On the Design and Implementation of a Shared Memory Dispatcher for Partially Clairvoyant Schedulers

With the onset of distributed computing in hard real-time applications, the problem of assigning to, scheduling in, and executing jobs on processors, has received a lot of attention. Usually, real-time systems are embedded in closed loop reactive environments with uncertain behaviors and such systems take varying times to respond to such stimuli. One of the fundamental features of such systems is the presence of complex timing constraints between pairs of jobs. A secondary feature is the non-constant nature of the execution times of jobs. Real-time operating systems such as MARUTI can measure the interval within which the execution time varies (Mosse et al. In: Second IEEE Workshop on Experimental Distributed System, pp. 29–34., IEEE, 1990; Levi et al. 1989, ACM Special Interest Group Operat Syst 23(3):90–106). Partially clairvoyant scheduling was introduced in (Saksena, Parametric Scheduling in Hard Real-Time Systems. PhD thesis, University of Maryland, College Park, June 1994) to schedule jobs with varying execution times and non-trivial timing constraints. The schedulability of the job set is determined offline and a set of dispatch functions are produced from the given set of constraints if the job set is schedulable. The dispatch functions bind the start time of a job J to an interval that depends on the start and execution times of jobs sequenced before J. The online dispatcher of the system reads these dispatch functions and computes the interval within which a job can start without violating the constraints imposed on the system. In certain situations, the dispatcher fails to dispatch a job as the time to compute the dispatch functions associated with a job is greater than the interval within which the job needs to be dispatched. This phenomenon is called Loss of Dispatchability (Subramani, Duality in the Parametric Polytope and its Applications to a Scheduling Problem. PhD thesis, University of Maryland, College Park, August 2000). In this paper, we propose and implement a partially clairvoyant dispatching algorithm on a shared memory cluster with Concurrent Read Exclusive Write (CREW) architecture and contrast it with the sequential approach. For a preset number of processors, our approach has O(1) dispatch complexity while using a total of O(n ²) space, while the sequential approach requires Ω(n) time. The detailed implementation profile obtained clearly demonstrates the superiority of the multiprocessor approach to dispatching. We also address the issue of scalability of the dispatcher for increasing number of processors and show that job sets of different sizes require different number of processors. Finally, we demonstrate the effect of execution time on the dispatchability of schedules.     

Global scheduling based reliability-aware power management for multiprocessor real-time systems

Reliability-aware power management (RAPM) has been a recent research focus due to the negative effects of the popular power management technique dynamic voltage and frequency scaling (DVFS) on system reliability. As a result, several RAPM schemes have been studied for uniprocessor real-time systems. In this paper, for a set of frame-based independent real-time tasks running on multiprocessor systems, we study global scheduling based RAPM (G-RAPM) schemes. Depending on how recovery blocks are scheduled and utilized, both individual-recovery and shared-recovery based G-RAPM schemes are investigated. An important dimension of the G-RAPM problem is how to select the appropriate subset of tasks for energy and reliability management (i.e., scale down their executions while ensuring that they can be recovered from transient faults). We show that making such decision optimally (i.e., the static G-RAPM problem) is NP-hard. Then, for the individual-recovery based approach, we study two efficient heuristics, which rely on local and global task selections, respectively. For the shared-recovery based approach, a linear search based scheme is proposed. The schemes are shown to guarantee the timing constraints. Moreover, to reclaim the dynamic slack generated at runtime from early completion of tasks and unused recoveries, we also propose online G-RAPM schemes which exploit the slack-sharing idea studied in previous work. The proposed schemes are evaluated through extensive simulations. The results show the effectiveness of the proposed schemes in yielding energy savings while simultaneously preserving system reliability and timing constraints. For the static version of the problem, the shared-recovery based scheme is shown to provide better energy savings compared to the individual-recovery based scheme, in virtue of its ability to leave more slack for DVFS. Moreover, by reclaiming the dynamic slack generated at runtime, online G-RAPM schemes are shown to yield better energy savings.     

Task construction for model-based design of embedded control software

Constructing runtime tasks, or operating system-level processes/threads, from the components of software design models is crucial to the model-based development of embedded control software. A better method should explore more design choices and reduce the overheads of the runtime system to meet the timing and resource constraints of embedded control software. This paper presents a novel, two-step method for systematic and automatic construction of runtime tasks from software design models. It uses graph transformation to construct a task set meeting system-level end-to-end (e2e) timing constraints. Its first step decomposes the system-level e2e timing constraints into the components' timing constraints, which form a necessary condition for any valid and feasible schedule. The second step iteratively merges the components into tasks and sequences their executions. A thus-constructed task set is proven to meet both intercomponent precedence and system-level e2e timing constraints and to minimize runtime overheads by minimizing the total number of resultant tasks. Our evaluation results based on randomly generated software models have shown that the proposed method outperforms commonly used methods and is also scalable.     

A model for scheduling of object-based,distributed real-time systems

This paper describes a general model for pre-run-time scheduling of distributed real-time systems that are composed of abstract data types (definable in languages such as Ada, Clu and Modula-2) and abstract data objects (which can be defined in C++, Eiffel and RT Euclid). An architecture model, a programming paradigm, and execution and communication paradigms form the basis for this general model. The model includesabsolute timing constraints to represent periodicity and deadlines,relative timing constraints to model several kinds of timed precedence relations and synchronization requirements,independency constraints to capture non-determinism of conditionals and repetitions, andconsistency constraints to enforce consistent use of resources. In this paper, the model is formalized to obtain a mathematical foundation on which assignment (Verhoosel et al. 1993a, Welch 1992, Welch et al. 1993) and pre-run-time scheduling problems (Verhoosel et al. 1991, Verhoosel 1992, 1993a, 1993c) are defined. Additionally, the model is extended to allow exploitation of parallelism from programs, a technique that can be used during assignment and scheduling for meeting timing constraints.     

