Periodic Real-time Tasks Research Articles

System-wide energy optimization for multiple DVS components and real-time tasks

Most dynamic voltage and frequency scaling (DVS) techniques adjust only CPU parameters; however, recent embedded systems provide multiple adjustable clocks which can be independently tuned. When considering multiple components, energy optimal frequencies depend on task set characteristics such as the number of CPU and memory access cycles. In this work, we propose a realistic energy model considering multiple components with individually adjustable frequencies such as CPUs, system bus and memory, and related task set characteristics. The model is validated on a real platform and shows less than 2% relative error compared to measured values. Based on the proposed energy model, we present an optimal static frequency assignment scheme for multiple DVS components to schedule a set of periodic real-time tasks. We simulate the energy gain of the proposed scheme compared to other DVS schemes for various task and system configurations, showing up to a 20% energy reduction. We also experimentally verify energy savings of the proposed scheme on a real hardware platform.

Minimum-Energy Semi-Static Scheduling of a Periodic Real-Time Task on DVFS-Enabled Multi-Core Processors

The proposed scheduling scheme minimizes the energy consumption of a real-time task on the multi-core processor with the dynamic voltage and frequency scaling capability. The scheme allocates a pertinent number of cores to the task execution, inactivates unused cores, and assigns the lowest frequency meeting the deadline. For a periodic real-time task with consecutive real-time instances, the scheme prepares the minimum-energy solutions for all input cases at off-line time, and applies one of the prepared solutions to each real-time instance at runtime.

PAUC: Power-Aware Utilization Control in Distributed Real-Time Systems

CPU utilization control has recently been demonstrated to be an effective way of meeting end-to-end deadlines for distributed real-time systems running in unpredictable environments. However, current research on utilization control focuses exclusively on task rate adaptation, which cannot effectively handle rate saturation and discrete task rates. Since the CPU utilization contributed by a real-time periodic task is determined by both its rate and execution time, CPU frequency scaling can be used to adapt task execution times for power-efficient utilization control. In this paper, we present PAUC, a two-layer coordinated CPU utilization control architecture. The primary control loop uses frequency scaling to locally control the CPU utilization of each processor, while the secondary control loop adopts rate adaptation to control the utilizations of all the processors at the cluster level on a finer timescale. Both the two control loops are designed and coordinated based on well-established control theory for theoretically guaranteed control accuracy and system stability. Empirical results on a physical testbed demonstrate that our control solution outperforms a state-of-the-art utilization control algorithm by having more accurate control and less power consumption. Extensive simulation results also show that our solution can significantly improve the feasibility of utilization control.

Feasibility Analysis for Temperature-Constraint Hard Real-Time Periodic Tasks

While the dynamic thermal management problem is closely related to the dynamic power management problem, it has its own distinct features. In this paper, we study the feasibility checking problem for real-time periodic task sets under the peak temperature constraint. We show that the traditional scheduling approach, i.e. to repeat the schedule that is feasible through the range of one hyperperiod, does not apply any more. We then present new necessary and sufficient conditions to check the feasibility of real-time schedules. We further incorporate the close relationship of leakage, temperature, and supply voltage into our feasibility analysis, and develop more elaborated feasibility conditions. Our experiments, based on technical parameters derived from a processor using the 65 nm IC technology, demonstrate the effectiveness of our feasibility conditions and, at the same time, highlight the fact that a power/thermal-aware computing technique becomes ineffective at the submicron scale if the inter dependency of leakage, temperature, and supply voltage is not properly addressed.

Key Technology of Embedded System Implementation for Software-based CNC System

The software-based computer numerical control(CNC) system includes three types of tasks, periodic real-time tasks, aperiodic real-time tasks, and non-real-time tasks. The tasks are characterized by concurrency, hybridization, and correlation, which make system implementation difficult. The conventional scheduling algorithm can not meet the demands of system implementation in the software-based CNC system completely. The uncertainty factors when running real-time tasks affect control performance by degrading manufacturing accuracy as a result of system resource and processor use restrictions. To address the technical difficulty of embedded system implementation, a novel fuzzy feedback scheduling algorithm based on output jitter of key real-time tasks for a software-based CNC system is proposed here. Time characteristics, such as sampling jitter, input-output jitter, and non-schedulability are discussed, followed by quantification through simulations of the impact of time characteristics on manufacturing accuracy. On the basis of this research, the scheduler architecture is designed, and then the algorithm table is calculated. When the system resource changes, the key periodic real-time tasks meet their deadlines by means of dynamically adjusting the task period. The simulated results show that the machining precision rises by an order of magnitude for the proposed scheduler in resource-constrained software-based CNC systems. Moreover, unlike conventional feedback scheduling methods, the algorithm in this paper does not rely on the availability of task execution times and is easy to implement while incurring only a small overhead.

