实时多处理器系统的动态分批优化调度算法

提出了一种实时多处理器系统的新的高效动态调度算法--动态分批优化调度算法,该算法突破了以往算法中一次只安排一项任务的做法,采用在每次扩充当前局部调度时,按一定规则在待调度的任务集中选取一批任务,对该批任务中的每项任务在每个处理器上运行构造目标函数,将问题转化为非平衡分配问题,一次性为这些任务都安排一个处理器或为每个处理器安排一项任务,使得这种安排具有最好的"合适性",以增大未安排任务的可行性.这种方法极大地提高了算法的调度成功率.同时,为了研究该算法的有效性,对其进行了大量的模拟,分析了一些任务参数的变化对算法调度成功率的影响,并与节约算法的调度成功率进行了比较.模拟结果显示,在节约算法的调度成功率小于10%的约束条件下,该算法的调度成功率大于90%,说明新算法的优势是非常明显的.     

一种批优化调度策略的实时异构系统的集成动态调度算法

针对实时异构多任务调度的特点,提出了软、硬实时任务形式化描述非精确计算的统一任务模型,在此基础上,提出了一种基于批优化调度策略的实时异构系统的集成动态调度算法.该算法以启发式搜索为基础,引入软实时任务服务质量降级策略,在每次扩充当前局部调度时,按制定的规则选取一批任务,计算其在各处理器上运行的目标函数,采用指派问题解法对任务优化分配.模拟实验表明,该算法与同类算法相比,提高了调度成功率.     

一种新的实时多处理器系统的动态调度算法

实时多处理器系统的动态调度算法一直是实时系统研究中的重要课题,而评价实时调度算法性能的一个最重要的指标是调度成功率.在近视算法的基础上提出了一种新的实时多处理器系统的动态调度算法--节约算法.在该算法中,提出了一个新的处理器选择策略,从而提高了算法的调度成功率.同时,为了研究节约算法的有效性,对其进行了大量的模拟,分析了一些任务参数的变化对算法调度成功率的影响,并与近视算法的调度成功率进行了比较.模拟结果显示,节约算法的调度成功率要优于近视算法.     

单处理器最少延误问题的双逆向调度算法

单处理器最少延误问题实际上是对任务集中每一个任务如何分配执行时间使得延误任务数量最少的问题,该问题是处理器调度问题中一类重要的基础问题.本文主要对该问题的调度算法进行研究,提出了一种基于排序的双逆向分配任务执行时间的调度算法,称为双逆向调度算法,该算法时间复杂度为O(n2),通过验证该算法是可行的.另外,还对任务关系进行了分析,并提出了任务固有冲突、任务临界冲突时刻、任务时间窗口中心点、任务间接冲突、任务冲突度等概念.     

最后期限动态分配的三步云工作流调度算法

现如今,云环境中的工作流调度问题依然很有挑战性.它的一个重要任务是找到一种能够满足最后期限约束且执行成本最优的调度方案.三步的列表调度算法可以有效地解决这一问题.该算法首先将最后期限分配到每个任务,形成任务子期限;之后再利用两步列表调度策略为每个任务分配资源.然而现有的最后期限分配策略均只能形成静态的子期限,因此还可以进行进一步的优化.本文采用三步列表调度算法进行云工作流调度,并提出一种基于粒子群的动态最后期限分配方法(DY-DD).实验结果表明,相比于其它经典调度算法,本文提出的算法在成功率和执行成本上均具有优势.     

实时多处理器系统的双优先级调度算法

针对同构多处理器系统提出一种基于双优先级的实时任务调度算法.对偶发任务进行接受测试,进一步提高了系统对偶发任务调度的成功率.模拟结果表明,当多核处理器系统利用率达到极限时,该算法依然能够在完成强实时周期任务的成功调度前提下,保证软实时周期任务和偶发任务具有较高的调度成功率.     

异构实时多处理器系统的动态调度算法研究

实时多处理器系统的动态调度算法一直是实时系统中的重要研究课题.根据异构实时多处理器的特点,提出了一种新的异构实时动态调度算法P_IEFT.该算法采用了一个新的处理器分配策略——将任务分配到能最早完成任务的处理器上.该策略能够缩短调度长度,提高后继任务被接受的可能性,从而能够提高成功调度率.模拟结果表明,该调度算法的成功调度率高于近视算法和节约算法的成功调度率.     

一种调度In-Tree任务图的算法

In-Tree任务图可用来表示归并、求和等分治算法的很多问题,该文针对这种任务图提出一种分层调度算法,利用队列存放被调度的任务,在同层任务调度中,优先把前驱不为空的任务调度到其一个前驱处理器上执行,只有前驱为空的任务才考虑是否分配新的处理器。实验表明,与以前的算法相比,该算法在调度长度相当的情况下,使用了更少的处理器。     

基于分批优化的实时多处理器系统的集成动态调度算法

提出了一种基于分批优化的实时多处理器系统的集成动态调度算法，该算法采用在每次扩充当前局部调度时，通过对所选取的一批任务进行优化分配的策略以及软实时任务的服务质量QoS（quality of service）降级策略，以统一方式实现了对实时多处理器糸统中硬、软实时任务的集成动态调度．进行了大量的模拟研究，结果表明．在多种任务参数取值下，新算法的调度成功率均高于近视算法（Myopic Algorithm）．     

一种实时异构系统的集成动态调度算法

提出了一种实时异构系统的集成动态调度算法.该算法通过一个新的任务分配策略以及软实时任务的服务质量QoS(quality of service)降级策略,不仅以统一方式完成了对实时异构系统中硬、软实时任务的集成动态调度,而且提高了算法的调度成功率.同时,还进行了大量的模拟研究.这些模拟以传统的近视算法为基准,将其应用在实时异构系统集成动态调度时的调度成功率与新算法进行比较,模拟结果表明,在多种任务参数取值下,新算法的调度成功率均高于传统的近视算法.     

