End-to-end deadline control for aperiodic tasks in distributed real-time systems

A category of Distributed Real-Time Systems (DRTS) that has multiprocessor pipeline architecture is increasingly used. The key challenge of such systems is to guarantee the end-to-end deadlines of aperiodic tasks. This paper proposes an end-to-end deadline control model, called Linear Quadratic Stochastic Optimal Control Model (LQ-SOCM), which features a distributed feedback control that dynamically enforces the desired performance. The control system considers the aperiodic task arrivals and execution times’ variation as the two external factors of the system unpredictability. LQ-SOCM uses discrete time state space equation to describe the real-time computing system. Then, in the actuator design, a continuous manner is adopted to deal with discrete QoS (Quality of Service) adaptation. Finally, experiments demonstrate that the system is globally stable and can statistically provide the end-to-end deadline guarantee for aperiodic tasks. At the same time, LQ-SOCM is capable of effectively improving the system throughput.

Experimental evaluation of linear time-invariant models for feedback performance control in real-time systems

In recent years a new class of soft real-time applications operating in unpredictable environments has emerged. Typical for these applications is that neither the resource requirements nor the arrival rates of service requests are known or available a priori. It has been shown that feedback control is very effective to support the specified performance of dynamic systems that are both resource insufficient and exhibit unpredictable workloads. To efficiently use feedback control scheduling it is necessary to have a model that adequately describes the behavior of the system. In this paper we experimentally evaluate the accuracy of four linear time-invariant models used in the design of feedback controllers. We introduce a model (DYN) that captures additional system dynamics, which a previously published model (STA) fails to include. The accuracy of the models are evaluated by validating the models with regard to measured data from the controlled system and through a set of experiments where we evaluate the performance of a set of feedback control schedulers tuned using these models. From our evaluations we conclude that second order models (e.g., DYN) are more accurate than first order models (e.g. STA). Further we show that controllers tuned using second order models perform better than controllers tuned using first order models.     

DFTS: A dynamic fault-tolerant scheduling for real-time tasks in multicore processors

This paper presents a dynamic scheduling for real-time tasks in multicore processors to tolerate single and multiple transient faults. The scheduling is performed based on three important issues: (1) current released tasks, (2) current available processor cores, and (3) consideration of the number of faults and their occurrences. Using tasks utilization along with a defined criticality threshold in the proposed scheduling method, current ready tasks are divided into critical- and noncritical ones. Based on whether a task is critical or noncritical, an appropriate fault-tolerance policy is exploited. Moreover, scheduling decisions are made to fulfill two key goals: (1) increasing scheduling feasibility and (2) decreasing the total tasks execution time. Several simulation experiments are carried out to compare the proposed method with two well-known methods, called checkpointing with rollback recovery and hardware replication. Experimental results reveal that in the presence of multiple transient faults, the feasibility rate of the proposed method is considerably higher than the other well-known fault-tolerance methods. Moreover, the average timing overhead of this method is lower than the traditional methods.     

Quantifying and suppressing the measurement disturbance in feedback controlled real-time systems

In the control of continuous and physical systems, the controlled system is sampled sufficiently fast to capture the dynamics of the system. In general, this property cannot be applied to the control of computer systems as the measured variables are often computed over a data set, e.g., deadline miss ratio. In this paper we quantify the disturbance present in the measured variable as a function of the data set size and the sampling period, and we propose a feedback control structure that suppresses the measurement disturbance. The experiments we have carried out show that a controller using the proposed control structure outperforms a traditional control structure with regard to performance reliability.

