4) DT-based KB class selection module, and (5) DT-based dynamic dispatching rule selection module. The proposed two-level DT-based KB RTS system yields better system performance than that by a one-level DT-based RTS system and heuristic individual dispatching rules in a flexible manufacturing system under various performance criteria over a long period.

An agent-based nurse rostering system under minimal staffing conditions

Nurse rostering is a complex problem. We propose a new heuristic using a competitive agent-based negotiation that focuses on nurse preferences called competitive nurse rostering (CNR). Unlike the existing literature, CNR models each nurse's preference functions separately and separates the cost minimization and preference maximization problems. CNR produces quality nurse rosters even though it cannot leverage extra staffing. As an agent system, CNR can distribute computational requirements over several computer systems, include other solution methods at various points in of the rostering problem, and act as a real-time scheduling system. These benefits are not naturally inherent in centralized heuristic solutions.

A Study on Real-Time Scheduling of Holonic Manufacturing Systems (5th Report, Estimation Method of Future Status Without Decision Criteria of Other Holons)

This paper deals with a real-time scheduling system of the holonic manufacturing systems (HMS). In the previous papers, real-time scheduling processes have been proposed based on the utility values of the candidate holons. A simulation based procedure has also been implemented to estimate the future status of HMS and to determine the utility values based on the estimated future status. However, it was assumed that the simulation models for estimation include the model representing the decision criteria of all the holons. A new simulation based real-time scheduling method is proposed, in the present paper, to improve the estimation process, which enable all the holons to estimate the future status of the HMS without decision criteria of other holons. An estimation process is developed for the individual job holons and resource holons to estimate the future status of HMS through the simulation. Some case studies of the real-time scheduling are carried out to verify the effectiveness of the proposed method. It is shown, through case studies, that the proposed methods are effective to improve the objective function values of the individual holons.

A FUZZY ALGORITHM FOR SCHEDULING SOFT PERIODIC TASKS IN PREEMPTIVE REAL-TIME SYSTEMS

Most researches concerning real-time system scheduling assume scheduling constraint to be precise. However, in the real world, scheduling is a decision making process which involves vague constraints and uncertain data. Fuzzy constraints are particularly well suited for dealing with imprecise data. This paper proposes a fuzzy scheduling approach to real-time system scheduling in which the scheduling parameters are treated as fuzzy variables. A simulation is also performed and the results are compared with both EDF and LLF scheduling algorithms. The latter two algorithms are the most commonly used algorithms for scheduling real-time processes. It is concluded that the proposed fuzzy approach is very promising and it has the potential to be considered for future research.

A REAL-TIME SCHEDULING AND RESCHEDULING SYSTEM BASED ON RFID FOR SEMICONDUCTOR FOUNDRY FABS

This paper constructs a real-time scheduling and rescheduling system based on RFID (Radio Frequency Identification) information for fully automated fabs. The system consists of three modules: a real-time decision-making scheduler, a rescheduling mechanism for machine breakdowns and bottleneck, and a simulation platform. The proposed scheduling and rescheduling system have been successfully applied to the 300mm foundry fab of SEMATECH (SEmiconductor MAnufacturing TECHnology) and TRC fab (Hewlett-Packard Technology Research Center Silicon fab). From simulation results, the dispatching rule sets proposed by SVM scheduler have better performance than a single dispatching rule. In SEMATECH model, the mean flow time is reduced 3%(203.32→197.307 hours), tardy numbers are decreased 17% (63→52 lots), and throughput is increased (176852→177951 wafers). In TRC fab model, the mean flow time is reduced 4% (451.23→427.86 hours) and tardy numbers are decreased 25% (15→12 lots). Clearly, the results are much better than previous researches. In addition, the proposed framework can deal with the machine breakdowns and bottleneck so that the system performance can be improved in time.

The infrastructure of the timed EOPNs-based multiple-objective real-time scheduling system for 300 mm wafer fab

Modern semiconductor wafer fabrication systems are changing from 200 mm to 300 mm wafer processing, and with the dual promises of more chips per wafer and economy of scale, leading semiconductor manufacturers are attracted to developing and implementing 300 mm wafer fabs. However, in today's dynamic and competitive global market, a successful semiconductor manufacturer has to excel in multiple performance indices, such as manufacturing cycle time and on-time delivery, and simultaneously optimize these objectives to reach the best-compromised system achievement. To cope with this challenge, in this paper, the infrastructure of a timed EOPNs-based multiple-objective real-time scheduling system (MRSS) is proposed to tackle complex 300 mm wafer fabs. Four specific performance objectives pursued by contemporary semiconductor manufacturers are integrated into a priority-ranking algorithm, which can serve as the initial scheduling guidance, and then all wafer lots will be dynamically dispatched by the real-time state-dependent dispatching system. This dispatching control system is timed EOPN-based and adopts a heterarchical organization that leads to a better real-time performance and adaptability. As the foundation of real-time schedule, the timed EOPNs modelling approach is expounded in detail, and the prototype of the MRSS simulation system is also provided.

