Control latency for task assignment and scheduling of multiprocessor real-time control systems

For task assignment and scheduling problems of multiprocessor real-time control systems, a new performance index called the control latency, is proposed. In order to ensure smooth operation and good performance of real-time control systems, one must analyse the problem of combined task assignment and scheduling during the conceptual system design stage. The proposed performance index, the control latency, is defined as a weighted sum of feedback, command and monitoring latencies. Given a set of tasks for a specific control application, each task execution time and intra-/interprocessor communication latencies, an algorithm for combined task assignment and scheduling can be solved by minimizing this performance index, thereby providing the minimum time delay and best performance.     

Optimal online multiprocessor scheduling of sporadic real-time tasks is impossible

Optimal online scheduling algorithms are known for sporadic task systems scheduled upon a single processor. Additionally, optimal online scheduling algorithms are also known for restricted subclasses of sporadic task systems upon an identical multiprocessor platform. The research reported in this article addresses the question of existence of optimal online multiprocessor scheduling algorithms for general sporadic task systems. Our main result is a proof of the impossibility of optimal online scheduling for sporadic task systems upon a system comprised of two or more processors. The result is shown by finding a sporadic task system that is feasible on a multiprocessor platform that cannot be correctly scheduled by any possible online, deterministic scheduling algorithm. Since the sporadic task model is a subclass of many more general real-time task models, the nonexistence of optimal scheduling algorithms for the sporadic task systems implies nonexistence for any model which generalizes the sporadic task model.     

Scheduling image processing tasks in a multilayer system

Multilayer multiprocessor systems are generally employed in real-time applications such as robotics and computer vision. This paper introduces three heuristic algorithms for multiprocessor task scheduling in such systems. In our model, tasks with arbitrary processing times and arbitrary processor requirements are considered. The scheduling aims at minimising completion time of processes in a two-layer system. We employed an effective lower bound (LB) for the problem. Then, we analysed the average performance of the heuristic algorithms by computing the average percentage deviation of each heuristic solution from the LB on a set of randomly generated problems. We have also applied these algorithms for scheduling computer vision tasks running on prototype multilayer architecture. Our computational and empirical results showed that the proposed heuristic algorithms perform well.     

多处理器调度算法实现及其Petri网建模与仿真

多处理器调度算法在嵌入式实时系统领域中起着关键的作用。根据多处理器的特点,提出一种实时多处理器动态分割并行调度算法SPara。该算法解决了此前多处理器算法,如Myopic、EDPF等仅依据截止期对任务调度产生的问题,实现了增加任务紧迫度限制的调度策略,以及针对执行时间长、截止期紧迫任务的有效调度方法。同时算法结合高级颜色时间Petri网理论进行建模并仿真。测试结果表明,SPara算法在处理器利用率以及调度成功率方面较Myopic等算法有较大提高。     

Partitioning and mapping in embedded multiprocessor architectures in the presence of constraints

The paper focuses on the problem of partitioning and mapping parallel programs onto heterogeneous embedded multiprocessor architectures for real-time applications. Such applications present unique constraints and challenges. In addition to heterogeneity, the proposed partitioning and mapping algorithms satisfy memory, task throughput, task placement, intertask communication bandwidth, and co-location constraints. They do so for architectures that utilize circuit-switched (rather than packet-switched) interprocessor communication and optimize latency and throughput in addition to load-balancing. Finally, these mapping algorithms make use of knowledge of the local scheduling discipline to accommodate real-time scheduling constraints. Our focus is on unstructured parallel programs that fall into one of two classes: (i) the class of computations characteristic of control applications in a real-time environment where tasks execute concurrently, periodically exchanging information, and (ii) pipelined computation graphs found in sensor data processing applications. The algorithms are implemented in a set of tools that operate with commercial CASE tools at one end, and present an interface to multiprocessor simulators at the other end. Collectively, the algorithms form a significant component of an interactive design environment for the development and mapping of real-time embedded parallel programs. The paper describes the algorithms, the encapsulating toolset, and presents an example of their application to an existing embedded application—an Autonomous Underwater Vehicle application.     