网络化运动控制系统反馈调度器研究

网络资源的有效利用对网络化运动控制系统的性能至关重要。传统的静态带宽分配方法无法适应系统结构变化和负载波动。针对基于数学优化的动态带宽方法难于在线使用的问题,提出了通过设计网络反馈调度器,基于系统实际性能动态分配网络带宽的方法。根据获得的优化带宽分配,并通过调整各轴回路的采样周期,实现整体性能的优化。     

一类航天器控制系统反馈调度方法研究

针对目前航天器控制系统普遍采用静态调度方式,不能够及时调整计算资源分配的问题,提出了一种基于执行时间测量的计算资源反馈调度算法。该方法一方面保证航天器在工作时间段控制精度达到指标要求,另一方面能够动态调整计算资源与控制增益,降低系统功耗。同时在计算机发生故障时保证航天器控制系统稳定运行。最后通过仿真分析验证了方法有效性。     

Supervisory control for real-time scheduling of periodic and sporadic tasks with resource constraints

In the framework of supervisory control of timed discrete event systems, this paper addresses the design problem of a real-time scheduler that meets stringent time constraints of periodic tasks and sporadic tasks which exclusively access shared resources. For this purpose, we present the timed discrete event models of execution of periodic tasks and sporadic tasks and resource access for shared resources. Based on these models, we present the notion of deadlock-free and schedulable languages that contain only deadline-meeting sequences which do not reach deadlock states. In addition, we present the method of systematically computing the largest deadlock-free and schedulable language, and it is also shown that schedulability analysis can be done using this language. We further show that the real-time scheduler achieving the largest deadlock-free and schedulable language is optimal in the sense that there are no other schedulers to achieve schedulable cases more than those achieved by the optimal scheduler.     

A general distributed scalable grid scheduler for independent tasks

We consider non-preemptively scheduling a bag of independent mixed tasks (hard, firm and soft) in computational grids. Based upon task type, we construct a novel generalized distributed scheduler (GDS) for scheduling tasks with different priorities and deadlines. GDS is scalable and does not require knowledge of the global state of the system. It is composed of several phases: a multiple attribute ranking phase, a shuffling phase, and a task-resource matched peer to peer dispatching phase. Results of exhaustive simulation demonstrate that with respect to the number of high-priority tasks meeting deadlines, GDS outperforms existing approaches by 10%–25% without degrading schedulability of other tasks. Indeed, with respect to the total number of schedulable tasks meeting deadlines, GDS is slightly better. Thus, GDS not only maximizes the number of mission-critical tasks meeting deadlines, but it does so without degrading the overall performance. The results have been further confirmed by examining each component phase of GDS. Given that fully known global information is time intensive to obtain, the performance of GDS is significant. GDS is highly scalable both in terms of processors and number of tasks—indeed it provides superior performance over existing algorithms as the number of tasks increase. Also, GDS incorporates a shuffle phase that moves hard tasks ahead improving their temporal fault tolerance. Furthermore, since GDS can handle mixed task types, it paves the way to open the grid to make it amenable for commercialization. The complexity of GDS is O(n²m)$O\left(n^{2}m\right)$ where n$n$ is the number of tasks and m$m$ the number of machines.     

Approximation algorithm for periodic real-time tasks with workload-dependent running-time functions

