新的分布式任务调度算法

详细对比了传统Min-Min算法的高效特性和Max-Min算法的负载平衡特性,结合Min-Min和Max -Min算法的优点,提出新的具有动态特性的启发式算法(Heuristic task scheduling algorithm based on Min-Min and Max-Min,H-MM),H-MM解决了Min-Min算法负载不平衡问题。实验表明,H- MM在充分保留Min-Min算法执行任务高效基础上实现了算法的动态平衡负载执行特性,得到了更好的任务调度执行效果。     

异构多核系统任务调度算法研究

为解决异构多核系统任务调度问题,提出一种混合静态调度算法——HSCGS (hybrid successor concerned genetic scheduling),该算法分为启发式算法和遗传算法2个阶段.第1阶段采用所提出的考虑后继节点的列表启发式调度算法(SCLS)产生一个近似最优的调度结果;第2阶段采用针对调度问题改进的遗传算法IGA (improved genetic algorithm),对第1阶段产生的调度结果进行优化.将SCLS与StarPU相结合,实现一种动态调度算法——DSCLS(dynamic successor concerned list scheduling),通过与StarPU上已有调度算法的对比实验表明了DSCLS算法在运行时间和系统吞吐量两方面的优势.     

异构集群下的任务调度算法研究

针对异构集群下高效节能的任务调度算法进行了研究, 提出了一种基于复制的任务调度算法, 在任务初始分配的基础上, 分别从能源感知和性能—能源平衡两个角度考虑任务的复制。建立了由计算和通信造成的能源消耗的数学模型, 并进行了大量的实验。实验结果表明, 与已有的BEATA算法相比, 该算法能明显地减少异构集群处理并行应用的调度长度和能耗。分析结果发现, 任务复制的方法在减少调度长度的同时会增加相应的能耗, 能同比优化调度长度和能耗的任务调度方法是今后的研究方向。     

一种新的异构网格任务调度算法

提出了一种基于免疫计算的异构网格任务调度算法。设计了异构网格独立任务调度问题的数学模型,给出了免疫调度算法的框架、基于实数编码的克隆变异算子和浓度抑制算子,并在仿真环境下进行了实验。实验结果表明,算法能有效地解决异构网格任务调度问题,具有较好的应用价值。     

嵌入式异构环境下任务调度算法的研究

高效的任务调度是提升系统性能的关键因素之一。讨论在任务异构和通信速度差异的Fork-join型嵌入式环境下,独立任务的调度问题,提出新的分配调度方案,选取负载最小的处理节点进行分配,实现节点间的负载均衡,且满足任务的响应时间和处理节点数目最小化的要求。基于方案,构造一个以任务的平均响应时间驱动的启发式算法:ARTDHA(Aver-age-Response-Time-Driven Heuristic Algorithm)。仿真实验表明,算法更符合复杂的嵌入式异构环境,能更好满足系统的时间特性、最小化资源的开销,同时任务的调度时间要优于FCFS(First Come First Serve)算法。     

高实时性异构多核处理器任务调度算法

在异构多核处理器条件下,Min-Min算法调度性能较好但在系统实时响应方面存在不足。最小空闲时间优先调度算法(LSF)、最早截止时间优先调度算法(EDF)和最大价值优先调度算法(HVF)虽然在系统任务调度响应实时性方面表现优异,但却不适用于异构多核处理器环境。为此,提出一种高实时性任务调度算法HRSA。在Min-Min调度算法的基础上融合LSF,EDF,HVF算法的调度策略,将任务能耗、任务完成价值和任务响应比相结合,在实现异构多核处理器任务动态调度的同时缩短系统对高实时性任务的响应时间。实验结果表明,相对于EDF算法和Min-Min算法,HRSA算法消耗单位能量所带来的价值较高,对高实时性任务处理的响应时间较短。     

异构系统中基于可用性的粒子群任务调度算法

任务调度技术是并行分布式系统中的关键技术之一,对系统的性能起着重要作用,但通常情况下大型系统的任务调度问题属于NP问题。而现代启发式生物进化算法是找出很多NP问题近似解的有效方法。本文将粒子群算法应用于基于可用性的网格系统调度中,提出了一种调度算法,对算法的性能进行了理论分析和模拟实验。结果表明:和最近文献中的基于可用性的调度算法SSAC相比,所提出的新算法在保证系统资源具有同样的可用性条件下,能够产生更好的调度长度。     

