基于异构环境的Out-Tree任务图的调度算法

分布式应用程序的有效调度是异构计算系统中的一个关键问题。目前已有的Out-Tree任务图的调度算法大多基于同构环境而开发,未考虑处理机的异构性,导致调度的效率较低。针对异构计算环境,提出一个基于列表和任务复制的Out-Tree任务图的静态启发式贪心调度算法,其时间复杂度为O(hv^2p),其中h、v和p分别表示任务图的高度、任务个数和调度使用的处理机个数。实验结果表明,相比其他算法,该算法能提供调度长度较短、处理机使用较少的有效调度,其应用性更强。     

基于异构分布式系统的实时容错调度算法

目前文献中研究的实时容错调度算法都是基于同构分布式系统，系统中的所有处理机完全相同。该文首先建立了一个基于异构分布式系统实时容错调度模型，异构分布式系统中的各个处理机均不相同。基于该异构分布式系统模型，该文引入了可靠性代价（reliability cost）概念，并提出两种静态实时容错调度算法（RTFTNO和RTFTRC）用于调度周期性实时容错任务。算法RTFTRC在调度任务时，尽量使系统的可靠性代价最小；而算法RTFTNO在调度实时任务时，没有考虑系统的可靠性代价。该文详细讨论了两种调度算法的性能。性能模拟实验分别比较了两个算法的可靠性代价，超时比率和可调度性；并研究了任务的计算时间与可靠性代价的关系以及调度长度阈值与最小处理机个数的关系。实验结果表明，算法RTFTRC的性能优于算法RTFTNO。     

混合型实时容错调度算法的设计和性能分析

以往文献中研究的实时容错调度算法都只能调度单一的具有容错需求的任务.该文建立了一个混合型实时容错调度模型,提出一种静态实时容错调度算法.该算法能同时调度具有容错需求的实时任务和无容错需求的实时任务.该文还提出了一个求解最小处理机个数的算法,用于对静态实时容错调度算法的性能进行模拟分析.为了提高静态调度算法的调度性能,提出了一种动态调度算法.最后,通过模拟实验分析了静态和动态调度算法的性能.实验表明,调度算法的性能与实时任务的个数、任务的计算时间、周期和处理机个数等系统参数相关.     

异构环境中Fork-Join任务图的调度算法

目前已有的Fork-Join任务图的调度算法大多假定处理机为同构的,而没有考虑实际应用中处理机的异构性以及节省处理机的问题,导致算法在具体应用中效率较低.因此,对Fork-Join任务图的调度问题进行研究,提出了一个基于异构环境的贪心调度算法,该算法具有高的加速比和总体效率,其时间复杂度为O(v~2),其中,v表示任务集中任务的个数.实验结果表明,相比其它算法,该算法具有较短的调度长度、较短的完成时间,使用的处理机数较少,具有更强的实用性.     

云环境下周期和非周期混合实时任务双容错调度算法

云环境中的处理机故障已成为云计算不可忽视的问题,容错成为设计和发展云计算系统的关键需求。针对一些容错调度算法在任务调度过程中调度效率低下以及任务类型单一的问题,提出一种处理机和任务主副版本分组的容错调度方法;并给出了副版本可重叠执行的判定方法,以及任务最坏响应时间的计算公式。通过实验和分析表明,和以前算法相比,将处理机分成两组分别执行任务主版本和任务副版本,减少了任务调度所需进行可调度测试的时间,增加了副版本重叠执行的机会,减少了所需的处理机个数,对提高系统处理机的利用率和容错调度的效率具有重要的意义。     

基于通信竞争的Fork-Join任务图的调度算法

Fork-Join任务图是一种并行处理的基本结构,目前已有的Fork-Join任务图的调度算法大多没有考虑实际应用中通信链路的竞争及延迟以及节省处理机的问题,导致算法在具体应用中效率较低.因此,针对Fork-Join任务图,提出一个基于通信竞争的贪心调度算法,该算法具有高的加速比和总体效率,时间复杂度为O(vlogv),其中v表示任务集中任务的个数.实验结果表明,该算法相比其它算法具有较短的调度长度、较短的完成时间,使用的处理机数较少,具有更强的实用性.     