This paper addresses the problem of resource allocation for distributed real-time periodic tasks, operating in environments that undergo unpredictable changes and that defy the specification of meaningful worst-case execution times. These tasks are supplied by input data originating from various environmental workload sources. Rather than using worst-case execution times (WCETs) to describe the CPU usage of the tasks, we assume here that execution profiles are given to describe the running time of the tasks in terms of the size of the input data of each workload source. The objective of resource allocation is to produce an initial allocation that is robust against fluctuations in the environmental parameters. We try to maximize the input size (workload) that can be handled by the system, and hence to delay possible (costly) reallocations as long as possible. We present an approximation algorithm based on first-fit and binary search that we call FFBS. As we show here, the first-fit algorithm produces solutions that are often close to optimal. In particular, we show analytically that FFBS is guaranteed to produce a solution that is at least 41% of optimal, asymptotically, under certain reasonable restrictions on the running times of tasks in the system. Moreover, we show that if at most 12% of the system utilization is consumed by input independent tasks (e.g., constant time tasks), then FFBS is guaranteed to produce a solution that is at least 33% of optimal, asymptotically. Moreover, we present simulations to compare FFBS approximation algorithm with a set of standard (local search) heuristics such as hill-climbing, simulated annealing, and random search. The results suggest that FFBS, in combination with other local improvement strategies, may be a reasonable approach for resource allocation in dynamic real-time systems. David Juedes is a tenured associate professor and assistant chair for computer science in the School of Electrical Engineering and Computer Science at Ohio University. Dr. Juedes received his Ph.D. in Computer Science from Iowa State University in 1994, and his main research interests are algorithm design and analysis, the theory of computation, algorithms for real-time systems, and bioinformatics. Dr. Juedes has published numerous conference and journal papers and has acted as a referee for IEEE Transactions on Computers, Algorithmica, SIAM Journal on Computing, Theoretical Computer Science, Information and Computation, Information Processing Letters, and other conferences and journals. Dazhang Gu is a software architect and researcher at Pegasus Technologies (NeuCo), Inc. He received his Ph.D. in Electrical Engineering and Computer Science from Ohio University in 2005. His main research interests are real-time systems, distributed systems, and resource optimization. He has published conference and journal papers on these subjects and has refereed for the Journal of Real-Time Systems, IEEE Transactions on Computers, and IEEE Transactions on Parallel and Distributed Systems among others. He also served as a session chair and publications chair for several conferences. Frank Drews is an Assistant Professor of Electical Engineering and Computer Science at Ohio Unversity. Dr. Drews received his Ph.D. in Computer Science from the Clausthal Unversity of Technolgy in Germany in 2002. His main research interests are resource management for operating systems and real-time systems, and bioinformatics. Dr. Drews has numerous publications in conferences and journals and has served as a reviewer for IEEE Transactions on Computers, the Journal of Systems and Software, and other conferences and Journals. He was Publication Chair for the OCCBIO’06 conference, Guest Editor of a Special Issue of the Journal of Systems and Software on “Dynamic Resource Management for Distributed Real-Time Systems”, organizer of special tracks at the IEEE IPDPS WPDRTS workshops in 2005 and 2006. Klaus Ecker received his Ph.D. in Theoretical Physics from the University of Graz, Austria, and his Dr. habil. in Computer Science from the University of Bonn. Since 1978 he is professor in the Department of Computer Science at the Clausthal University of Technology, Germany, and since 2005 he is visiting professor at the Ohio University. His research interests are parallel processing and theory of scheduling, especially in real time systems, and bioinformatics. Prof. Ecker published widely in the above mentioned areas in well reputed journals and proceedings of international conferences as well. He is also the author of two monographs on scheduling theory. Since 1981 he is organizing annually international workshops on parallel processing. He is associate editor of Real Time Systems, and member of the German Gesellschaft fuer Informatik (GI) and of the Association for Computing Machinery (ACM). Lonnie R. Welch received a Ph.D. in Computer and Information Science from the Ohio State University. Currently, he is the Stuckey Professor of Electrical Engineering and Computer Science at Ohio University. Dr. Welch performs research in the areas of real-time systems, distributed computing and bioinformatics. His research has been sponsored by the Defense Advanced Research Projects Agency, the Navy, NASA, the National Science Foundation and the Army. Dr. Welch has twenty years of research experience in the area of high performance computing. In his graduate work at Ohio State University, he developed a high performance 3-D graphics rendering algorithm, and he invented a parallel virtual machine for object-oriented software. For the past 15 years his research has focused on middleware and optimization algorithms for high performance computing. His research has produced three successive generations of adaptive resource management (RM) middleware for high performance real-time systems. The project has resulted in two patents and more than 150 publications. Professor Welch also collaborates on diabetes research with faculty at Edison Biotechnology Institute and on genomics research with faculty in the Department of Environmental and Plant Biology at Ohio University. Dr. Welch is a member of the editorial boards of IEEE Transactions on Computers, The Journal of Scalable Computing: Practice and Experience, and The International Journal of Computers and Applications. He is also the founder of the International Workshop on Parallel and Distributed Real-time Systems and of the Ohio Collaborative Conference on Bioinformatics. Silke Schomann graduated in 2003 with a M.Sc. in Computer Science from Clausthal University Of Technology, where she has been working as a scientific assistant since then. She is currently working on her Ph.D. thesis in computer science at the same university.     