Rate monotonic schedulability tests using period-dependent conditions

Feasibility and schedulability problems have received considerable attention from the real-time systems research community in recent decades. Since the publication of the Liu and Layland bound, many researchers have tried to improve the schedulability bound of the RM scheduling. The LL bound does not make any assumption on the relationship between any of the task periods. In this paper we consider the relative period ratios in a system. By reducing the difference between the smallest and the second largest virtual period values in a system, we can show that the RM schedulability bound can be improved significantly. This research has also proposed a system design methodology to improve the schedulability of real time system with a fixed system load.

Dynamic Scheduling of Skippable Periodic Tasks: Issues and Proposals

This paper deals with dynamic scheduling in realtime systems that have Quality of Service requirements. We assume that tasks are periodic and may miss their deadlines, occasionally, as defined by the so-called Skip-Over model. In this paper, we present a dynamic scheduling algorithm, called RLP (Red as Late as possible, a variant of Earliest Deadline to make slack stealing and to get better performance in terms of ratio of periodic task instances which complete before their deadline). Simulation results show that RLP outperforms the two conventional skip-over algorithms, namely RTO and BWP, introduced about ten years ago. Then, we investigate a second criteria called fairness, aiming to balance individual success ratios of tasks. We present variants of RLP to improve fairness and report comparative simulation results. Finally, we present the integration of these QoS scheduling services into CLEOPATRE1, a free open-source library which offers selectable real-time facilities on shelves.

A REAL-TIME SCHEDULING AND RESCHEDULING SYSTEM BASED ON RFID FOR SEMICONDUCTOR FOUNDRY FABS

ABSTRACT This paper constructs a real-time scheduling and rescheduling system based on RFID (Radio Frequency Identification) information for fully automated fabs. The system consists of three modules: a real-time decision-making scheduler, a rescheduling mechanism for machine breakdowns and bottleneck, and a simulation platform. The proposed scheduling and rescheduling system have been successfully applied to the 300mm foundry fab of SEMATECH (SEmiconductor MAnufacturing TECHnology) and TRC fab (Hewlett-Packard Technology Research Center Silicon fab). From simulation results, the dispatching rule sets proposed by SVM scheduler have better performance than a single dispatching rule. In SEMATECH model, the mean flow time is reduced 3%(203.32→197.307 hours), tardy numbers are decreased 17% (63→52 lots), and throughput is increased (176852→177951 wafers). In TRC fab model, the mean flow time is reduced 4% (451.23→427.86 hours) and tardy numbers are decreased 25% (15→12 lots). Clearly, the results are much better than previous researches. In addition, the proposed framework can deal with the machine breakdowns and bottleneck so that the system performance can be improved in time.

Real-time scheduling for holonic manufacturing systems based on estimation of future status

This paper deals with a real-time scheduling system of holonic manufacturing systems (HMSs), which were proposed by an international cooperative research consortium called The HMS Consortium for Autonomous Distributed Management and Control of the Manufacturing Systems. The scheduling system generates real-time suitable production schedules, based on decision making by individual constituents named holons and their coordination. The objective of the present research is to improve the decision-making processes of the individual holons through the use of estimation process of the future status of the HMS. Procedures are developed and implemented to the individual holons, in order to estimate the future status of the HMS by applying the simulation model of the HMS. A neural network model is proposed to represent and to simulate the decision-making process of the individual holons.

2108 ホロニック生産システムにおけるシミュレーションに基づくリアルタイムスケジューリング(OS2 生産スケジューリング)

This paper deals with a real-time scheduling system of the Holonic Manufacturing Systems (HMSs). In the previous papers, real-time scheduling processes based on the utility values have been proposed and applied to the HMSs. A simulation based procedure has also been implemented to estimate the future status of HMSs and to determine the utility values aiming at improving scheduling method. However, it was assumed that only one holon in the HMSs carries out the estimation, in the previous research, for the ease of the estimation process. A simulation based real-time scheduling method is newly proposed, in the paper, to improve the objective function values by estimating the future status of HMSs by all the holons. Some case studies of the real-time scheduling are carried out to verify the effectiveness of the proposed method.

A framework for developing intelligent real-time scheduling systems

The authors describe the adaptation of a hierarchical management-control framework to the design of intelligent real-time scheduling systems. Originally developed for the analysis of organizations where dynamics are governed solely by the reactions of human decision makers, the control framework provides insights concerning design requirements for decision systems that react in human-like fashion within narrow domains of expertise. The framework emphasizes the dynamic balance of autonomy and decentralization required to achieve planned goals within a hierarchical organization. Applying this framework, design specifications for knowledge-based scheduling systems are developed, which are responsive to organizational dynamics as required for schedule control and replanning. An illustration is given that addresses kitchen scheduling in full-service restaurants. © 2006 Wiley Periodicals, Inc. Hum Factors Man 16: 385–408, 2006.