Dynamic Voltage Scaling for Real-Time Systems with System Workload Analysis

Dynamic Voltage Scaling (DVS) is a well-known low-power design technique, which adjusts the clock speed and supply voltage dynamically to reduce the energy consumption of real-time systems. Previous studies considered the probabilistic distribution of tasks' workloads to assist DVS in task scheduling. These studies use probability information for intra-task frequency scheduling but do not sufficiently explore the opportunities for the system workload to save more energy. This paper presents a novel DVS algorithm for periodic real-time tasks based on the analysis of the system workload to reduce its power consumption. This algorithm takes full advantage of the probabilistic distribution characteristics of the system workload under priority-driven scheduling such as Earliest-Deadline-First (EDF). Experimental results show that the proposed algorithm reduces processor idle time and spends more busy time in lower-power speeds. The measurement indicates that compared to the relative DVS algorithms, this algorithm saves energy by at least 30% while delivering statistical performance guarantees.

Imprecise Computation Model, Synchronous Periodic Real-time Task Sets and Total Weighted Error

This paper proposes two scheduling approaches, one-level and two-level scheduling, for synchronous periodic real-time task sets based on the Imprecise Computation Model. The imperative of real-time systems is a reaction on an event within a limited amount of time. Sometimes the available time and resources are not enough for the computations to complete within the deadlines, but still enough to produce approximate results. The Imprecise Computation Model is motivated by this idea, which gives the flexibility to trade off precision for timeliness. In this model a task is logically decomposed into a mandatory and optional subtask. Only the mandatory subtask is required to complete by its deadline, while the optional subtask may be left unfinished. Usually, different scheduling policies are used for the scheduling of mandatory and optional subtasks. For both proposed approaches the earliest deadline first and rate monotonic scheduling algorithms are used for the scheduling of mandatory subtasks, whereas the optional subtasks are scheduled in a way that the total weighted error is minimized. The basic idea of one-level scheduling is to extend the mandatory execution times, while in two-level scheduling the mandatory and optional subtasks are separately scheduled. The single preemptive processor model is assumed.

Exploiting Redundancies to Enhance Schedulability in Fault-Tolerant and Real-Time Distributed Systems

In the past decades, distributed systems have been widely applied to real-time applications, most of which have fault-tolerance requirements to assure high reliability. Due to the stringent space constraints of real-time systems, the issue of schedulability becomes a major concern in the design of fault-tolerant and real-time distributed systems. Most existing real-time and fault-tolerant scheduling algorithms, which are based on the primary-backup scheme for periodic real-time tasks, introduce unnecessary redundancies by aggressively using active-backup copies. To solve this problem, we propose two novel fault-tolerant techniques, which are seamlessly integrated with fixed-priority-based scheduling algorithms. These techniques leverage redundancies to enhance schedulability in fault-tolerant and real-time distributed systems. Our fault-tolerant techniques make use of the primary-backup scheme to tolerate permanent hardware failures. The first technique (referred to as Tercos) terminates the execution of active-backup copies, when corresponding primary copies are successfully completed. Tercos is designed to reduce scheduling lengths in fault-free scenarios to enhance schedulability by virtue of executing portions of active-backup copies in passive forms. The second technique (referred to as Debus) uses a deferred-active-backup scheme to further minimize schedule lengths to improve the schedulability performance. Debus schedules active-backup copies as late as possible, while terminating active-backup copies when their primary copies are completed. Experimental results show that, compared with existing algorithms in literature, Tercos can significantly improve schedulability by up to 17.0% (with an average of 9.7%). Furthermore, empirical results reveal that Debus can enhance schedulability over Tercos by up to 12% (with an average of 7.8%).

A Novel DVS Algorithm for Real Time Embedded Operating Systems

Energy efficiency is an important property of mobile and pervasive computing devices. Dynamic voltage scaling (DVS) is an energy saving technique, which is achieved by reducing energy dissipation of the core by lowering the supply voltage and operating frequency. In this paper a novel DVS algorithm is presented, which achieves better energy savings with deadline guarantees using real-time scheduling and task splitting technique. The proposed DVS algorithm is compared with existing RT-DVS algorithms for real-time periodic task sets, analyzing their energy efficiency, and discussing the performance differences quantitatively. The simulation results show that the proposed algorithm closely approaches the theoretical lower bound on energy consumption and reduces the energy consumption by 10–15% over the existing DVS algorithm.

