处理顺序约束的信息物理融合系统静态任务表调度算法

针对异构环境并行计算的静态任务调度问题,以最小化有向无环图 (Directed acyclic graph, DAG)的执行跨度为目标,改变HEFT (Heterogeneous earliest finish time)算法中任务上行权重的计算方法, 获得更加合理的任务顺序排列,提出了一种最早完成时间优先的表调度算法IHEFT (Improvement heterogeneous earliest finish time).该算法在计算任务的上行权重时, 分别计算该任务分配给不同资源的上行权重,取其最小值,比使用所有资源对该任务的平均处理时间进行计算的HEFT算法更为准确. 确定任务的处理顺序后采用最早完成时间越小越优先的策略将任务分配给最优资源,并使得任务的开始执行时间和结束时间满足DAG中有向边的通讯时间约束.通过使用部分文献中的算例数据以及随机生成满足一定结构要求的DAG进行算法测试,将IHEFT与HEFT, CPOP (Critical-path-on-a-processor)和LDCP (Longest dynamic critical path)进行了比较,结果显示IHEFT算法更有效,而且时间复杂度较低.     

异构计算环境下基于优先队列划分的调度算法

人工智能的飞速发展对高性能计算提出了更高的要求,异构计算环境下任务调度问题一直是高性能计算中的关键问题.本文提出一种基于优先队列划分的调度算法(PQDSA),该算法根据DAG(有向无循环图)任务集的入口节点数量确定优先队列数,通过任务的通信开销和计算开销划分任务队列,进而将关键节点任务分配给合适的队列,以产生效果较佳的任务调度队列,从而提高任务间的并行性,降低任务集的完工时间.与此同时,进一步基于插入策略将任务调度到处理器上,使任务调度更加高效地执行.PQDSA算法可以减少任务间的时间消耗,提高处理器的调度效率.通过与两个经典算法的性能对比,实验结果表明本文提出的PQDSA算法在任务完工时间和调度效率方面都要明显优于对比的算法.     

基于HEFT和CPOP的相关任务表调度算法

DAG任务调度是当前研究的热点,DAG任务模型中任务的调度顺序一方面会影响用户服务满意质量,另一方面也会影响云服务资源的利用率,高效的任务调度算法能够使多核处理器的资源分配和并行计算能力更强.表调度算法HEFT算法以及CPOP算法在相关任务调度中存在效率较低等问题.本文基于HEFT算法和CPOP算法,提出了一种相关任务调度模型和相关任务调度算法IHEFT算法,对任务排序和任务调度两个方面进行改进.任务排序阶段,以任务的方差以及平均通信代价作为排序的依据;任务调度阶段,对满足任务复制条件的结点进行任务复制.实验证明,IHEFT算法在任务调度跨度、任务调度平均等待时间以及平均Slack值方面均优于HEFT算法和CPOP算法.     

Iterative list scheduling for heterogeneous computing

Optimal scheduling of parallel applications on distributed computing systems represented by directed acyclic graph (DAG) is NP-complete in the general case. List scheduling is a very popular heuristic method for DAG-based scheduling. However, it is more suited to homogenous distributed computing systems. This paper presents an iterative list scheduling algorithm to deal with scheduling on heterogeneous computing systems. The main idea in this iterative scheduling algorithm is to improve the quality of the schedule in an iterative manner using results from previous iterations. The algorithm first uses the heterogeneous earliest-finish-time (HEFT) algorithm to find an initial schedule and iteratively improves it. Hence the algorithm can potentially produce shorter schedule length. The simulation results show that in the majority of the cases, there is significant improvement to the initial schedule. The algorithm is also found to perform best when the tasks to processors ratio is large.     

