实时协同的调度算法研究

研究了目前流行的实时调度技术,归纳总结了不同调度技术下的典型调度算法,介绍了实时调度算法的调度规则、调度特点、适用场合以及需要解决的问题,分析了典型商业实时操作系统中的调度技术,提出了增强操作系统实时性能需要解决的技术问题,为将优秀的实时调度算法应用在实时操作系统中奠定了理论基础。     

Power-aware scheduling algorithms for sporadic tasks in real-time systems

In this paper, we consider the canonical sporadic task model with the system-wide energy management problem. Our solution uses a generalized power model, in which the static power and the dynamic power are considered. We present a static solution to schedule the sporadic task set, assuming worst-case execution time for each sporadic tasks release, and propose a dynamic solution to reclaim the slacks left by the earlier completion of tasks than their worst-case estimations. The experimental results show that the proposed static algorithm can reduce the energy consumption by 20.63%–89.70% over the EDF* algorithm and the dynamic algorithm consumes 2.06%–24.89% less energy than that of the existing DVS algorithm.     

两种经典实时调度算法的研究与实现

速率单调（RM）调度和最早截止期限优先（EDF）调度在实时调度领域占有重要低位。基于一个x86体系结构的小系统上设计实现RM和EDF调度算法,并在不同的工作负载下,以任务截止期错失率作为衡量不同任务调度算法性能优劣的指标,对两种算法进行了性能分析和比较。在通常情况下,RM和EDF都可以保证任务成功调度,EDF算法可承受较多的工作负载。但是随着负载的增加,EDF算法性能急剧下降,到一定过载程度,EDF算法性能低于RM算法。     

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     

Performance evaluation of a new scheduling algorithm for distributed systems with security heterogeneity

High quality of security service is increasingly critical for applications running on heterogeneous distributed systems. However, existing scheduling algorithms for heterogeneous distributed systems disregard security requirements of applications. To address this issue, in this paper, we introduce security heterogeneity concept for our scheduling model in the context of distributed systems. Based on the concept, we propose a novel heuristic scheduling algorithm, or SATS, which strives to maximize the probability that all tasks are executed without any risk of being attacked. Extensive experimental studies using real-world traces indicate that the scheduling performance is affected by heterogeneities of security and computational power. Additionally, empirical results demonstrate that with respect to security and performance, the proposed scheduling algorithm outperforms existing approaches under a wide spectrum of workload conditions.     

改进的异构多处理器的实时任务调度算法研究*

现有的很多调度算法存在时间复杂度过高或调度成功率低的问题。提出一种新的调度算法(HRTSA),提高实时任务的调度成功率。HRTSA首先通过METC策略初始化分簇,降低算法的时间复杂度;再在放置任务时根据处理器的负载均衡进行处理器负载的有效控制;最后通过任务复制调度以提高任务调度成功率。对比实验分析表明提出的HRTSA算法时间复杂度与RTSDA相比较低,调度成功率较高。     

Energy-aware disk scheduling for soft real-time I/O requests

In this work, we develop energy-aware disk scheduling algorithm for soft real-time I/O. Energy consumption is one of the major factors which bar the adoption of hard disk in mobile environment. Heat dissipation of large scale storage system also calls for an energy-aware scheduling technique to further increase the storage density. The basic idea in this work is to properly determine the I/O burst size so that device can be in standby mode between consecutive I/O bursts and that it can satisfy the soft real-time requirement. We develop an elaborate model which incorporates the energy consumption characteristics, overhead of mode transition in determining the appropriate I/O burst size and the respective disk operating schedule. Efficacy of energy-aware disk scheduling algorithm greatly relies on not only disk scheduling algorithm itself but also various operating system and device firmware related concerns. It is crucial that the various operating system level and device level features need to be properly addressed within disk scheduling framework. Our energy-aware disk scheduling algorithm successfully addresses a number of outstanding issues. First, we examine the effect of OS and hard disk firmware level prefetch policy and incorporate its effect in our disk scheduling framework. Second, our energy aware scheduling framework can allocate a certain fraction of disk bandwidth to handle sporadically arriving non real-time I/O’s. Third, we examine the relationship between lock granularity of the buffer management and energy consumption. We develop a prototype software with energy-aware scheduling algorithm. In our experiment, proposed algorithm can reduce the energy consumption to one fourth if we use energy-aware disk scheduling algorithm. However, energy-aware disk scheduling algorithm increases buffer requirement significantly, e.g., from 4 to 140 KByte. We carefully argue that the buffer overhead is still justifiable given the cost of DRAM chip and importance of energy management in modern mobile devices. The result of our work not only provides the energy efficient scheduling algorithm but also provides an important guideline in capacity planning of future energy efficient mobile devices. This paper is funded by KOSEF through Statistical Research Paper for Complex System at Seoul National University.     