New Algorithms for Resource Reclaiming from Precedence Constrained Tasks in Multiprocessor Real-Time Systems

The scheduling of tasks in multiprocessor real-time systems has attracted many researchers in the recent past. Tasks in these systems have deadlines to be met, and most of the real-time scheduling algorithms use worst case computation times to schedule these tasks. Many resources will be left unused if the tasks are dispatched purely based on the schedule produced by these scheduling algorithms, since most of the tasks will take less time to execute than their respective worst case computation times. Resource reclaiming refers to the problem of reclaiming the resources left unused by a real-time task when it takes less time to execute than its worst case computation time. In this paper, we propose two algorithms to reclaim these resources from real-time tasks that are constrained by precedence relations and resource requirements, in shared memory multiprocessor systems. We introduce a notion called a restriction vector for each task which captures its resource and precedence constraints with other tasks. This will help not only in the efficient implementation of the algorithms, but also in obtaining an improvement in performance over the reclaiming algorithms proposed in earlier work [[2]]. We compare our resource reclaiming algorithms with the earlier algorithms and, by experimental studies, show that they reclaim more resources, thereby increasing the guarantee ratio (the ratio of the number of tasks guaranteed to meet their deadlines to the number of tasks that have arrived), which is the basic requirement of any resource reclaiming algorithm. From our simulation studies, we demonstrate that complex reclaiming algorithms with high reclaiming overheads do not lead to an improvement in the guarantee ratio.     

Utilization Bounds for EDF Scheduling on Real-Time Multiprocessor Systems

The utilization bound for earliest deadline first (EDF) scheduling is extended from uniprocessors to homogeneous multiprocessor systems with partitioning strategies. First results are provided for a basic task model, which includes periodic and independent tasks with deadlines equal to periods. Since the multiprocessor utilization bounds depend on the allocation algorithm, different allocation algorithms have been considered, ranging from simple heuristics to optimal allocation algorithms. As multiprocessor utilization bounds for EDF scheduling depend strongly on task sizes, all these bounds have been obtained as a function of a parameter which takes task sizes into account. Theoretically, the utilization bounds for multiprocessor EDF scheduling can be considered a partial solution to the bin-packing problem, which is known to be NP-complete. The basic task model is extended to include resource sharing, release jitter, deadlines less than periods, aperiodic tasks, non-preemptive sections, context switches, and mode changes.     

一种同构多处理机动态实时调度算法

本文提出一种新型线性复杂度多处理机实时任务启发式调度算法,利用并行技术为动态实时系统提供较优解．使用大量存在可行调度的任务集合测试多处理机实时任务调度算法的性能,分析了几种主要参数对调度成功率的影响．实验表明新调度算法调度成功率较高,适用于不完全知晓任务参数的动态多处理机实时系统．     

A genetic algorithm for multiprocessor scheduling

The problem of multiprocessor scheduling can be stated as finding a schedule for a general task graph to be executed on a multiprocessor system so that the schedule length can be minimized. This scheduling problem is known to be NP-hard, and methods based on heuristic search have been proposed to obtain optimal and suboptimal solutions. Genetic algorithms have recently received much attention as a class of robust stochastic search algorithms for various optimization problems. In this paper, an efficient method based on genetic algorithms is developed to solve the multiprocessor scheduling problem. The representation of the search node is based on the order of the tasks being executed in each individual processor. The genetic operator proposed is based on the precedence relations between the tasks in the task graph. Simulation results comparing the proposed genetic algorithm, the list scheduling algorithm, and the optimal schedule using random task graphs, and a robot inverse dynamics computational task graph are presented     

一种高效的实时多处理器系统的资源回收算法

实时多处理器系统中,为了更好地利用资源,常常采用资源回收算法。在分析了已有资源回收算法的优缺点的基础上,提出了一种高效的实时多处理器的资源回收算法——可倒置法。可倒置法允许只存在资源冲实的任务,在不会引起运行时间异常的情况下,出现执行顺序倒置。实验结果表明改进的限制向量法要由于已有的资源回收算法。     