混合粒子群算法的异构多核处理器间任务调度

针对异构多核处理器间的任务调度问题,为了更好地发挥异构多核处理器间的平台优势,提出一种基于将有关联的且不在同一处理器上的任务进行复制的思想,从而使每个异构多核的处理器能独立执行任务,来减少不同处理器之间的通信开销,并且通过混合粒子群算法(HPSO)来调度异构多核处理器中的任务,避免由于当任意一个异构多核处理器由于任务分配过多而导致计算机不能及时且准确地得出结果.最后实验证明,对比传统的启发式分配方案和常见的遗传算法(GA),基于任务复制思想分配方案和混合粒子群算法(HPSO)具有更好的求解能力,并且可以提供执行时间更少的调度分配方案,具有较好的应用价值.     

异构分布系统的实时任务轮转式容错调度算法

建立了一个异构分布式系统实时调度模型,对异构分布式系统中的任务及不同处理机资源进行了形式化描述.结合基版本/副版本技术,给出了用于异构分布式系统的实时任务轮转式容错调度算法.实例分析表明,该算法有效提高了异构处理机环境下的资源利用率以及整体计算性能.     

混合EDA求解三阶段异构并行机装配集成调度问题

本文提出一种混合分布估计算法（hybrid estimation of distribution algorithm，HEDA）用于求解带载重约束的三阶段异构并行机集成调度问题（three-stage heterogeneous parallel machine integrated scheduling problem with capacitated constraint，THPMISP_CC），第一阶段为加工阶段，即带释放时间的多工序异构并行机调度问题；第二阶段为带载重约束的运输阶段，即多维背包优化调度问题；第三阶段为装配阶段.本文研究工件从加工、运输到装配三阶段的集成调度优化问题.首先，本文构建了THPMISP_CC的数学模型，其优化目标为三阶段整体最大完工时间（Makespan）；然后，提出的HEDA用于优化THPMISP_CC；最后，对算法运用于THPMISP_CC模型的结果进行分析和比较，验证模型的可行性及算法的有效性.     

A distributed self-scheduler for partially ordered tasks

Large scale scientific applications such as weather modelling and continuous simulation require the orders of magnitude performance improvement available with the new generation of parallel vector supercomputers such as the Floating Point Systems T Series. Many of these applications exhibit a high degree of parallelism, much of which can be expressed as computational tasks which are of varying size and degrees of dependence on one another, and can be partially ordered for execution. DeSPOT (A Distributed Self-Scheduler for Partially Ordered Tasks) is an algorithm for the dynamic distribution of such non-uniform tasks to achieve automatic load balancing on a distributed memory hypercube multiprocessor. This paper describes the DeSPOt algorithm and presents its performance characteristics of various test cases using result timings on the FPS T 20.     

Scheduling independent tasks on heterogeneous processors using heuristics and Column Pricing

Efficiently scheduling a set of independent tasks on a virtual supercomputer formed by many heterogeneous components has great practical importance, since such systems are commonly used nowadays. Scheduling efficiency can be seen as the problem of minimizing the overall execution time (makespan) of the set of tasks under question. This problem is known to be NP-hard and is currently addressed using heuristics, evolutionary algorithms and other optimization methods. In this paper, firstly, two novel fast executing heuristics, called LSufferage and TPB, are introduced. L(ist)Sufferage is based on the known heuristic Sufferage and can achieve in general better results than it for most of the cases. T(enacious)PB is also based on another heuristic (Penalty Based) and incorporates new ideas that significantly improve the quality of the resulted schedule. Secondly, a mathematical model of the problem is presented alongside with an associated approach based on the Linear Programming method of Column Pricing. This approach, which is called Column Pricing with Restarts (CPR), can be categorized as a hybrid mathematical programming and heuristic approach and is capable of solving in reasonable time problem instances of practically any size. Experiments show that CPR achieves superior results improving over published results on problem instances of various sizes. Moreover, hardware requirements of CPR are minimal.     

Scheduling of network tasks to minimize the consumed energy

In this paper, we consider the problem of scheduling network tasks that have to be performed in a given computer network. Each network task consists of the transmission of data files between two nodes of the network and has to be performed in an a priori known time window in such a way that the energy consumed by the network equipment during the transmission of all the considered tasks is minimized. The entire mathematical model of this scheduling problem is proposed. Some results of computational experiments are also presented to show energy savings achieved through implementation of the proposed model in the interdata center network.     