同构环境中Join任务图的一个调度算法

任务调度问题是一个NP完全问题。Join结构是一种并行处理的基本结构,虽然许多算法对Join任务图能产生最优调度,但大多都忽略了节省处理机个数和最小化程序总的完成时间等问题。因此,专门针对Join任务图,提出一个能产生最优调度的同构贪心调度算法,该算法具有高的加速比和总体效率,时间复杂度为O（v2）,其中,v表示任务集中任务的个数。实验结果表明,相比其他算法,该算法具有较短的调度长度、较短的完成时间,使用的处理机数较少。     

基于多处理机的混合实时任务容错调度

提出了一种混合实时任务容错调度算法．该算法采用Rate Monotonic(RM)算法完成周期任务的静态调度；采用预订处理机时间方法和Earlier Deadline First(EDF)算法动态调度非周期任务；采用主／副版本备份技术确保系统的容错能力．通过充分利用周期任务的剩余处理机时间调度非周期任务和主动备份与被动备份相结合的方法有效地减少了处理机数．仿真结果证明了算法的有效性．     

先来先服务与短作业优先调度算法的比较

为了打破处理机等资源限制,通过不同的调度算法,按照一定规律,允许不同的进程占用处理机,分配资源。依据系统资源分配策略分配资源的算法是调度算法。调度算法也可用于进程调度和作业调度。当系统性能和系统目标不同时,要选择不同的调度算法。基于此,针对调度算法中最简单的两种算法——先来先服务调度算法和短作业优先调度算法,分别介绍并比较。     

常见进程调度算法的比较与改进

在多道程序系统中,有多个进程存在于主存中且其数目一般多于处理机数目,这会导致它们互相争夺处理机。这就需要系统按某种算法,能动态地把处理机分配给处于就绪队列中的某一个进程让其执行。采用什么样的算法把处理机分配给进程便成了进程调度的核心问题,为此,该文详细分析了处理机调度的各种调度算法。     

一种基于调度簇树的周期性分布实时任务调度算法

本文针对现有的基于任务复制的静态调度算法在调度周期性分布实时任务时存在的缺点，提出了一种称之为调度簇树（SCT）的新的结构并研究了其特性，在此基础上给出了一种基于SCT树的周期性分布实时任务调度算法（SAS）。通过与OSA算法进行比较的实验结果表明，SAS算法可实现调度长度向上最接近分布实时任务周期，最大程度减少所需顸留处理器数目，大大提高分布实时系统的处理器利用率，同时并不增加调度算法的复杂度。     

基于时间帧的处理器PFair调度改进算法

为了解决PFair算法进行交互任务调度时,由于忽略了不同阶段的周期性任务而导致多个线程之间任务的迁移问题以及空间和时间的浪费问题,提出了基于时间帧的处理器PFair调度改进算法。该算法基于周期性任务系统的特点,引入时间帧控制和改变本地周期性任务调度来限制任务迁移,从而实现对PFair算法的改进。为了评估算法的迁移开销和公平性,通过实验对普通PFair算法及本文所提出的改进算法ERfair进行对比实验,结果表明,改进算法ERfair能够通过时间帧内调度和分区控制大大降低任务在不同处理器间的迁移次数。基于时间帧的处理器PFair调度改进算法在保证公平性的同时,提高了系统的效率,应用于多核处理器上的任务调度是可行的、有效的。     

Contention-aware scheduling with task duplication

Finding an efficient schedule for a task graph on several processors is a trade-off between maximising concurrency and minimising interprocessor communication. Task duplication is a technique that has been employed to reduce or avoid interprocessor communication. Certain tasks are duplicated on several processors to produce the data locally and avoid the communication among processors. Most of the algorithms using task duplication have been proposed for the classic scheduling model, which allows concurrent communication and ignores contention for communication resources. It is increasingly recognised that this classic model is unrealistic and does not permit creating accurate and efficient schedules. The recently proposed contention model introduces contention awareness into task scheduling by assigning the edges of the task graph to the links of the communication network. It is intuitive that scheduling under such a model benefits even more from task duplication, yet no such algorithm has been proposed as it is not trivial to duplicate tasks under the contention model. This paper proposes a contention-aware task duplication scheduling algorithm. We investigate the fundamentals for task duplication in the contention model and propose an algorithm that is based on state-of-the-art techniques found in task duplication and contention-aware algorithms. An extensive experimental evaluation demonstrates the significant improvements to the speedup of the produced schedules.     

A high performance algorithm for static task scheduling in heterogeneous distributed computing systems

Effective task scheduling is essential for obtaining high performance in heterogeneous distributed computing systems (HeDCSs). However, finding an effective task schedule in HeDCSs requires the consideration of both the heterogeneity of processors and high interprocessor communication overhead, which results from non-trivial data movement between tasks scheduled on different processors. In this paper, we present a new high-performance scheduling algorithm, called the longest dynamic critical path (LDCP) algorithm, for HeDCSs with a bounded number of processors. The LDCP algorithm is a list-based scheduling algorithm that uses a new attribute to efficiently select tasks for scheduling in HeDCSs. The efficient selection of tasks enables the LDCP algorithm to generate high-quality task schedules in a heterogeneous computing environment. The performance of the LDCP algorithm is compared to two of the best existing scheduling algorithms for HeDCSs: the HEFT and DLS algorithms. The comparison study shows that the LDCP algorithm outperforms the HEFT and DLS algorithms in terms of schedule length and speedup. Moreover, the improvement in performance obtained by the LDCP algorithm over the HEFT and DLS algorithms increases as the inter-task communication cost increases. Therefore, the LDCP algorithm provides a practical solution for scheduling parallel applications with high communication costs in HeDCSs.     