多处理器计算环境中基于能量节约的实时动态调度算法

当前处理器由于较高的能量消耗，导致处理器热量散发的提高及系统可靠性的降低，已经成为目前计算机领域较为关心的问题．然而目前一些有效降低能量消耗的技术大多针对单处理器系统，较少考虑多处理器系统．提出的调度算法针对多处理器计算环境，以执行时间最快的任务优先调度为基础，结合其它有效技术（共享空闲时间回收），使得实时任务在其截止期内完成的同时能够有效地减低整个系统的能量消耗．针对独立任务集及具有依赖关系的任务集，提出两种针对同构计算环境的算法：STFBA1（Shortest—Task—First—Based Algorithm）及STFBA2，及两钟针对多任务集的算法HSA1（Hybrid Seheduling Algorithm）及HAS2．在单任务集计算环境下，与目前所知的有效算法相比，算法具有更好的性能（调度长度及能量消耗）．在多任务集计算环境下，基于混合调度策略的算法能够明显改进调度性能．     

Orthogonal multiprocessor sharing memory with an enhanced mesh for integrated image understanding

This paper proposes a new parallel architecture, which has the potential to support low-level image processing as well as intermediate and high-level vision analysis tasks efficiently. The integrated architecture consists of an SIMD mesh of processors enhanced with multiple broadcast buses, and MIMD multiprocessor with orthogonal access buses, and a two-dimensional shared memory array. Low-level image processing is performed on the mesh processor, while intermediate and high-level vision analysis is performed on the orthogonal multiprocessor. The interaction between the two levels is supported by a common shared memory. Concurrent computations and I/O are made possible by partitioning the memory into disjoint spaces so that each processor system can access a different memory space. To illustrate the power of such a two-level system, we present efficient parallel algorithms for a variety of problems from low-level image processing to high-level vision. Representative problems include matrix based computations, histogramming and key counting operations, image component labeling, pyramid computations, Hough transform, pattern clustering, and scene labeling. Through computational complexity analysis, we show that the integrated architecture meets the processing requirements of most image understanding tasks.     

A parallel architecture for performing mail sorting in real time

This paper describes a special-purpose embedded multiprocessor architecture developed for performing real-time multi-line optical character recognition (MLOCR). MLOCR is a computationally intensive real-time application involving pattern recognition, character image extraction, gray-scale thresholding, rotation and scaling of individual characters, and character identification. The computational complexity of the MLOCR application dictated the development of custom hardware in a parallel processing environment in order to meet the real-time system requirements. The overall system organization is described, along with the functional partitioning of algorithms onto processors, development of specific custom hardware to implement the algorithms in real time, interprocess communications, and system control.     

实时多处理器系统中基于能量节约的动态调度算法

当前处理器由于较高的能量消耗。导致处理器热量散发的提高及系统可靠性的降低，已经成为目前计算机领域较为关心的问题．然而目前一些有效降低能量消耗的技术大多针对单处理器系统，较少考虑多处理器系统．本文提出的调度算法针对多处理器系统，以最短任务优先调度为基础，结合其它有效技术，如共享空闲时间回收等，使得实时任务在其截止期内完成的同时能够有效地减低整个系统的能量消耗．针对独立任务集及具有依赖关系的任务集，本文提出两种算法：STFBA1及STFBA2（Shortest Task First—Based Algorithm）．与目前所知的有效算法相比，我们的算法具有更好的性能（调度长度及能量消耗）．     