An efficient list-based task scheduling algorithm for heterogeneous distributed computing environment

The task scheduling in heterogeneous distributed computing systems plays a crucial role in reducing the makespan and maximizing resource utilization. The diverse nature of the devices in heterogeneous distributed computing systems intensifies the complexity of scheduling the tasks. To overcome this problem, a new list-based static task scheduling algorithm namely Deadline-Aware-Longest-Path-of-all-Predecessors (DA-LPP) is being proposed in this article. In the prioritization phase of the DA-LPP algorithm, the path length of the current task from all its predecessors at each level is computed and among them, the longest path length value is assigned as the rank of the task. This strategy emphasizes the tasks in the critical path. This well-optimized prioritization phase leads to an observable minimization in the makespan of the applications. In the processor selection phase, the DA-LPP algorithm implements the improved insertion-based policy which effectively utilizes the unoccupied leftover free time slots of the processors which improve resource utilization, further least computation cost allocation approach is followed to minimize the overall computation cost of the processors and parental prioritization policy is incorporated to further reduce the scheduling length. To demonstrate the robustness of the proposed algorithm, a synthetic graph generator is used in this experiment to generate a huge variety of graphs. Apart from the synthetic graphs, real-world application graphs like Montage, LIGO, Cybershake, and Epigenomic are also considered to grade the performance of the DA-LPP algorithm. Experimental results of the DA-LPP algorithm show improvement in performance in terms of scheduling length ratio, makespan reduction rate , and resource reduction rate when compared with other algorithms like DQWS, DUCO, DCO and EPRD. The results reveal that for 1000 task set with deadline equals to two times of the critical path, the scheduling length ratio of the DA-LPP algorithm is better than DQWS by 35%, DUCO by 23%, DCO by 26 %, and EPRD by 17%.     

一种基于代价抢占的混合可重构任务调度算法*

针对同时存在独立任务和相依性任务的混合可重构任务调度,提出了基于代价抢占的混合可重构任务实时调度算法。提出了相依性任务等价运行截止时刻的计算方法,使混合可重构任务按照配置截止时刻排队配置。针对相依性任务调度特点,分析得到了相依性任务集合调度失败的充分条件,提前判定和丢弃无法调度成功的相依性任务集合;通过有限预配置防止相依性任务无效占用可重构资源;通过基于代价抢占减少调度失败任务个数。仿真结果表明,该调度算法提高了任务调度成功率。     

带负载均衡的混合算法求解分布式异构作业车间调度问题

针对以最小化最大完工时间为目标的分布式异构作业车间调度问题(DHJSP), 本文提出了一种新的混合遗传禁忌搜索算法. 首先, 综合考虑工厂的工件总负载与最大机器负载, 提出了一种新的工厂负载表达方式. 其次, 针对DHJSP总工序数不定的特性, 提出以最小化最大工厂负载为目标快速确定初始工件分配方案, 并验证了方法的高效性. 然后, 新设计了两种考虑负载均衡的单工件转移邻域结构, 根据工序调度的结果对工件分配方案进行局部搜索. 最后, 因DHJSP缺少标准算例和相关算法, 在分布式同构作业车间调度问题(DJSP)上与现有算法进行对比, 所提算法在TA算例的480个问题上更新了420个问题的最优解, 其余60个问题取得了同等最优解. 在随机生成的3个不同规模的异构算例中, 所提算法也均取得了较好解, 验证了所提方法的优越性.     

分布异构环境下数据库访问技术的研究

详细分析了当前分布异构数据库访问技术的研究现状和发展趋势,结合Web Services的优势构造了一个基于Web Services的分布异构数据库访问系统,并阐述了系统的实现过程。该系统能够有效地支持分布式数据查询,数据源透明并且支持跨平台检索。     

学习驱动的分布式异构混合流水车间批量流能效调度优化

本文研究了分布式异构混合流水车间批量流能效调度问题, 其中每个工厂的加工效率不同, 工件可以分割成若干子批进入加工系统. 以最大完成时间和总能耗为优化目标, 建立了混合整数规划模型. 本文提出了一种学习驱动的多目标进化算法, 包括学习驱动的全局搜索和局部搜索. 引入Q学习作为学习引擎, 以种群和非支配解集的评价作为环境反馈信号, 通过不断的学习来动态指导搜索操作的选择; 基于问题特征, 设计了算法的状态集、动作集和奖励机制. Q学习的引入能够及时感知当前搜索的状态, 减少搜索操作的盲目性, 提高搜索的效率. 通过对仿真数据集的测试, 表明所提出算法能够有效地求解分布式异构混合流水车间批量流能效调度问题.