基于混合粒子群算法的网格任务调度

减少分布式程序的执行时间是网格调度系统需要解决的重要问题。因分布式程序常建模为DAG图,故该问题又称异构DAG调度问题。在研究网格环境下的任务调度的基础上,提出了一种用于解决DAG任务调度问题的通用混合粒子群优化算法(Common Hybrid Particle Swarm Optimization),简称为CHPSO。该算法将问题的解(粒子)表示为任务的调度优先权向量,采用混合粒子群优化算法探索解空间。实验结果表明,在求解不含孤立点的单个DAG调度问题时,该算法所得解的调度长度仅为HEFT的90%～92%,求解质量与PSGA相当;在多张DAG图(含孤立节点)并发执行的网格环境中,该算法的调度性能明显优于PSGA及文中列出的其它演化计算方法。     

多构造蚁群优化求解置换流水车间调度问题

针对置换流水车间调度问题,提出了一种多构造蚁群优化求解算法。在该算法中,蚁群采用两种方式构造解,分别是基于NEH(Nawaz-Enscore-Ham,NEH)启发式算法和Rajendran启发式算法,并根据解的质量,自适应地调整两种构造方式在蚁群中所占的比例。对置换流水车间调度问题的基准问题测试表明,提出的算法是有效的。     

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。     

异构多核计算系统的Codelet任务调度策略

Codelet数据流计算模型在处理大规模并行计算任务时效果显著,但该模型目前缺少在异构多核环境中的任务调度策略。因此,提出了一种在异构多核环境下基于蚁群算法的Codelet任务调度策略。该调度策略将启发式算法与蚁群算法相融合,在发挥各自优势的同时克服了启发式算法不能得出最优解的缺陷以及蚁群算法初始信息匮乏的问题。实验结果表明,智能蚁群任务调度策略相比Codelet运行时系统中原生的动态调度和静态调度策略具有更高的执行效率。     

置换流水车间调度的蚁群优化算法

提出了一种求解置换流水车间调度的蚁群优化算法。该算法的要点是结合了NEH启发式算法和蚁群优化方法。理论论证和对置换流水车间调度问题的基准测试表明了该算法的有效性。     

异构分布式环境下多DAG工作流的混合调度策略

关于多个DAG工作流在异构分布式环境下调度的研究近来有了新的进展,也解决了一些问题,但现阶段还没有考虑和解决根据不同类型DAG的需求按优先级进行分类,以及对不同时间到达的多个不同优先级DAG进行调度的问题.为解决这些问题,针对各用户对DAG工作流的QoS需求的不同,在对不同用户的DAG工作流进行优先级划分的基础上,首先提出了一种新的调度模型,并改进了已有的公平调度算法,解决在不同时间上被提交的具有相同优先级的多个DAG工作流之间调度的公平性问题.为了提高资源利用率和高优先级DAG尽可能小地受低优先级DAG的影响,又提出了一种适用于多个不同优先级DAG之间调度的Backfill算法.在新的系统模型和这两种算法的基础上,提出了一种混合调度策略.实验结果表明,这种混合调策略能够兼顾不同时间到达的多个不同类型DAG调度需求和资源利用率的改善.另外,通过实验发现了关于两个DAG调度所特有的"拖尾"规律,具有进一步研究和应用的价值.     

改进的蚁群算法求解置换流水车间调度问题

针对蚂蚁算法在求解置换流水车间调度问题时易陷入局部最优以及计算时间较长的缺点,对最大最小蚂蚁系统(MMAS)进行了改进。在该算法中,采用NEH启发式算法提高初始解质量,并通过自适应的调节策略进一步提高蚁群算法的搜索能力。运用提出的混合算法求解Taillard基准测试集,并将测试结果与其他算法进行比较,验证了该调度算法的有效性。     

基于任务复制的分簇与调度算法

针对并行与分布式系统中相关任务的静态调度问题,以最小化调度长度为主要目标,以减少资源数为次要目标,对待复制的重要祖先集定义了新的选择策略,提出了基于任务复制的动态关键前驱调度算法.改进了粒度的定义,证明了对任意DAG,算法有优于前人的性能下界.实验结果优于典型任务复制算法,特别是对经典EZ算例的解(调度长度为8)好于前人认为的理论最优解(调度长度为8.5),并证明了新的解为最优解.定义了DAG的补图,讨论了不允许任务复制时树型DAG的2-优度算法.     