On-line scheduling of scalable real-time tasks on multiprocessor systems

The computation time of scalable tasks depends on the number of processors allocated to them in multiprocessor systems. As more processors are allocated to a scalable task, the overall computation time of the task decreases but the total amount of processors’ time devoted to the execution of the task, called workload, increases due to parallel execution overhead. In this paper, we propose a task scheduling algorithm that utilizes the property of scalable tasks for on-line and real-time scheduling. In the proposed algorithm, the total workload of all scheduled tasks is reduced by managing processors allocated to the tasks as few as possible without missing their deadlines. As a result, the processors in the system have less load to execute the scheduled tasks and can execute more newly arriving tasks before their deadlines. Simulation results show that the proposed algorithm performs significantly better than the conventional algorithm based on a fixed number of processors to execute each task.     

基于自平衡的非周期任务动态实时调度算法

为了更有效地进行开放和不可预测系统的载荷管理，提出了一种基于自平衡的动态实时调度模型——DRSSR（Dynamic Real-time Scheduling based on Self-Regulation）.DRSSR把许可控制和QoS降级相结合，采用反馈控制技术来确保系统的性能、消除干扰和提高系统吞吐率.建立了DRSSR的数学模型，并分析了其稳态性能和瞬态性能.最后，一组基于实时操作系统CRTOS-Ⅱ的实验表明，DRSSR在不确定实时环境中具有良好的性能，并且响应快、实现简单.     

Energy-efficient scheduling of real-time tasks with shared resources

This paper explores the energy-efficient scheduling of real-time tasks on a non-ideal DVS processor in the presence of resource sharing. We assume that tasks are periodic, preemptive and may access to shared resources. When dynamic-priority and fixed-priority scheduling are considered, we use the earliest deadline first (EDF) algorithm and the rate monotonic (RM) algorithm to schedule the given set of tasks. Based on the stack resource policy (SRP), we propose an approach, called blocking-aware two-speed (BATS) algorithm, to synchronize the tasks with shared resources and to calculate appropriate execution speeds so that the shared resources can be accessed in a mutual exclusive manner and the energy consumption can be reduced. Particularly, BATS uses a static low speed to execute tasks initially, and then it switches to a high speed dynamically whenever a task blocks a higher priority task. More specifically, the processor runs at the high speed from the beginning of the blocking until the deadline of the blocked task or the processor becomes idle. In order to guarantee that the deadlines of tasks are met, the static low speed and the dynamic high speeds are derived based on the theoretical analysis of the schedulability of tasks. Compared with existing work, BATS achieves more energy saving because its dynamic high speeds are lower than that of existing work and the processor has less chance to execute tasks at the high speeds. The schedulability analysis and the properties of our proposed BATS are provided in this paper. We also evaluated the capabilities of BATS by a series of experiments, for which we have some encouraging results.     

M-CASH: A real-time resource reclaiming algorithm for multiprocessor platforms

Resource reclaiming schemes are typically applied in reservation-based real-time uniprocessor systems to support efficient reclaiming and sharing of computational resources left unused by early completing tasks, improving the response times of aperiodic and soft tasks in the presence of overruns. In this paper, we introduce a novel and efficient reclaiming algorithm, named M-CASH, for multiprocessor platforms. M-CASH leverages the resource reservation approach offered by the Multiprocessor CBS server offering significant improvements. The correctness of the algorithm is formally proven and its performance is evaluated through extensive synthetic simulations.

A new algorithm for scheduling periodic, real-time tasks

We consider the problem of preemptively scheduling a set of periodic, real-time tasks on a multiprocessor computer system. We give a new scheduling algorithm, the so-called Slack-Time Algorithm, and show that it is more effective than the known Deadline Algorithm. We also give an (exponential-time) algorithm to decide if a task system is schedulable by the Slack-Time or the Deadline Algorithm. The same algorithm can also be used to decide if a task system is schedulable by any given fixed-priority scheduling algorithm. This resolves an open question posed by Leung and Whitehead. Finally, it is shown that the problem of deciding if a task system is schedulable by the Slack-Time, the Deadline, or any given fixed-priority scheduling algorithm is co-NP-hard for each fixedm.     