一种基于两级DAG模型的MapReduce工作流异构调度算法

MapReduce编程模型被广泛应用于大数据处理平台,而一个有效的任务调度算法对模型的运行效率至关重要。将MapReduce工作流的Map和Reduce阶段分别拆解为若干个有先后序限定关系的作业,每个作业再拆解为多个任务。之后基于计算集群的可用资源和任务异构性,构建面向作业和任务的2级有向无环图(DAG)模型,同时提出基于2级优先级排序的异构调度算法2-MRHS。算法的第1阶段进行优先级排序,即对作业和任务分别进行优先权值计算,再汇总得到任务的调度队列;第2阶段进行任务分配,即基于最快完成时间将每个任务所包含的数据块子任务分配给最适合的计算结点。采用大批量随机生成的DAG模型进行实验,结果表明与其他相关算法相比,本文算法有更短的调度长度(makespan)且更加稳定。     

基于DAG图的自适应代码划分优化算法

并行编译的两大工作是程序代码划分和调度。对于调度问题,目前已有大量的解决方案,但是针对代码划分提取并行性的研究工作却非常少。该文提出了通过合并结点来划分DAG图的新的划分算法。实例分析证明,该算法是一种有效的、低复杂度的自适应代码划分解决方案,并且适用于异构计算的任务图划分。     

Degree-of-Node Task Scheduling of Fine-Grained Parallel Programs on Heterogeneous Systems

Processor specialization has become the development trend of modern processor industry. It is quite possible that this will still be the main-stream in the next decades of semiconductor era. As the diversity of heterogeneous systems grows, organizing computation efficiently on systems with multiple kinds of heterogeneous processors is a challenging problem and will be a normality. In this paper, we analyze some state-of-the-art task scheduling algorithms of heterogeneous computing systems and propose a Degree of Node First (DONF) algorithm for task scheduling of fine-grained parallel programs on heterogeneous systems. The major innovations of DONF include:1) simplifying task priority calculation for directed acyclic graph (DAG) based fine-grained parallel programs which not only reduces the complexity of task selection but also enables the algorithm to solve the scheduling problem for dynamic DAGs; 2) building a novel communication model in the processor selection phase that makes the task scheduling much more efficient. They are achieved by exploring finegrained parallelism via a dataflow program execution model, and validated through experimental results with a selected set of benchmarks. The results on synthesized and real-world application DAGs show a very good performance. The proposed DONF algorithm significantly outperforms all the evaluated state-of-the-art heuristic algorithms in terms of scheduling length ratio (SLR) and efficiency.     

一种基于负载均衡性的网格任务调度算法

针对二分图匹配算法在任务之间存在时序关系时无法进行有效调度以及EFT算法没有充分考虑各处理机性能及网络通信状况的问题,提出基于二分图匹配的改进ETF算法。该算法综合考虑任务之间的时序关系、处理机的性能、处理机之间的通信情况及已处理任务的调度情况,利用二分图最佳匹配思想对局部任务进行调度。实验表明该算法具有较小的调度长度和较好的负载均衡性。     

A heterogeneous computing system for data mining workflows in multi-agent environments

Abstract: The computing-intensive data mining (DM) process calls for the support of a heterogeneous computing system, which consists of multiple computers with different configurations connected by a high-speed large-area network for increased computational power and resources. The DM process can be described as a multi-phase pipeline process, and in each phase there could be many optional methods. This makes the workflow for DM very complex and it can be modeled only by a directed acyclic graph (DAG). A heterogeneous computing system needs an effective and efficient scheduling framework, which orchestrates all the computing hardware to perform multiple competitive DM workflows. Motivated by the need for a practical solution of the scheduling problem for the DM workflow, this paper proposes a dynamic DAG scheduling algorithm according to the characteristics of an execution time estimation model for DM jobs. Based on an approximate estimation of job execution time, this algorithm first maps DM jobs to machines in a decentralized and diligent (defined in this paper) manner. Then the performance of this initial mapping can be improved through job migrations when necessary. The scheduling heuristic used considers the factors of both the minimal completion time criterion and the critical path in a DAG. We implement this system in an established multi-agent system environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. The system evaluation and its usage in oil well logging analysis are also discussed.     

Stochastic DAG scheduling using a Monte Carlo approach

In heterogeneous computing systems, there is a need for solutions that can cope with the unavoidable uncertainty in individual task execution times, when scheduling DAGs. When such uncertainties occur, static DAG scheduling approaches may suffer, and some rescheduling may be necessary. Assuming that the uncertainty in task execution times is modelled in a stochastic manner, we may be able to use this information to improve static DAG scheduling considerably. In this paper, a novel DAG scheduling approach is proposed to solve this stochastic scheduling problem, based on a Monte Carlo method. The approach is built on the top of a classic static DAG scheduling heuristic and evaluated through extensive simulation. Empirical results show that a significant improvement of average application performance can be achieved by the proposed approach at a reasonable execution time cost.     