Task behavior monitoring for adaptive real-time communication

Real-time distributed systems include communicating tasks that interact via message-passing. In such systems the timely delivery of messages is essential for meeting task timing constraints. Consequently, in addition to task execution times, message delivery times must also be constrained. In order to minimize the number of failures to meet timing constraints message communication protocols, in addition to task scheduling algorithms, play a crucial role. A legitimate question to ask is whether making such protocols adaptive to run-time system and environment status can significantly improve system performance. Consequently, a rum-time monitoring approach to adaptive real-time distributed systems is proposed; the work focuses on an investigation of adaptive message communication protocols and corresponding run-time support mechanisms. Simulation is used to obtain performance results. It is concluded that although improvement is obtained it ,ay not be significant enough to offset the increased overhead and requirement for task information.     

Response modeling runtime schedulers for timing analysis of self-timed dataflow graphs

Constituent tasks of modern day Embedded Streaming Applications (ESAs), such as engine control systems, multimedia and software defined radios often exhibit execution behaviors that do not conform to conventional task models. ESAs consist of iterative, pipelined sequences of tasks that are conditioned by intra- and inter-iteration dependencies, and often have strict throughput and latency requirements. We model ESAs as dataflow graphs, where actors represent computational units, and directed edges represent communication channels between actors. Due to practical constraints like cost-effectiveness, power consumption and chip-area, multiple ESAs are run on a shared (multi-processor) platform. Thus rigorous timing analysis is required to verify whether individual ESAs meet their respective timing requirements.We look at response modeling, a compositional timing analysis approach wherein the local worst-case influence of runtime scheduling is represented within the constructs provided in dataflow. These local representations (called response models) can be composed together to construct a global understanding of an ESA’s worst-case execution which is then used to verify whether its real-time requirements are met. This paper proposes a generic response modeling technique for runtime scheduling of ESAs. We focus on preemptive Fixed Priority Scheduling (FPS) but also demonstrate that we can apply our technique to a wide range of runtime schedulers. In our experiments, we present academic and industrial case-studies that highlight the effectiveness of our approach in the timing analysis of ESAs with unconventional execution behavior.     

Efficient Construction of Minimum Makespan Schedules for Tasks with a Fixed Number of Distinct Execution Times

This paper addresses scheduling problems for tasks with release and execution times. We present a number of efficient and easy to implement algorithms for constructing schedules of minimum makespan when the number of distinct task execution times is fixed. For a set of independent tasks, our algorithm in the single processor case runs in time linear in the number of tasks; with precedence constraints, our algorithm runs in time linear in the sum of the number of tasks and the size of the precedence constraints. In the multi-processor case, our algorithm constructs minimum makespan schedules for independent tasks with uniform execution times. The algorithm runs in O(n log m) time where n is the number of tasks and m is the number of processors. Received September 25, 1997; revised June 11, 1998.     

Optimal feedback scheduling of model predictive controllers

Model predictive control （MPC） could not be reliably applied to real-time control systems because its computation time is not well defined. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance, thus obtaining the predictability in time. Optimal feedback scheduling （FS-CBS） of a set of MPC tasks is presented to maximize the global control performance subject to limited processor time. Each MPC task is assigned with a constant bandwidth server （CBS）, whose reserved processor time is adjusted dynamically. The constraints in the FS- CBS guarantee scheduler of the total task set and stability of each component. The FS-CBS is shown robust against the variation of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.     

采用配置完成优先策略的可重构任务调度算法

如何隐藏和减少配置时间是相依性可重构任务调度的关键问题.提出一种采用配置完成优先策略的相依性可重构任务调度算法,通过基于预配置优先级的列表调度算法,实现将后续任务的配置时间隐藏于前驱任务的运行时间中,并采用基于配置完成优先策略的配置重用机制,减少了任务调度后的配置过程,从而在总体上缩短了相依性任务集合的运行时间.仿真结果表明,该调度算法能有效避免调度死锁,并可减少相依性可重构任务的整体运行时间.     

中断驱动控制系统的有界模型检验技术

针对一类中断驱动的航天控制系统,给出了有界模型检验的算法.这类系统由中断处理程序和操作系统调度的任务组成.当中断发生时,对应的中断处理程序响应中断事件,并可以修改控制变量值,以便在系统任务中完成后续工作.操作系统周期性地调度任务序列处理日常事务以及中断事件的后续工作.使用了带中断标记的时间自动机对中断事件和任务调度事件进行建模,并使用中断向量表和中断处理程序的伪代码模型共同描述中断的处理过程.控制变量将中断处理过程和系统任务相关联,中断处理程序可以设定某个控制变量,而系统任务则通过检查该控制变量来确定是否需要进行后续处理.对于这样的形式化模型,给出了检验关键时序性质的有界模型检验算法.该算法使用深度优先的方式遍历所有长度小于等于K的可行路径,并使用SMT Z3实现了对时间约束和规约的处理.