On real-time scheduling policies for flexible manufacturing systems

The authors consider a model proposed by J.R. Perkins and P.R. Kumar (ibid., vol.34, no.2, pp. 139-148, Feb. 1989) for real-time control of flexible manufacturing systems. In this model, a machine can process a finite number of part types at specified rates, but only one part type can be processed at any given time. To process multiple part types, the machine uses a feedback rule to switch, from time to time, from one part type to another. Such switches incur a setup time of δ time units during which no parts are processed. By introducing the notion of idling, the authors derive a lower bound on the performance, as measured by average buffer size, of any stable feedback scheduling policy for a single machine     

Stability of queueing networks and scheduling policies

We develop a programmatic procedure for establishing the stability of queueing networks and scheduling policies. The method uses linear or nonlinear programming to determine what is an appropriate quadratic functional to use as a Lyapunov function. If the underlying system is Markovian, our method establishes not only positive recurrence and the existence of a steady-state probability distribution, but also the geometric convergence of an exponential moment. We illustrate this method on several example problems     

Novel scheduling policies in real-time multithread control system design

In this paper soft real-time scheduling approaches (Resource Reservations) are applied to multithread digital control design and implementation. The advantage of this choice is the possibility of raising the sampling frequencies beyond the hard real-time boundaries, while retaining control on the transient overloads. An important component of the proposed framework is a simulation tool, which allows for a fine tuning of the scheduling parameters. Experimental and simulation results on two case studies show a significant performance improvement with respect to hard real-time scheduling, especially when the feedback controller operates on dataflows requiring statistically widespread computation times.     

Stability analysis of buffer priority scheduling policies using Petri nets

A Petri net approach to determining the conditions for stability of a re-entrant system with buffer priority scheduling policy is described in this paper.The concept of buffer boundedness based on the dynamic behavior of the markings in the system model is emphasized.The method is used to demonstrate the stability of the first buffer first served(FBFS)and the last buffer first served(LBFS)scheduling policies.Finally a sufficient condition for instability of systems with a positive feedback Joop(PFL) is established,and an example is given.     

Capacitated lot sizing and sequence dependent setup scheduling: an iterative approach for integration

We consider here the lot sizing and scheduling problem in single-level manufacturing systems. The shop floor is composed of unrelated parallel machines with sequence dependent setup times. We propose an integer programming model embedding precise capacity information due to scheduling constraints in a classical lot-sizing model. We also propose an iterative approach to generate a production plan taking into account scheduling constraints due to changeover setup times. The procedure executes two decision modules per iteration: a lot-sizing module and a scheduling module. The capacitated lot-sizing problem is solved to optimality considering estimated data and aggregate information, and the scheduling problem is solved by a GRASP heuristic. In the proposed scheme the information flow connecting the two levels is managed in each iteration. We report a set of computational experiments and discuss future work.     

Adaptive location policies for global scheduling

Two important components of a global scheduling algorithm are its transfer policy and its location policy. While the transfer policy determines whether a task should be transferred, the location policy determines where it should be transferred. Based on their location policies, global scheduling algorithms can be broadly classified as receiver-initiated, sender-initiated, or symmetrically-initiated. The relative performance of these classes of algorithms has been shown to depend on the system workload. We present two adaptive location policies for global scheduling in distributed systems. These location policies are general, and can be used in conjunction with many existing transfer policies. By adapting to the system workload, the proposed policies capture the advantages of both sender-initiated and receiver-initiated policies. In addition, by adaptively directing their search activities toward the nodes that are most likely to be suitable counterparts in task transfers, the proposed policies provide short transfer latency and low overhead, and more important, high probability of finding a suitable counterpart if one exists. These properties allow these policies to deliver good performance over a very wide range of system operating conditions. The proposed policies are compared with nonadaptive policies, and are shown to considerably improve performance and to avoid causing system instability     

An iterative procedure for lot streaming in job-shop scheduling

The issue in Lot Streaming is to split lots into sublots in order to improve the makespan (or some other criterion). We present a model and an iterative procedure in a general job-shop environment. In a first step, optimal sublot-sizes are computed given a sequence of sublots on the machines. In a second step, a better sequence is computed by solving a standard job-shop scheduling problem with fixed sublot-sizes. Some computational results on a sample (including the famous 10-10) are reported. In case of no set-up, in few iterations, the makespan approaches a lower bound with few sublots.     

A genetic algorithm for solving economic lot size scheduling problem

The purpose of this research is to determine an optimal batch size for a product and purchasing policy of associated raw materials. Like most other practical situation, this manufacturing firm has a limited storage space and transportation fleet of known capacity. The mathematical formulation of the problem indicates that the model is a constrained nonlinear integer program. Considering the complexity of solving such model, we investigate the use of genetic algorithms (GAs) for solving this model. We develop GA code with three different penalty functions usually used for constraint optimizations. The model is also solved using an existing commercial optimization package to compare the solution. The detailed computational results are presented.     