The One-Shot Task Model for Robust Real-Time Embedded Control Systems

Embedded control systems are often implemented in small microprocessors enabled with real-time technology. In this context, control laws are often designed according to discrete-time control systems theory and implemented as hard real-time periodic tasks. Standard discrete-time control theory mandates to periodically sample (input) and actuate (output). Depending on how input/output (I/O) operations are performed within the hard real-time periodic task, different control task models can be distinguished. However, existing task models present important drawbacks. They generate task executions prone to violate the periodic control demands, a problem known as sampling and latency jitter, or they impose synchronized I/O operations at each task job execution that produce a constant but artificially long I/O latency. In this paper, the one-shot task model for implementing control systems in embedded multitasking hard real-time platforms is presented. The novel control task model is built upon control theoretical results that indicate that standard control laws can be implemented considering only periodic actuation. Taking advantage of this property, the one-shot task model is shown to remove endemic problems for real-time control systems such as sampling and latency jitters. In addition, it can minimize the harmful effects that long I/O latencies have on control performance. Extensive simulations and real experiments show the feasibility and effectiveness of the novel task model, compared to previous real-time and/or control-based solutions.

Two-level hierarchical scheduling scheme of hybrid tasks for software CNC system

For the scheduling of hybrid tasks for software CNC,a two-level hierarchical scheduling scheme is presented.The time-triggered mode and event-triggered mode are adopted for the real-time periodic tasks and real-time aperiodic tasks respectively. Firstly,the task model is established and the related concepts are defined.Then,the structure of scheduling scheme is proposed,and scheduling algorithm,schedulability and tick-interval are deeply researched.Subsequently,the scheduling scheme is adopted for the software CNC based on Windows CE.NET,and software CNC structure and task division are analyzed.Finally,the experimental verification of two-level hierarchical scheduling scheme is carried out and the results demonstrate that the proposed method can ensure the determination of real-time tasks,and improve the performance of software CNC system.

Application of predictive control scheduling method to real-time periodic control tasks overrun

Based on the abort strategy of fixed periods, a novel predictive control scheduling methodology was proposed to efficiently solve overrun problems. By applying the latest control value in the prediction sequences to the control objective, the new strategy was expected to optimize the control system for better performance and yet guarantee the schedulability of all tasks under overrun. The schedulability of the real-time systems with p-period overruns was analyzed, and the corresponding stability criteria was given as well. The simulation results show that the new approach can improve the performance of control system compared to that of conventional abort strategy, it can reduce the overshoot and adjust time as well as ensure the schedulability and stability.

Server-based execution of periodic tasks on dynamically reconfigurable hardware

A prototype system that executes a set of periodic real-time tasks utilising dynamic hardware reconfiguration is presented. The proposed scheduling technique, merge server distribute load (MSDL), is not only able to give an offline guarantee for the feasibility of the task set, but also minimises the number of device configurations. After describing this technique, the schedulability analysis is extended to cover different runtime system overheads, including the device reconfiguration time. Then, a light-weight runtime system that performs the online part of the MSDL scheduling technique is detailed. The runtime system is implemented entirely in hardware. Finally, the corresponding synthesis tool flow is outlined and the overhead posed by the runtime system is reported.

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.

An EDF schedulability test for periodic tasks on reconfigurable hardware devices

In this paper, we consider the scheduling of periodic real-time tasks on reconfigurable hardware devices. Such devices can execute several tasks in parallel. All executing tasks share the hardware resource, which makes the scheduling problem differ from single- and multiprocessor scheduling. We adapt the global EDF multiprocessor scheduling approach to the reconfigurable hardware execution model and define two preemptive scheduling algorithms, EDF-First-k-Fit and EDF-Next-Fit . For these algorithms, we present a novel linear-time schedulability test and give a proof based on a resource augmentation technique. Then, we propose a task placement and relocation scheme utilizing partial device reconfiguration. This scheme allows us to extend the schedulability test to include reconfiguration time overheads. Experiments with synthetic workloads compare the scheduling test with the actual scheduling performance of EDF-First-k-Fit and EDF-Next-Fit . The main evaluation result is that the reconfiguration overhead is acceptable if the task computation times are one order of magnitude higher than the device reconfiguration time.

Procrastination for leakage-aware rate-monotonic scheduling on a dynamic voltage scaling processor

As the dynamic voltage scaling (DVS) technique provides system engineers the flexibility to trade-off the performance and the energy consumption, DVS has been adopted in many computing systems. However, the longer a job executes, the more energy in the leakage current the device/processor consumes for the job. To reduce the energy consumption resulting from the leakage current, a system might enter the dormant mode. This paper targets energy-efficient rate-monotonic scheduling for periodic real-time tasks on a uniprocessor DVS system with non-negligible leakage power consumption. An on-line simulated scheduling strategy and a virtually blocking time strategy are developed for procrastination scheduling to reduce energy consumption. The proposed algorithms derive a feasible schedule for real-time tasks with worst-case guarantees for any input instance. Experimental results show that our proposed algorithms could derive energy-efficient solutions.