有期限约束的多DAG共享资源的调度及公平费用优化方法

随着网格和云计算工作流技术的发展,近来关于多DAG(Directed Acyclic Graph)共享资源调度的研究取得了一些进展,然而,关于具有最晚完成期限约束的多DAG共享一组有限异构资源的调度及其费用最低化等问题还有待进一步研究和解决.针对这些问题,文中首先提出了衡量DAG期限紧急水平的"相对严格程度"的新方法,并在此基础上提出了基于相对严格程度的调度算法MDRS(Scheduling for Multi-DAGs with Deadline based on Relative Stritness).该算法不仅能够合理处理多个DAG之间调度的紧急水平关系,也能对由于DAG期限过于严格而可能产生的"过饱和"情况进行探测和处理.一旦遇到"过饱和"情况,则采用"堆栈"与"调度回溯"相结合的机制尽可能少地丢弃其中的DAG,从而达到DAG吞吐量最大化调度目标.在MDRS算法的基础上,为了满足各DAG期限内完成约束条件,并尽可能公平地降低多个DAG执行的费用,又提出了基于单位相对严格程度变化量的费用降低率最大化方法的费用优化算法CDVRS(Cost Decrease based on Variance of the Relative Strictness).实验表明:这些方法及算法能够达到较好的性能.     

求解带硬时间窗车辆路径问题的自适应蚁群算法

蚁群算法具有较强的鲁棒性和优良的分布式计算机制.研究重点是对现有的求解带硬时间窗的车辆路径问题VRP-H(Vehicle Routing Problem with Hard Time Windows)的蚁群算法作出更好的改进,使得算法的计算效率更高且得到的解更优,提出了蚁群算法的改进算法-改进的自适应蚁群算法.该算法先用自适应蚁群算法对VRP-H求得一个可行解,再利用多种改善方法对初始解进一步优化,从而得到最优解.测试时选用Solomon提出的题库,结果表明该算法能够有效地求解VRP-H.     

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.     

一个基于DAG图的指令调度优化算法

指令调度是优化编译技术中一项关键技术,对于VLIW体系结构的CPU,指令调度显得尤为重要。指令调度是在保证语义正确的前提下,改变指令的执行顺序,减少流水线中的空闲周期,从而提高CPU性能的一种优化方法。文章着重分析了优化编译中的指令调度问题,提出了一个指令调度算法和DAG图的一种化简方法,证明了算法的正确性,分析了算法的效率,比较了生成的新指令序列和最优的指令序列总的执行时间的差别。同时,针对目前流行的编译器GCC的指令调度算法中存在的问题,提出了一个较好的解决途径。     

A novel hybrid PSO–GA meta-heuristic for scheduling of DAG with communication on multiprocessor systems

This work presents a novel hybrid meta-heuristic that combines particle swarm optimization and genetic algorithm (PSO–GA) for the job/tasks in the form of directed acyclic graph (DAG) exhibiting inter-task communication. The proposed meta-heuristic starts with PSO and enters into GA when local best result from PSO is obtained. Thus, the proposed PSO–GA meta-heuristic is different than other such hybrid meta-heuristics as it aims at improving the solution obtained by PSO using GA. In the proposed meta-heuristic, PSO is used to provide diversification while GA is used to provide intensification. The PSO–GA is tested for task scheduling on two standard well-known linear algebra problems: LU decomposition and Gauss–Jordan elimination. It is also compared with other states-of-the-art heuristics for known solutions. Furthermore, its effectiveness is evaluated on few large sizes of random task graphs. Comparative study of the proposed PSO-GA with other heuristics depicts that the PSO–GA performs quite effectively for multiprocessor DAG scheduling problem.     

成本约束的网格工作流时间优化方法

针对成本约束有向无环图DAG(directed acyclic graph)表示的网格工作流完工时间最小化问题,提出两个基于优先级规则的迭代启发算法.算法利用并行活动特征定义正向分层和逆向分层两个概念,将其分别引入最大收益规则MP(maximum profit),得到正分层最大收益规则MPTL(maximum profit with top level)和逆分层最大收益规则MPBL(maximum profit with bottom level).两规则每次迭代尽量以完工时间的最小增加换取总费用的最大降低,逐步将分层初始解构造为满足成本约束的可行解.模拟结果表明,两规则在获得较少迭代次数和运行时间的同时,能显著改进MP规则的平均性能,且MPBL优于MPTL.     

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.     

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。     

面向并行应用程序的DAG模式复用技术

在并行应用程序设计中可以用有向无环图(DAG)表示子任务之间的依赖关系,从而实现子任务的调度。为此,针对DAG数据驱动模型中的DAG模式复用问题,提出插件式DAG模式图库。将DAG模式图模块和运行时系统模块分离,增强模块的复用性和扩展性。在Easy PDP系统中实现并扩展该模式图库。以棋盘问题为例,证明该技术可降低扩展系统所需的代价。     

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.