Message scheduling for real-time interprocessor communication

In this paper an efficient algorithm is proposed which optimizes periodic message scheduling in a real-time multiprocessor system. The system is based on a many-core single-chip computer architecture and uses a multistage baseline network for inter-core communication. Due to its basic architecture, internal blockings can occur during data transfers, i.e. the baseline network is not real-time capable by itself. Therefore, we propose a scheduling algorithm that may be performed before the execution of an application in order to compute a non-blocking schedule of periodic message transfers. Additionally, we optimize the clock rate of the network subject to the constraint that all data transfers can be performed in a non-blocking way. Our solution algorithm is based on a generalized graph coloring model and a randomized greedy approach. The algorithm was tested on some realistic communication scenarios as they appear in modern electronic car units. Computational results show the effectiveness of the proposed algorithm.     

Efficient scheduling algorithms for real-time multiprocessorsystems

Efficient scheduling algorithms based on heuristic functions are developed for scheduling a set of tasks on a multiprocessor system. The tasks are characterized by worst-case computation times, deadlines, and resources requirements. Starting with an empty partial schedule, each step of the search extends the current partial schedule by including one of the tasks yet to be scheduled. The heuristic functions used in the algorithm actively direct the search for a feasible schedule, i.e. they help choose the task that extends the current partial schedule. Two scheduling algorithms are evaluated by simulation. To extend the current partial schedule, one of the algorithms considers, at each step of the search, all the tasks that are yet to be scheduled as candidates. The second focuses its attention on a small subset of tasks with the shortest deadlines. The second algorithm is shown to be very effective when the maximum allowable scheduling overhead is fixed. This algorithm is hence appropriate for dynamic scheduling in real-time systems     

Modeling video sources for real-time scheduling

What is the impact of the autocorrelation of variable-bit-rate (VBR) sources on real-time scheduling algorithms? Our results show that the impact of long term, or interframe, autocorrelation is negligible, while the impact of short term, or intraframe, autocorrelation can be significant. Such results are essentially independent of the video coding scheme employed. To derive these results, video sequences are modeled as a collection of stationary subsequences called scenes. Within a scene, a statistical model is derived for both the sequence of frames and of slices. The model captures the distribution and the autocorrelation function of real-time video data. In previous work, the pseudoperiodicity of the slice level auto-correlation function made it difficult to develop a simple yet accurate model. We present a generalization of previous methods that can easily capture this pseudoperiodicity and is suited for modeling a greater variety of autocorrelation functions. By simply tuning a few parameters, the model reproduces the statistic behavior of sources with different types and levels of correlation on both the frame and the slice level.     

Stable scheduling policies for flexible manufacturing systems

In this brief note we provide a new analysis of the transient behavior of the clear-a-fraction policy of Perkins and Kumar (1989). In addition, we show that a new “clear-average-oldest-buffer” policy and a “random part selection” policy (of which “first-come-first-served” is a special case) are stable. Finally, we introduce a stable and efficient “stream modifier” that can be used to obtain network level stability results     

Applying pinwheel scheduling and compiler profiling for power-aware real-time scheduling

Energy consumption is a critical design issue in embedded systems, especially in battery-operated systems. Maintaining high performance while extending the battery life is an interesting challenge for system designers. Dynamic voltage scaling and dynamic frequency scaling allow us to adjust supply voltage and processor frequency to adapt to the workload demand for better energy management. Because of the high complexity involved, most solutions depend on heuristics for online power-aware real-time scheduling or offline time-consuming scheduling. In this paper, we discuss how we can apply pinwheel model to power-aware real-time scheduling so that task information, including start times, finish times, preemption times, etc, can be efficiently derived using pinwheel model. System predictability is thus increased and under better control on power-awareness. However, job execution time may be only a small portion of its worst case execution time and can only be determined at runtime. We implement a profiling tool to insert codes for collecting runtime information of real-time tasks. Worst case execution time is updated online for scheduler to perform better rescheduling according to actual execution. Simulations have shown that at most 50% energy can be saved by the proposed scheduling algorithm. Moreover, at most additional 33% energy can be saved when the profiling technique is applied. This paper is an extended version of the paper Power-Aware Real-Time Scheduling using Pinwheel Model and Profiling Technique that appeared in the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications.     