An efficient dynamic scheduling algorithm for multiprocessorreal-time systems

Many time-critical applications require predictable performance and tasks in these applications have deadlines to be met. In this paper, we propose an efficient algorithm for nonpreemptive scheduling of dynamically arriving real-time tasks (aperiodic tasks) in multiprocessor systems. A real-time task is characterized by its deadline, resource requirements, and worst case computation time on p processors, where p is the degree of parallelization of the task. We use this parallelism in tasks to meet their deadlines and, thus, obtain better schedulability compared to nonparallelizable task scheduling algorithms. To study the effectiveness of the proposed scheduling algorithm, we have conducted extensive simulation studies and compared its performance with the myopic scheduling algorithm. The simulation studies show that the schedulability of the proposed algorithm is always higher than that of the myopic algorithm for a wide variety of task parameters     

Schedulability Analysis of Global Scheduling Algorithms on Multiprocessor Platforms

This paper addresses the schedulability problem of periodic and sporadic real-time task sets with constrained deadlines preemptively scheduled on a multiprocessor platform composed by identical processors. We assume that a global work-conserving scheduler is used and migration from one processor to another is allowed during a task lifetime. First, a general method to derive schedulability conditions for multiprocessor real-time systems will be presented. The analysis will be applied to two typical scheduling algorithms: earliest deadline first (EDF) and fixed priority (FP). Then, the derived schedulability conditions will be tightened, refining the analysis with a simple and effective technique that significantly improves the percentage of accepted task sets. The effectiveness of the proposed test is shown through an extensive set of synthetic experiments.     

NETRA: a hierarchical and partitionable architecture for computervision systems

Computer vision is regarded as one of the most complex and computationally intensive problems. In general, a Computer Vision System (CVS) attempts to relate scene(s) in terms of model(s). A typical CVS employs algorithms from a very broad spectrum such as numerical, image processing, graph algorithms, symbolic processing, and artificial intelligence. The authors present a multiprocessor architecture, called “NETRA,” for computer vision systems. NETRA is a highly flexible architecture. The topology of NETRA is recursively defined, and hence, is easily scalable from small to large systems. It is a hierarchical architecture with a tree-type control hierarchy. Its leaf nodes consists of a cluster of processors connected with a programmable crossbar with selective broadcast capability to provide the desired flexibility. The processors in clusters can operate in SIMD-, MIMD- or Systolic-like modes. Other features of the architecture include integration of limited data-driven computation within a primarily control flow mechanism, block-level control and data flow, decentralization of memory management functions, and hierarchical load balancing and scheduling capabilities. The paper also presents a qualitative evaluation and preliminary performance results of a cluster of NETRA     

基于Xenomai的实时测控系统的研究与实现

介绍实时操作系统仿真框架Xenomai和操作系统自适应域环境Adeos(Adaptive Domain Environment for Operating System)的基本原理.分析带有中断屏蔽功能的Adeos中断管道机制,以及用户态下混合执行模式的实时任务被抢占和"优先级反转"问题.应用Xenomai的实时核构建了Xenomai/Linux双内核实时机制;通过Xenomai提供的可插式接口仿真模块实现了基于RTAI API的应用程序编程;结合IPC等具体硬件实现了一套模拟小型电站任务的实时测控系统.运行结果显示系统满足电力系统硬实时的时限要求且运行稳定.     

Approximation algorithms in partitioning real-time tasks with replications

Today is an era where multiprocessor technology plays a major role in designs of modern computer architecture. While multiprocessor systems offer extra computing power, it also opens a new range of opportunities to improve fault-robustness. This paper focuses on a problem of achieving fault-tolerance using replications in real-time, multiprocessor systems. In the problem, multiple replicas, or copies, of a computing task are executed on distinct processors to resist potential processor failures and computing faults. Two greedy, approximation heuristics, named Worst Fit Increasing K-Replication and First Fit Increasing K-Replication, are studied to maximise the number of real-time tasks assigned on a system with identical processors, respecting to the tasks’ replicating and timely requirements. Worst case performance is analysed by using an approximation ratio between the algorithms and an optimal solution. We mathematically prove that the ratios of using both algorithms are infinitely close to 2. Simulations are performed on a large set of testing cases which can be used to bring to light the average performance of using the algorithms in practice. The results show that both heuristic algorithms provide simple but fast and effective solutions to solve the problem.