基于DAG图解-重构的机群系统静态调度算法

机群系统静态任务调度是NP-完全问题,通常的算法是通过一些启发式算法得到多项式次优 解.该文提出的图解-子图重构算法实现了对分布在有向无环图(directed acyclic graph, 简称DAG)上的并行任务的快速有效调度.该算法的复杂性为O(log^|V|×(|V|+| E|)),采用递归方法实现了对任务图的有效分解和子图重构,生成任务群,完成任务调度,并 且初步实现了对处理机的优化.通过实例分析以及与其他启发式调度算法的性能比较,证明该 算法是一种快速、有效、可     

基于任务复制的网格任务调度算法

网格中资源之间存在着通信延迟，通过任务复制的冗余，可以减少任务之间的通信开销，缩短整个计算程序的计算时间。目前网格中的任务调度算法基本上是没有考虑任务复制的；而基于任务复制调度算法往往会产生过多的复制任务，增大系统开销，甚至有可能延迟计算时间。由于基于任务复制的任务调度是一个NP问题，因此本文提出了一种基于任务复制的网格资源调度算法，以减少调度长度为主要目标、减少任务复制量和资源占用量为次要目标。该算法在调度长度和任务复制数量以及占用资源数量方面都等于或优于其它算法。     

Benchmark-problem instances for static scheduling of task graphs with communication delays on homogeneous multiprocessor systems

Scheduling program tasks on processors is at the core of the efficient use of multiprocessor systems. Most task-scheduling problems are known to be NP-Hard and, thus, heuristics are the method of choice in all but the simplest cases. The utilization of acknowledged sets of benchmark-problem instances is essential for the correct comparison and analysis of heuristics. Yet, such sets are not available for several important classes of scheduling problems, including multiprocessor scheduling problem with communication delays (MSPCD) where one is interested in scheduling dependent tasks onto homogeneous multiprocessor systems, with processors connected in an arbitrary way, while explicitly accounting for the time required to transfer data between tasks allocated to different processors. We propose test-problem instances for the MSPCD that are representative in terms of number of processors, type of multiprocessor architecture, number of tasks to be scheduled, and task graph characteristics (task execution times, communication costs, and density of dependencies between tasks). Moreover, we define our task-graph generators in a way appropriate to ensure that the corresponding problem instances obey the theoretical principles recently proposed in the literature.     

On the complexity of fixed-priority scheduling of periodic, real-time tasks

We consider the complexity of determining whether a set of periodic, real-time tasks can be scheduled on m 1 identical processors with respect to fixed-priority scheduling. It is shown that the problem is NP-hard in all but one special case. The complexity of optimal fixed-priority scheduling algorithm is also discussed.     

A Bipartite Genetic Algorithm for Multi-processor Task Scheduling

Until now, several methods have been presented to optimally solve the multiprocessor task scheduling problem that is an NP-hard one. In this paper, a genetic-based algorithm has been presented to solve this problem with better results in comparison with related methods. The proposed method is a bipartite algorithm in a way that each part is based on different genetic schemes, such as genome presentation and genetic operators. In the first part, it uses a genetic method to find an adequate sequence of tasks and in the second one, it finds the best match processors. To evaluate the proposed method, we applied it on several benchmarks and the results were compared with well known algorithms. The experimental results were satisfactory and in most cases the presented method had a better makespan with at least 10% less iterations compared to related works.     

Online scheduling of moldable parallel tasks

In this paper, we study an online scheduling problem with moldable parallel tasks on m processors. Each moldable task can be processed simultaneously on any number of processors of a parallel computer, and the processing time of a moldable task depends on the number of processors allotted to it. Tasks arrive one by one. Upon arrival of each task, the scheduler has to determine both the number of processors and the starting time for the task. Moreover, these decisions cannot be changed in the future. The objective is to attain a schedule such that the longest completion time over all tasks, i.e., the makespan, is minimized. First, we provide a general framework to show that any \(\rho \)-bounded algorithm for scheduling of rigid parallel tasks (the number of processors for a task is fixed a prior) can be extended to yield an algorithm for scheduling of moldable tasks with a competitive ratio of \(4\rho \) if the ratio \(\rho \) is known beforehand. As a consequence, we achieve the first constant competitive ratio, 26.65, for the moldable parallel tasks scheduling problem. Next, we provide an improved algorithm with a competitive ratio of at most 16.74.