Performance evaluation of real-time decision-making architectures for computer-integrated manufacturing systems

A general method is proposed for the performance evaluation of a decision-making architecture for computer-integrated manufacturing systems. A decision-making architecture broadens the concept of a control architecture by integrating control, communication and database functions. A modular modeling methodology is developed that captures these features and is applicable to an arbitrary computer-integrated manufacturing architecture. The model is based on generalized stochastic Petri nets and leads to a quantitative evaluation of such performance measures as response time, average utilization of a particular system component, average queue length, etc. The net result is a design tool that can be used to make tradeoffs among the system parameters.

The proposed technique is demonstrated using several real-time decision-making architectures. Several general conclusions are drawn from this investigation. Finally, a Petri net model reduction method is presented for this problem and used to compare the original performance evaluation results with those obtained from the simplified models.     

模糊数据流实时任务调度算法

针对数据流系统中实时查询任务的特点,提出了基于模糊综合评判的动态优先级调度算法。用语言模糊集描述任务的不确定性因素和不同的优先等级,利用最大隶属度原理确定任务的优先等级。算法充分考虑了任务的持续周期性、任务之间存在依赖关系与共享滑动窗口的特点对任务优先级的影响。从累积实现价值率、差分截止期保证率和CPU切换频率3个方面测试了算法的性能,实验表明所提出算法相对于HVF算法、LSF算法、模糊动态抢占调度算法都有很大的改进。     

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.     

Memory-centric scheduling for multicore hard real-time systems

Memory resources are a serious bottleneck in many real-time multicore systems. Previous work has shown that, in the worst case, execution time of memory intensive tasks can grow linearly with the number of cores in the system. To improve hard real-time utilization, a real-time multicore system should be scheduled according to a memory-centric scheduling approach if its workload is dominated by memory intensive tasks. In this work, a memory-centric scheduling technique is proposed where (a)?core isolation is provided through a coarse-grained (high-level) Time Division Multiple Access (TDMA) memory schedule; and (b)?the scheduling policy of each core ??promotes?? the priority of its memory intensive computations above CPU-only computation when memory access is permitted by the high-level schedule. Our evaluation reveals that under high memory demand, our scheduling approach can improve hard real-time task utilization significantly compared to traditional multicore scheduling.     

长释放时间间隔优先的混合任务调度算法

针对混合任务实时调度的需求和MUF算法的局限性,提出了一种长释放时间间隔优先的混合任务实时调度算法LRIF,该算法除了可对周期性硬实时任务提供调度保证外,同时还可确保非周期性软实时任务的可调度率。论文还提出了LRIF调度算法的可调度性分析方法,并讨论了LRIF调度算法的实现方法。     

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.     

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     

Intra-task device scheduling for real-time embedded systems

An ever increasing need for extra functionality in a single embedded system demands for extra Input/Output (I/O) devices, which are usually connected externally and are expensive in terms of energy consumption. To reduce their energy consumption, these devices are equipped with power saving mechanisms. While I/O device scheduling for real-time (RT) systems with such power saving features has been studied in the past, the use of energy resources by these scheduling algorithms may be improved.Technology enhancements in the semiconductor industry have allowed the hardware vendors to reduce the device transition and energy overheads. The decrease in overhead of sleep transitions has opened new opportunities to further reduce the device energy consumption. In this research effort, we propose an intra-task device scheduling algorithm for real-time systems that wakes up a device on demand and reduces its active time while ensuring system schedulability. This intra-task device scheduling algorithm is extended for devices with multiple sleep states to further minimise the overall device energy consumption of the system. The proposed algorithms have less complexity when compared to the conservative inter-task device scheduling algorithms. The system model used relaxes some of the assumptions commonly made in the state-of-the-art that restrict their practical relevance. Apart from the aforementioned advantages, the proposed algorithms are shown to demonstrate the substantial energy savings.     

