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.     

TSA—OT：一个调度Out—Tree任务科的算法

对于把一个任务群调度到多个处理器的问题,人们往往只注重找到一个调度路径最短的算法,却忽略了要节省处理器。收于Out-Tree任务图代表分治算法的一大类问题,因此,文中专门针对该任务图,给出了一个基于任务复制的算法TSA－OT。它首先分配关键路径上的任务结点,然后在不改变调度长度的情况下,把非关键路径上的结点尽可能分配到已用的处理器上。并且,该算法将Out-Tree任务图中的所有通信都化为零。TSA－OT算法与近几年所提出的TDS,CPFD,DCP算法之间的比较表明,TSA－OT算法不仅调度长度最短,而且采用了更少或相当个数的处理器。     

List scheduling with duplication for heterogeneous computing systems

Effective task scheduling is essential for obtaining high performance in heterogeneous computing systems (HCS). However, finding an effective task schedule in HCS, requires the consideration of the heterogeneity of computation and communication. To solve this problem, we present a list scheduling algorithm, called Heterogeneous Earliest Finish with Duplicator (HEFD). As task priority is a key attribute for list scheduling algorithm, this paper presents a new approach for computing their priority which considers the performance difference in target HCS using variance. Another novel idea proposed in this paper is to try to duplicate all parent tasks and get an optimal scheduling solution. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithm HEFD significantly surpasses other three well-known algorithms.     

一个调度Fork-Join任务图的新算法

任务调度是影响工作站网络效率的关键因素之一.Fork-Join任务图可以代表很多并行结构,但其他已有调度Fork-Join任务图算法忽略了在非全互连工作站网络环境中通信之间不能并行执行的问题,有些效率高的算法又没有考虑节省处理器个数的问题.因此,专门针对该任务图,综合考虑调度长度、非并行通信和节省处理器个数问题,提出了一个基于任务复制的静态调度算法TSA_FJ.通过随机产生任务的执行时间和通信时间,生成了多个Fork-Join任务图,并且采用TSA_FJ算法和其他调度算法对生成的任务图进行调度.结果表明,     

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.     

Data‐aware task scheduling on heterogeneous hybrid memory multiprocessor systems

In this paper, we propose a method about task scheduling and data assignment on heterogeneous hybrid memory multiprocessor systems for real‐time applications. In a heterogeneous hybrid memory multiprocessor system, an important problem is how to schedule real‐time application tasks to processors and assign data to hybrid memories. The hybrid memory consists of dynamic random access memory and solid state drives when considering the performance of solid state drives into the scheduling policy. To solve this problem, we propose two heuristic algorithms called improvement greedy algorithm and the data assignment according to the task scheduling algorithm, which generate a near‐optimal solution for real‐time applications in polynomial time. We evaluate the performance of our algorithms by comparing them with a greedy algorithm, which is commonly used to solve heterogeneous task scheduling problem. Based on our extensive simulation study, we observe that our algorithms exhibit excellent performance and demonstrate that considering data allocation in task scheduling is significant for saving energy. We conduct experiments on two heterogeneous multiprocessor systems. Copyright © 2016 John Wiley & Sons, Ltd.     

Optimizing layer‐based scheduling algorithms for parallel tasks with dependencies

Programming with parallel tasks leads to task graphs with dependencies representing a parallel program. Scheduling algorithms are employed to find an efficient execution order of the parallel tasks. A large variety of scheduling algorithms exist, including layer‐based scheduling algorithms for homogeneous target platforms that build consecutive layers of independent parallel tasks and schedule each layer separately. Although these scheduling algorithms provide good results in terms of scheduling algorithm runtime and schedule execution time, the resulting schedules leave room for optimization. This article proposes an optimization for arbitrary layer‐based scheduling algorithms, which is called Move‐blocks algorithm. Given a layer‐based schedule of the parallel tasks, this algorithm moves blocks of parallel tasks into preceding layers in order to reduce the overall execution time of a task‐based application. Suitable blocks of parallel tasks are identified by the algorithm Find‐blocks, which is employed together with the Move‐blocks algorithm. The algorithm Move‐blocks is applied to four well‐known scheduling algorithms. A detailed evaluation for a wide range of test cases is given. Copyright © 2010 John Wiley & Sons, Ltd.     