Runtime feasibility check for non-preemptive real-time periodic tasks

Runtime feasibility check for non-preemptive real-time periodic tasks

Feasibility Analysis of Real-Time Periodic Tasks with Offsets

The problem of feasibility analysis of asynchronous periodic task sets, where tasks can have an initial offset, is known to be co-NP-complete in the strong sense. A sufficient pseudo-polynomial test has been proposed by Baruah, Howell and Rosier, which consists in analyzing the feasibility of the corresponding synchronous task set (i.e. all offsets are set equal to 0). If the test gives a positive result, then the original asynchronous task set is feasible; else, no definitive answer can be given. In many cases, this sufficient test is too pessimistic, i.e. it gives no response for many feasible task sets. In this paper, we present a new sufficient pseudo-polynomial test for asynchronous periodic task sets. Our test reduces the pessimism by explicitely considering the offsets in deriving a small set of critical arrival patterns. We show, trough a set of extensive simulations, that our test outperforms the previous sufficient test.

Comparison of Deadline-Based Scheduling Algorithms for Periodic Real-Time Tasks on Multiprocessor

Multiprocessor architecture becomes common on real-time systems as the workload of real-time systems increases. Recently new deadline-based (EDF-based) multiprocessor scheduling algorithms are devised, and comparative studies on the performance of these algorithms are necessary. In this paper, we compare EDZL, a hybrid of EDF and LLF, with other deadline-based scheduling algorithms such as EDF, EDF-US[m(2m-1)], and fpEDF. We show EDZL schedules all task sets schedulable by EDF. The experimental results show that the number of preemptions of EDZL is comparable to that of EDF and the schedulable utilization bound of EDZL is higher than those of other algorithms we consider.

리눅스 미들웨어(TMOSM/Linux)에서 주기성을 가진 실시간 태스크의 스케쥴링 향상에 관한 연구

실시간 응용 제품을 개발하기 위해 운영체제는 실시간 태스크의 시간 보장성(timeliness guarantee)이 지원되어야한다. 그러나 현재 대부분의 운영체제는 실시간 태스크의 시간적 제약조건(timing constraints)을 효율적으로 지원할 수 있는 방법을 제공해 주지 못하고 있다. 실시간 응용의 시간적 제약조건을 지원하기 위해서는 운영체제 커널 변경 방법과 미들웨어 방법이 있다. 본 논문에서는 운영체제 변경없이 잘 알려진 Real-time Object Model인 TMO에 근거한 미들웨어 접근 방식을 적용한다. 현재 TMO(Time-triggered Message-triggered Object) 모델을 기반으로 한 미들웨어로 다양한 운영체제 시스템 상에서 개발되어온 TMOSM(TMO Support Middleware)이 있다. 리눅스 기반의 TMOSM의 스케줄링 알고리즘은 효율적으로 실시간 스케줄링을 지원하지만 주기적인 실시간 태스크를 위해 몇 가지 고려해야할 사항들이 있다. 본 논문에서 는 주기적인 실시간 태스크를 효율적으로 처리할 수 있는 개선된 실시간 미들웨어 스케줄링 알고리즘을 제안하고 성능을 비교한다. 제안한 알고리즘은 실시간 미들웨어의 구조를 간단하게 함으로써 시스템 성능 향상과 주기적인 실시간 태스크의 적시성을 더욱더 보장함을 확인하였다. For real-time applications, the underlying operating system (0S) should support timeliness guarantees of real-time tasks. However, most of current operating systems do not provide timely management facilities in an efficient way. There could be two approaches to support timely management facilities for real-time applications: (1) by modifying 0S kernel and (2) by Providing a middleware without modifying 0S. In our approach, we adopted the middleware approach based on the TMO (Time-triggerred Message-triggered Object) model which is a well-known real-tine object model. The middleware, named TMSOM (TMO Support Middleware) has been implemented on various OSes such as Linux and Windows XP/NT/98. In this paper, we mainly consider TMOSM implemented on Linux(TMOS/Linux). Although the real-time schedul-ing aIgorithm used in current TMOSM/Linux can produce an efficient real-time schedule, it can be improved for periodic real-time tasks by considering several factors. In this paper, we discuss those factors and propose an improved real-time scheduling algorithm for periodic real-time tasks, In order to simulate the performance of our algorithm, we measure timeliness guarantee rate for periodic real-time tasks. The result shows that the performance of our algorithm is superior to that of existing algorithm. Additionally, the proposed algorithm can improve system performance by making the structure of real-time middleware simpler.