Fault-tolerant and real-time scheduling for mixed-criticality systems

The design and analysis of real-time scheduling algorithms for safety-critical systems is a challenging problem due to the temporal dependencies among different design constraints. This paper considers scheduling sporadic tasks with three interrelated design constraints: (i) meeting the hard deadlines of application tasks, (ii) providing fault tolerance by executing backups, and (iii) respecting the criticality of each task to facilitate system’s certification. First, a new approach to model mixed-criticality systems from the perspective of fault tolerance is proposed. Second, a uniprocessor fixed-priority scheduling algorithm, called fault-tolerant mixed-criticality (FTMC) scheduling, is designed for the proposed model. The FTMC algorithm executes backups to recover from task errors caused by hardware or software faults. Third, a sufficient schedulability test is derived, when satisfied for a (mixed-criticality) task set, guarantees that all deadlines are met even if backups are executed to recover from errors. Finally, evaluations illustrate the effectiveness of the proposed test.     

QoC elastic scheduling for real-time control systems

The elastic task model, a significant development in scheduling of real-time control tasks, provides a mechanism for flexible workload management in uncertain environments. It tells how to adjust the control periods to fulfill the workload constraints. However, it is not directly linked to the quality-of-control (QoC) management, the ultimate goal of a control system. As a result, it does not tell how to make the best use of the system resources to maximize the QoC improvement. To fill in this gap, a new feedback scheduling framework, which we refer to as QoC elastic scheduling, is developed in this paper for real-time process control systems. It addresses the QoC directly through embedding both the QoC management and workload adaptation into a constrained optimization problem. The resulting solution for period adjustment is in a closed-form expressed in QoC measurements, enabling closed-loop feedback of the QoC to the task scheduler. Whenever the QoC elastic scheduler is activated, it improves the QoC the most while still meeting the system constraints. Examples are given to demonstrate the effectiveness of the QoC elastic scheduling.     

实时系统中弹性调度策略

弹性调度面向负载可变的实时系统,通过动态调整任务属性以满足系统的灵活性要求,是一种高效的任务调度策略。针对弹性调度研究中的成果及问题,概述了弹性调度的研究背景,从任务模型、调度模型以及调度算法三个方面对弹性调度的国内外研究进展进行综述,探讨当前研究中存在的问题,并对弹性调度未来研究工作进行分析和展望。     

Approximation algorithms for scheduling real-time jobs with multiple feasible intervals

Time-critical jobs in many real-time applications have multiple feasible intervals. Such a job is constrained to execute from start to completion in one of its feasible intervals. A job fails if the job remains incomplete at the end of the last feasible interval. Earlier works developed an optimal off-line algorithm to schedule all the jobs in a given job set and on-line heuristics to schedule the jobs in a best effort manner. This paper is concerned with how to find a schedule in which the number of jobs completed in one of their feasible intervals is maximized. We show that the maximization problem is -hard for both non-preemptible and preemptible jobs. This paper develops two approximation algorithms for non-preemptible and preemptible jobs. When jobs are non-preemptible, Algorithm Least Earliest Completion Time First (LECF) is shown to have a 2-approximation factor; when jobs are preemptible, Algorithm Least Execution Time First (LEF) is proved being a 3-approximation algorithm. We show that our analysis for the two algorithms are tight. We also evaluate our algorithms by extensive simulations. Simulation results show that Algorithms LECF and LEF not only guarantee the approximation factors but also outperform other multiple feasible interval scheduling algorithms in average.     

Implications of classical scheduling results for real-time systems

Knowledge of complexity, fundamental limits and performance bounds-well known for many scheduling problems-helps real time designers choose a good design and algorithm and avoid poor ones. The scheduling problem has so many dimensions that it has no accepted taxonomy. We divide scheduling theory between uniprocessor and multiprocessor results. In the uniprocessor section, we begin with independent tasks and then consider shared resources and overload. In the multiprocessor section, we divide the work between static and dynamic algorithms