On real-time robust model predictive control

High-speed applications impose a hard real-time constraint on the solution of a model predictive control (MPC) problem, which generally prevents the computation of the optimal control input. As a result, in most MPC implementations guarantees on feasibility and stability are sacrificed in order to achieve a real-time setting. In this paper we develop a real-time MPC approach for linear systems that provides these guarantees for arbitrary time constraints, allowing one to trade off computation time vs. performance. Stability is guaranteed by means of a constraint, enforcing that the resulting suboptimal MPC cost is a Lyapunov function. The key is then to guarantee feasibility in real-time, which is achieved by the proposed algorithm through a warm-starting technique in combination with robust MPC design. We address both regulation and tracking of piecewise constant references. As a main contribution of this paper, a new warm-start procedure together with a Lyapunov function for real-time tracking is presented. In addition to providing strong theoretical guarantees, the proposed method can be implemented at high sampling rates. Simulation examples demonstrate the effectiveness of the real-time scheme and show that computation times in the millisecond range can be achieved.     

Online optimization for scheduling preemptable tasks on IaaS cloud systems

In Infrastructure-as-a-Service (IaaS) cloud computing, computational resources are provided to remote users in the form of leases. For a cloud user, he/she can request multiple cloud services simultaneously. In this case, parallel processing in the cloud system can improve the performance. When applying parallel processing in cloud computing, it is necessary to implement a mechanism to allocate resource and schedule the execution order of tasks. Furthermore, a resource optimization mechanism with preemptable task execution can increase the utilization of clouds. In this paper, we propose two online dynamic resource allocation algorithms for the IaaS cloud system with preemptable tasks. Our algorithms adjust the resource allocation dynamically based on the updated information of the actual task executions. And the experimental results show that our algorithms can significantly improve the performance in the situation where resource contention is fierce.     

Reconfigurable computing: design methodology and hardware tasks scheduling for real-time image processing

Technology evolution makes possible the integration of heterogeneous components as programmable elements (processors), hardware dedicated blocks, hierarchical memories and buses. Furthermore, an optimized reconfigurable logic core embedded within a System-on-Chip will associate the performances of dedicated architecture and the flexibility of programmable ones. In order to increase performances, some of the applications are carried out in hardware, using dynamically reconfigurable logic, rather than software, using programmable elements. This approach offers a suitable hardware support to design malleable systems able to adapt themselves to a specific application. This article makes a synthesis of the Ardoise project. The first objective of Ardoise project was to design and to produce a dynamically reconfigurable platform based on commercial FPGAs. The concept of dynamically reconfigurable architecture depends partially on new design methodologies elaboration as well as on the programming environment. The platform architecture was designed to be suitable for real-time image processing. The article outlines mainly the Ardoise tools aspect: development environment and real-time management of the hardware tasks. The proposed methodology is based on a dynamic management of tasks according to an application scenario written using C++ language.

Non-preemptible last section assignment for reducing feedback latency in real-time control systems

A real-time control system design procedure consists of the controller design stage and the implementation stage. In the controller design stage, various digital control theories are used with assumptions, such as synchronous sampling, no sampling jitter and negligible feedback latency (latency from sensing to actuation). However, in the implementation stage, multiple control tasks are usually scheduled on a processor, which creates a finite sampling period, varying feedback latency and sampling jitter, and therefore the controller's performance is degraded. In this article, we investigate the relationship between control performance and timing parameters. In the course of this investigation, we found that the feedback latency is the dominant factor affecting control performance. In addition, we propose a rate monotonic (RM) scheduler with non-preemptible last section algorithm, which can reduce the feedback latency considerably. The proposed algorithm provides better control performance than a preemptive RM scheduler, in most cases. The effectiveness of the proposed algorithm is shown in illustrative examples.