一个调度Out-Tree任务图的新算法

Out-Tree任务图代表分治算法的一大类问题。本文专门针对该类任务图，提出了一个新的调度算法。它利用fork结构的最优调度为各任务定义优先级，准确的反映了任务对调度的影响，保证了任务的正确调度顺序，得到优的调度长度。并在不改变调度长度的情况下，将结点尽可能地分配到已用处理器上，节省了处理器。实验表明，本文算法的调度性能优于现有同类算法。     

可重构系统中的实时任务在线调度与放置算法

有效的任务调度与放置是发挥可重构计算性能优势的重要因素.针对实时任务在二维可重构器件上的在线调度问题,定义了调度算法完全识别的概念,即算法不会拒绝能够成功调度的任务.提出了新的实时在线调度与放置算法,充分利用了任务的时间信息,实现了完全识别的调度.实验表明,与已有的算法相比,新算法显著地改善了调度效果,而运行开销没有明显增加.     

并行任务图的优化调度算法

科学与工程计算中的很多复杂应用问题需要使用科学工作流技术,超算领域中的科学工作流常以并行任务图建模,并行任务图的有效调度对应用的高效执行有重要意义。给出了资源限制条件下并行任务图的调度模型;针对Fork-Join类并行任务图给出了若干最优化调度结论;针对一般并行任务图提出了一种新的调度算法,该算法考虑了数据通信开销对资源分配和调度性能的影响,并对已有的CPA算法在特定情况下进行了改进。通过实验与常用的CPR和CPA算法做比较,验证了提出的新算法能够获得很好的调度效果。本文提出的调度算法和得到的最优调度结论对工作流应用系统的高性能调度功能开发具有借鉴意义。     

实时调度中基于多特征参数的任务优先级设计方法

本文在实时任务调度中基于任务的价值、剩余执行时间、空闲时间以及到达时间等多特征参数设计任务的优先级,并使任务的优先级随着任务紧迫性和完成程度变化而动态调整,并基于新的优先级设计策略提出一种实时动态抢占式调度算法VRSAF算法。仿真实验表明,在负载较轻时,VRSAF算法能获得近似EDF算法的调度性能;在过载情况下,其调度性能优于HVF算法;总体调度性能高,并能在系统过载的情况下实现平缓的降级。     

树型网格计算环境下的自适应任务调度算法

提出一种基于树型计算网格的自适应调度算法,实现对小粒度独立任务和用户大作业的自适应最优调度。通过对网格环境的实时检测,给出了基于节点负载状况、节点任务执行时间、任务传输时间和任务特性的自适应调度算法,即基于最优任务分配方案的启发式任务调度算法。通过实验与其他调度算法的比较,证明了所提出的任务调度算法在负载平衡和最优跨度方面具有明显的优越性。     

树型网格计算环境下的独立任务调度

任务调度是实现高性能网格计算的一个基本问题,然而,设计和实现高效的调度算法是非常具有挑战性的.讨论了在网格资源计算能力和网络通信速度异构的树型计算网格环境下,独立任务的调度问题.与实现最小化任务总的执行时间不同(该问题已被证明是NP难题),为该任务调度问题建立了整数线性规划模型,并从该线性规划模型中得到最优任务分配方案??各计算节点最优任务分配数.然后,基于最优任务分配方案,构造了两种动态的需求驱动的任务分配启发式算法:OPCHATA(optimization-based priority-computation heuristic algorithm for task allocation)和OPBHATA(optimization-basedpriority-bandwidth heuristic algorithm for task allocation).实验结果表明:在异构的树型计算网格环境下实现大量独立任务调度时,该算法的性能明显优于其他算法.     

软件容错模型中的容错实时调度算法

在软件容错模型的容错实时调度算法中,主部分可执行性的预测精度是影响调度算法性能的关键.针对此问题提出了DPA(deep-prediction based algorithm)和EDPA(EDF-based DPA)算法.算法考虑当前时间至替代部分通知时间之间的任务执行情况,通过构建预测表对待执行主部分的可执行性进行精确预测.当主部分不发生错误时算法根据预测表调度任务. DPA依照预测表中通知时间的先后顺序调度主部分,而EDPA则按照EDF算法调度预测表中的主部分.模拟结果表明,DPA和EDPA较目前同类算法可获得更多的主部分执行时间,降低CPU的消耗.当软件错误率较低、任务周期较短时,算法能够以较小的调度开销获得较高的调度性能.     