A scheduling algorithm for applications in a cloud computing system with communication changes

This paper proposes a scheduling algorithm to solve the problem of task scheduling in a cloud computing system with time‐varying communication conditions. This algorithm converts the scheduling problem with communication changes into a directed acyclic graph (DAG) scheduling problem for existing fuzzy communication task nodes, that is, the scheduling problem for a communication‐change DAG (CC‐DAG). The CC‐DAG contains both computation task nodes and communication task nodes. First, this paper proposes a weighted time‐series network bandwidth model to solve the indefinite processing time (cost) problem for a fuzzy communication task node. This model can accurately predict the processing time of a fuzzy communication task node. Second, to address the scheduling order problem for the computation task nodes, a dynamic pre‐scheduling search strategy (DPSS) is proposed. This strategy computes the essential paths for the pre‐scheduling of the computation task nodes based on the actual computation costs (times) of the computation task nodes and the predicted processing costs (times) of the fuzzy communication task nodes during the scheduling process. The computation task node with the longest essential path is scheduled first because its completion time directly influences the completion time of the task graph. Finally, we demonstrate the proposed algorithm via simulation experiments. The experimental results show that the proposed DPSS produced remarkable performance improvement rate on the total execution time that ranges between 11.5% and 21.2%. In view of the experimental results, the proposed algorithm provides better quality scheduling solution that is suitable for scientific application task execution in the cloud computing environment than HEFT, PEFT, and CEFT algorithms.     

Reliability-aware scheduling strategy for heterogeneous distributed computing systems

Heterogeneous computing systems are promising computing platforms, since single parallel architecture based systems may not be sufficient to exploit the available parallelism with the running applications. In some cases, heterogeneous distributed computing (HDC) systems can achieve higher performance with lower cost than single-machine supersystems. However, in HDC systems, processors and networks are not failure free and any kind of failure may be critical to the running applications. One way of dealing with such failures is to employ a reliable scheduling algorithm. Unfortunately, most existing scheduling algorithms for precedence constrained tasks in HDC systems do not adequately consider reliability requirements of inter-dependent tasks. In this paper, we design a reliability-driven scheduling architecture that can effectively measure system reliability, based on an optimal reliability communication path search algorithm, and then we introduce reliability priority rank (RRank) to estimate the task’s priority by considering reliability overheads. Furthermore, based on directed acyclic graph (DAG) we propose a reliability-aware scheduling algorithm for precedence constrained tasks, which can achieve high quality of reliability for applications. The comparison studies, based on both randomly generated graphs and the graphs of some real applications, show that our scheduling algorithm outperforms the existing scheduling algorithms in terms of makespan, scheduling length ratio, and reliability. At the same time, the improvement gained by our algorithm increases as the data communication among tasks increases.     

FGFS: Feature Guided Frontier Scheduling for SIMT DAGs

In the past decade, heterogeneous multicore architectures with support for Single Instruction Multiple Thread (SIMT) style computing have become the standard platform of choice for scheduling HPC applications. Here, applications are typically modelled as a set of data-parallel tasks with dependencies represented in the form of a directed acyclic graph (DAG). The relevant execution time information for each constituent task in the DAG is known beforehand and is leveraged by scheduling algorithms (List or Cluster based) to ascertain near-optimal schedules at runtime. However, given an online setting, where applications are submitted by multiple users and the types of applications are not restrictive, the chances of knowing execution time information for every program are highly unlikely. In this context, we propose a class of intelligent algorithms for heterogeneous CPU-GPU platforms that leverage static analysis-assisted machine learning techniques for deciding how device assignments should be made at runtime, thus bypassing the requirement for expensive offline profiling passes. We formalize relevant task-level ranking metrics and discuss how existing scheduling techniques can be adapted for our proposed class of algorithms. We also devise an online cluster scheduling algorithm that supports dynamic task arrival by determining in any given scheduling epoch, mapping decisions for a subset of tasks in a DAG. We perform a detailed comparative analysis between our proposed cluster and list scheduling heuristics via extensive simulation experiments using a variety of heterogeneous multicore platform configurations and observe performance speedups in the range of 1.1–1.5× for cluster scheduling over that of list scheduling.