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     

A fault-tolerant dynamic scheduling algorithm for multiprocessorreal-time systems and its analysis

Many time-critical applications require dynamic scheduling with predictable performance. Tasks corresponding to these applications have deadlines to be met despite the presence of faults. In this paper, we propose an algorithm to dynamically schedule arriving real-time tasks with resource and fault-tolerant requirements on to multiprocessor systems. The tasks are assumed to be nonpreemptable and each task has two copies (versions) which are mutually excluded in space, as well as in time in the schedule, to handle permanent processor failures and to obtain better performance, respectively. Our algorithm can tolerate more than one fault at a time, and employs performance improving techniques such as 1) distance concept which decides the relative position of the two copies of a task in the task queue, 2) flexible backup overloading, which introduces a trade-off between degree of fault tolerance and performance, and 3) resource reclaiming, which reclaims resources both from deallocated backups and early completing tasks. We quantify, through simulation studies, the effectiveness of each of these techniques in improving the guarantee ratio, which is defined as the percentage of total tasks, arrived in the system, whose deadlines are met. Also, we compare through simulation studies the performance our algorithm with a best known algorithm for the problem, and show analytically the importance of distance parameter in fault-tolerant dynamic scheduling in multiprocessor real-time systems     

Increasing processor utilization in hard-real-time systems with checkpoints

Often hard real-time systems require results that are produced on time despite the occurrence of processor failures. This paper considers a distributed system where tasks are periodic and each task occurs in multiple copies which are periodically synchronized in order to handle failures. The problem of preemptively scheduling a set of such tasks is discussed where every occurrence of a task has to be completely executed before the next occurrence of the same task. First, a static scheduling algorithm is proposed which uses periodic checkpoints to tolerate processor failures. Then, the performance of the algorithm is substancially improved employing a mixed strategy which constructs a schedule where high frequency tasks are duplicated, and low frequency tasks are periodically checkpointed. The performance of the solution proposed is evaluated in terms of the minimum achievable processor utilization due to the useful computation of the tasks. Moreover, analytical and simulation studies are used to reveal interesting trade-offs associated with the scheduling algorithm. In particular, if high frequency tasks are less than 70 percent of the total number of tasks then the mixed strategy yields a higher processor utilization than the task duplication scheme.     

基于超立方体的静态任务调度

该文给出一个基于超立方体的静态任务调度算法.在算法的设计中,首先建立了任务优先级表和处理机优先级表,任务在调度时总是顺次调度高优先级任务,然后再从处理机优先级表中选择能使该任务最早开始执行的处理机.最后,分别给出了基于LU分解的任务图与随机生成的任务图的调度结果.     

求解多处理机调度问题的蚁群算法

蚁群算法是受自然界中的蚂蚁觅食行为启发而设计的智能优化算法,特别适合处理离散型的组合优化问题。提出一种求解多处理机调度的蚁群算法,利用一个蚂蚁代表一个处理机来选择任务,并通过分析关键路径及每个任务的最早、最迟开始时间来确定每个任务的紧迫程度,让蚂蚁以此来选择任务。实验证明,该算法可比传统算法取得有更好运行效率的调度策略。     

分布式控制系统中多种混合任务的容错调度

分布式控制系统中存在有强实时、软实时和非实时等多种实时性的任务,其中强实时任务必须在其时限前完成,否则会出现灾难性后果,因此必须为分布式控制系统提供一定的容错能力。首先给出了用于调度多种实时性任务的单处理器调度算法——双优先级队列调度算法,并分析算法的可调度性条件。针对分布式控制系统,考虑基版本与副版本的执行时间不同时,结合版本复制技术和单处理器调度算法提出了一种新的容错调度算法。分析了算法的可调度行,给出了可任务集的可调度条件判断方法和基版本任务时限的设置方法。在此基础上,采用启发式静态任务分配算法,保证各处理器的负载均衡。本算法在保证任务容错可调度的条件下,可提高系统中各处理器的利用率,仿真结果表明该算法是有效的。     

Research on the collaboration of service selection and resource scheduling for IoT simulation workflows

Cloud resources provide a promising way to efficiently perform the needed simulation tasks for a complex manufacturing process. Most of the existing work focuses only on how to effectively schedule computing resources to execute computing requirements of simulation workflows in Internet of Things (IoT) applications. Research on the scheduling of simulation workflows in consideration of task ordering, service selection, and resource allocation altogether has not been lacking. To fill in this void, this paper proposes a cloud-based 3-stage workflow scheduling model. Before scheduling computing resources to complete task requirements, the order of the tasks is determined and the services that can meet the task requirements are selected. In this model, the workload to satisfy task requirements is not fixed and takes on a different value depending upon the service selected with its unique complexity and accuracy. An optimization function that transforms and integrates makespan, cost, and accuracy in a unique way is proposed. For its solution, the relatively new symbiotic organisms search (SOS) algorithm is modified and two SOS-based optimization strategies are developed, i.e., joint optimization-based SOS (JOSOS) and split optimization-based SOS (SOSOS). The simulation results reveal that SOS-based algorithms, especially the SOSOS method, outperform all compared algorithms. Based on the proposed method, simulation services and computing resources can be rationally selected and scheduled to ensure the requirements of IoT applications.