基于任务复制的处理器预分配算法

基于任务复制的调度算法比无任务复制的调度算法具有较好的性能．文章在分析了基于任务复制的几个典型算法(如TDS，OSA等算法)及其假设条件后，提出了以使调度长度最短作为主要目标、减少处理机数目作为次要目标的处理器预分配算法PPA．该算法对任务计算时间与任务间通信时间未做任何限制(即不考虑任务粒度)．通过与相关工作的比较可以看出：PPA算法在调度长度与处理器使用数目上均优于其它算法或与其它算法相当，同时，该算法具有与TDS，OSA相同的时间复杂度．这对嵌入式实时分布系统具有重要的意义。     

T–L plane-based real-time scheduling for homogeneous multiprocessors

We consider optimal real-time scheduling of periodic tasks on multiprocessors—i.e., satisfying all task deadlines, when the total utilization demand does not exceed the utilization capacity of the processors. We introduce a novel abstraction for reasoning about task execution behavior on multiprocessors, called T–L plane and present T–L plane-based real-time scheduling algorithms. We show that scheduling for multiprocessors can be viewed as scheduling on repeatedly occurring T–L planes, and feasibly scheduling on a single T–L plane results in an optimal schedule. Within a single T–L plane, we analytically show a sufficient condition to provide a feasible schedule. Based on these, we provide two examples of T–L plane-based real-time scheduling algorithms, including non-work-conserving and work-conserving approaches. Further, we establish that the algorithms have bounded overhead. Our simulation results validate our analysis of the algorithm overhead. In addition, we experimentally show that our approaches have a reduced number of task migrations among processors when compared with a previous algorithm.     

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.     

To Schedule or to Execute: Decision Support and Performance Implications

This paper addresses a fundamental trade-off in dynamic scheduling between the cost of scheduling and the quality of the resulting schedules. The time allocated to scheduling must be controlled explicitly, in order to obtain good-quality schedules in reasonable times. As task constraints are relaxed, the algorithms proposed in this paper increase scheduling complexity to optimize longer and obtain high-quality schedules. When task constraints are tightened, the algorithms adjust scheduling complexity to reduce the adverse effect of long scheduling times on the schedule quality. We show that taking into account the scheduling time is crucial for honoring the deadlines of scheduled tasks. We investigate the performance of our algorithms in two scheduling models: one that allows idle-time intervals to exist in the schedule and another that does not. The model with idle-time intervals has important implications for dynamic scheduling which are discussed in the paper. Experimental evaluation of the proposed algorithms shows that our algorithms outperform other candidate algorithms in several parameter configurations.     

Block transportation scheduling under delivery restriction in shipyard using meta-heuristic algorithms

Special vehicles called transporters are used to deliver heavy blocks from one plant to another in shipyards. Because of the limitation on the number of transporters, the scheduling of transporters is important for maintaining the overall production schedule of the blocks. This paper considers a scheduling problem of block transportation under a delivery restriction to determine when and by which transporter each block is delivered from its source plant to its destination plant. The objective of the problem is to minimize the penalty times that can cause delays in the overall block production schedule. A mathematical model for the optimal solution is derived, and two meta-heuristic algorithms based on a genetic algorithm (GA) and a self-evolution algorithm (SEA) are proposed. The performance of the algorithms is evaluated with several randomly generated experimental examples.     

CCTD：一种通信限制下的Fork-Join任务调度算法

现代并行系统的复杂调度问题可以转化为Fork-join图的任务调度问题.然而在实际计算环境中,两个处理节点之间的通信大多以独占方式进行,现有的大多数任务调度算法往往忽略了对通信信道独占性的考虑.提出了一种带通信限制的Fork-join图调度算法CCTD.该算法引入了实际环境中的通信独占性限制,同时保证了Fork-join图的基于复制的优化调度,而且尽可能地减少了对处理器占用.实验结果表明,CCTD算法是一种适应性强的、高效的Fork-join图调度算法.