     

基于通信和计算开销的网格任务调度算法的研究

在计算密集型的异构网格环境中,有效的任务调度是一个关键的问题,这是一个完全NP问题,针对这一问题提出了一种基于通信和计算开销的启发式网格任务调度算法,这一算法考虑了不同的节点计算能力、任务大小和网络带宽,最后给出了相应的实验及相关算法的比较结果,表明了该算法对于异构环境具有更优的性能。     

基于选择性复制前驱任务的DAG调度算法

针对异构环境下相关任务的静态调度问题,以最小化调度长度为主要目标,结合表调度与基于复制的调度思想提出了选择性任务复制调度算法.在任务调度过程中,利用处理器的空闲时间,通过有选择地复制能提前当前任务开始执行时间的父任务来减少任务之间信息传递的通信延迟,有利于后续任务的及时调度,从而缩短整个任务图的并行完成时间.实验结果表明,文中算法在通信量比较大的情况下在时间上优于复杂度相同的HEFT,HNDP及DDS算法,且随着任务图中通信时间/计算时间比值的增加,其优越性也越来越明显.     

A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system

Effective task scheduling, which is essential for achieving high performance in a heterogeneous multiprocessor system, remains a challenging problem despite extensive studies. In this article, a heuristic-based hybrid genetic-variable neighborhood search algorithm is proposed for the minimization of makespan in the heterogeneous multiprocessor scheduling problem. The proposed algorithm distinguishes itself from many existing genetic algorithm (GA) approaches in three aspects. First, it incorporates GA with the variable neighborhood search (VNS) algorithm, a local search metaheuristic, to exploit the intrinsic structure of the solutions for guiding the exploration process of GA. Second, two novel neighborhood structures are proposed, in which problem-specific knowledge concerned with load balancing and communication reduction is utilized respectively, to improve both the search quality and efficiency of VNS. Third, the proposed algorithm restricts the use of GA to evolve the task-processor mapping solutions, while taking advantage of an upward-ranking heuristic mostly used by traditional list scheduling approaches to determine the task sequence assignment in each processor. Empirical results on benchmark task graphs of several well-known parallel applications, which have been validated by the use of non-parametric statistical tests, show that the proposed algorithm significantly outperforms several related algorithms in terms of the schedule quality. Further experiments are carried out to reveal that the proposed algorithm is able to maintain high performance within a wide range of parameter settings.     

分布式系统下的DAG任务调度研究综述

近年来随着网格、云计算工作流等分布式计算技术的发展,关于DAG(有向无环图)模型任务在分布式系统环境下的调度问题逐渐成为备受关注的研究热点。根据最新研究进展,对分布式系统下的DAG任务调度问题和有关技术进行了研究与讨论,主要包括四个方面:系统地描述了分布式系统和异构分布式系统的有关概念,异构分布式系统下的DAG任务调度问题、调度模型及其典型应用;对现有分布式系统下DAG任务调度的研究按照不同的方式进行了分类;探讨了多DAG共享异构分布式资源调度的研究现状;讨论了目前多DAG共享异构分布式资源调度研究存在的问题和未来可能的研究方向。     

基于最小化传输和完成时间的多DAG调度

针对云计算环境下多个有向无环图(DAG)工作流的调度问题,提出一种基于最小化数据传输时间和任务完成时间(LTCT)的算法,用于处理具有相同优先级的多个DAG工作流之间的调度问题。在多个DAG优先级各不相同时的情况下,给出多优先级多DAG的混合调度算法。实验结果表明,LTCT算法较E-Fairness算法在保证多DAG调度公平性的基础上,能避免额外的数据传输开销,有利于缩短整个工作流的执行Makespan,提高资源的利用率。     

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。     

一种用于异构环境中任务调度的高效算法

在异构计算环境中,有效的任务调度对于获得高性能是十分重要的。现在虽然已经有许多异构处理器调度算法,但它们或者不具有良好的效果,或者算法代价太高。提出了一种新的基于表的调度算法APS。APS利用有向无环图来计算任务优先级,并采用基于调度的策略分配任务到不同处理器,以获得任务最少完工时间。将APS和LMT,HEFT,CPOP算法做比较之后得出：在大多数情况下APS算法都能获得更好性能。