CLSP问题的周期调度方法及其PSO求解

采用分解思想考虑多阶段CLSP问题,从多阶段生产系统抽象出单阶段生产环节,提出以周期方式对该生产环节进行生产批量调度。在对CLSP周期调度问题进行描述和界定的基础上,建立了相应的数学模型,讨论了周期调度方法中的周期上界以及周期长度与物料批量大小之间的关系等性质,采用基于三层编码的粒子群优化算法进行问题求解。源于冷轧生产实际的计算实例表明周期方法能够大大降低问题的规模且所得设备调整费用比人工方法减少约16%。     

基于信道状态的WiMAX系统实时调度算法

介绍了M-LWDF、EXP和CD-EDD三种经典的实时调度算法,并在此基础上提出一种基于信道状态的WiMAX系统的实时调度算法CBRTS(channel-based real-time scheduling)。该算法核心思想是在数据链路层中考察物理层信道的传输条件,从而进一步将有限的无线资源更加合理地分配给用户。仿真结果表明,提出的算法具有更高的吞吐量、更小的时延和丢包率,能满足实时业务的QoS要求。     

Modelling heterogeneous processor scheduling for real-time systems

A new model is presented to describe data-flow algorithms implemented in a multiprocessing system. Called the resource/data flow graph (RDFG), the model explicitly represents cyclo-static processor schedules as circuits of processor arcs that reflect the order that processors execute graph nodes. The model also allows the guarantee of meeting hard real-time deadlines. When unfolded, the model identifies statistically the processor schedule. The model therefore is useful for determining the throughput and latency of systems with heterogeneous processors. The applicability of the model is demonstrated using a space surveillance algorithm.     

Fault-tolerant scheduling for real-time embedded control systems

With the increasing complexity of industrial application, an embedded control system (ECS) requires processing a number of hard real-time tasks and needs fault-tolerance to assure high reliability. Considering the characteristics of real-time tasks in ECS, an integrated algorithm is proposed to schedule real-time tasks and to guarantee that all real-time tasks are completed before their deadlines even in the presence of faults. Based on the nonpreemptive critical-section protocol (NCSP), this paper analyzes the blocking time introduced by resource conflicts of relevancy tasks in fault-tolerant multiprocessor systems. An extended schedulability condition is presented to check the assignment feasibility of a given task to a processor. A primary/backup approach and on-line replacement of failed processors are used to tolerate processor failures. The analysis reveals that the integrated algorithm bounds the blocking time, requires limited overhead on the number of processors, and still assures good processor utilization. This is also demonstrated by simulation results. Both analysis and simulation show the effectiveness of the proposed algorithm in ECS.     

Demand-based schedulability analysis for real-time multi-core scheduling

In real-time systems, schedulability analysis has been widely studied to provide offline guarantees on temporal correctness, producing many analysis methods. The demand-based schedulability analysis method has a great potential for high schedulability performance and broad applicability. However, such a potential is not yet fully realized for real-time multi-core scheduling mainly due to (i) the difficulty of calculating the resource demand under dynamic priority scheduling algorithms that are favorable to multi-cores, and (ii) the lack of understanding how to combine the analysis framework with deadline-miss conditions specialized for those scheduling algorithms. Addressing those two issues, to the best of our knowledge, this paper presents the first demand-based schedulability analysis for dynamic job-priority scheduling algorithms: EDZL (Earliest Deadline first until Zero-Laxity) and LLF (Least Laxity First), which are known to be effective for real-time multi-core scheduling. To this end, we first derive demand bound functions that compute the maximum possible amount of resource demand of jobs of each task while the priority of each job can change dynamically under EDZL and LLF. Then, we develop demand-based schedulability analyses for EDZL and LLF, by incorporating those new demand bound functions into the existing demand-based analysis framework. Finally, we combine the framework with additional deadline-miss conditions specialized for those two laxity-based dynamic job-priority scheduling algorithms, yielding tighter schedulability analyses. Via simulations, we demonstrate that the proposed schedulability analyses outperform the existing schedulability analyses for EDZL and LLF.     

Fault-tolerant adaptive scheduling for embedded real-time systems

Describes a fault-tolerant algorithm which uses a time-value scheduling approach to detect faults, sustain high processor utilization, and ensure timely execution of critical tasks     

Energy-efficient real-time scheduling for two-type heterogeneous multiprocessors

We propose three novel mathematical optimization formulations that solve the same two-type heterogeneous multiprocessor scheduling problem for a real-time taskset with hard constraints. Our formulations are based on a global scheduling scheme and a fluid model. The first formulation is a mixed-integer nonlinear program, since the scheduling problem is intuitively considered as an assignment problem. However, by changing the scheduling problem to first determine a task workload partition and then to find the execution order of all tasks, the computation time can be significantly reduced. Specifically, the workload partitioning problem can be formulated as a continuous nonlinear program for a system with continuous operating frequency, and as a continuous linear program for a practical system with a discrete speed level set. The latter problem can therefore be solved by an interior point method to any accuracy in polynomial time. The task ordering problem can be solved by an algorithm with a complexity that is linear in the total number of tasks. The work is evaluated against existing global energy/feasibility optimal workload allocation formulations. The results illustrate that our algorithms are both feasibility optimal and energy optimal for both implicit and constrained deadline tasksets. Specifically, our algorithm can achieve up to 40% energy saving for some simulated tasksets with constrained deadlines. The benefit of our formulation compared with existing work is that our algorithms can solve a more general class of scheduling problems due to incorporating a scheduling dynamic model in the formulations and allowing for a time-varying speed profile.