一种高效频繁子图挖掘算法

由于在频繁项集和频繁序列上取得的成功,数据挖掘技术正在着手解决结构化模式挖掘问题--频繁子图挖掘.诸如化学、生物学、计算机网络和WWW等应用技术都需要挖掘此类模式.提出了一种频繁子图挖掘的新算法.该算法通过对频繁子树的扩展,避免了图挖掘过程中高代价的计算过程.目前最好的频繁子图挖掘算法的时间复杂性是O(n³·2ⁿ),其中,n是图集中的频繁边数.提出算法的时间复杂性是O〔2ⁿ·n^2.5／logn〕,性能提高了O(√n·logn)倍.实验结果也证实了这一理论分析.     

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。     

基于周期虚拟缩减的实时任务调度和分析方法

针对航天器等安全关键系统中实时任务调度和可调度性分析的实际问题, 提出基于任务周期虚拟缩减的可调度性判定方法, 构建SHT (strong-hard task)任务模型对强硬实时任务进行精确描述, 并根据任务时间特性分配优先级. 虚拟化所有强实时任务为一个硬实时任务, 对此硬实时任务周期虚拟缩减并计算出其最差虚拟执行时间, 然后按RMS可调度性判定公式判定. 给出了判定方法的严格证明, 可对包含n个SHT任务的任务集进行快速可调度性判定, 此算法时间复杂度仅为O(n²). 在我国空间站计算机进行了对比验证, 实验表明判定效率优于现有可调度性判定方法, 平均运行时间开销降低了41.8%, 可调度率提高了5.7%.     

多跳中继无线网络资源复用的建模及算法设计

建立了中继网络资源复用问题的图论模型,依据该模型设计了自适应资源复用调度算法ARRS(adaptive resource reuse scheduling),以提高中继网络资源利用率.由于ARRS算法的核心步骤涉及顶加权图G(V,E,W)的染色,是NP-hard问题,为此给出了求解最优资源复用约束的顶加权图染色的近似算法ARRS_Greedy.该算法被证明具有时间复杂度O(|V|²),近似比为?(Δ+1)/2?(Δ表示图G顶点度数的最大值).该近似比是紧的.仿真分析验证了近似算法ARRS_Greedy在应用中取得了与最优解非常接近的性能,证明了ARRS算法能够动态适应网络状态变化,因而与现有算法相比大幅度提高了系统容量.     

采用配置完成优先策略的可重构任务调度算法

如何隐藏和减少配置时间是相依性可重构任务调度的关键问题.提出一种采用配置完成优先策略的相依性可重构任务调度算法,通过基于预配置优先级的列表调度算法,实现将后续任务的配置时间隐藏于前驱任务的运行时间中,并采用基于配置完成优先策略的配置重用机制,减少了任务调度后的配置过程,从而在总体上缩短了相依性任务集合的运行时间.仿真结果表明,该调度算法能有效避免调度死锁,并可减少相依性可重构任务的整体运行时间.     

面向区分服务的可重构任务在线调度算法

现有的先来先服务和预约调度算法中可重构任务调度顺序取决于该任务到达次序,无法体现不同任务的优先级差异以及前后任务的时间关联性,为此提出一种基于预约抢占的可重构任务在线调度算法.通过区分不同任务的优先级属性,并引入任务紧迫度的概念,实现差异化任务调度;对已预约任务采用预约失效机制,使高优先级或同优先级中紧迫度较大的新任务优先调度,从而实现对已预约任务队列进行抢占式调度.实验结果表明,该算法能有效地提高任务的整体调度成功率,并可优先保证高优先级任务的调度成功率.     

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.     

网络流量的有效测量方法分析

把网络流量的有效测量问题抽象为求给定图G=(V,E)的最小弱顶点覆盖集的问题.给出了一个求最小弱顶点覆盖集的近似算法,并证明了该算法具有比界2(lnd+1),其中d是图G中顶点的最大度.指出了该算法的时间复杂性为O(|V|²).     

一种基于分簇复制的DAG任务图调度算法

并行任务调度是影响机群计算效率的关键因素之一，机群环境DAG(Directed Acyclic Graph)任务图调度是一个NP完全问题，只能寻求启发式算法。已有的研究中，图解重构算法在允许任务复制的条件下，通过对DAG图递归分解与子图重构，初步实现了一个可行的调度方案。该文在此基础上，提出了以调度长度增量为依据的任务复制策略，利用该策略调整受制约节点的同簇前驱，解决了任务簇间的时间制约问题，缩短了调度长度；通过合理地选择任务簇进行合并，增大任务簇的粒度，提高了处理器的利用率。提出的以任务簇扩展-合并为特征、以分簇复制为手段的DAG图调度算法，改进和拓展了图解重构方法。实例分析表明本算法复杂度与TDS (Task Duplication Scheduling)相同，但性能更优。     

云计算环境下基于路径优先级的任务调度算法

为了最小化云计算系统的任务调度长度,结合表启发式调度技术和任务复制的思想提出基于路径优先权的任务调度算法.采用一种新方法计算DAG图中任务节点及边的权值,从最高优先权的路径开始依次选择任务进行调度,并通过有选择性地复制任务节点的父任务来减少任务间信息传送的时间花费,最后将任务安排到使其执行完成时间最早的虚拟机上.通过随机产生的DAG图与HEFT算法进行对比分析,实验结果表明了该算法能获得较短的调度长度.     

同构计算环境中一种快速有效的静态任务调度算法

快速有效的调度任务是多处理器计算环境中的一个关键问题．目前任务调度算法中刻画任务依赖关系最流行的模型是DAG,在以前的文献中，提出了一种新的更实际、更普遍的TTIG模型及其相应的MATE算法(基于同构计算环境)．延伸了TTIG模型，并提出基于同构系统的新的算法及两种启发式方法(GBHA1和GBHA2)．GBHA以组的形式尽量消除图中回路，因而能获得任务图的全局信息，具有更好的调度性能．在模拟实验中，将此算法与MATE和其他同构环境中基于DAG的有效调度算法，在不同测试条件下进行了比较，结果显示GBHA在性能上明显优于MATE，与基于DAG模型的调度算法比较而言，在性能方面各有千秋，但在算法时间复杂度方面具有显著的优势．     

Scheduling DAG-based applications in multicluster environments with background workload using task duplication

Grids or multicluster computing environments are becoming increasingly popular to both scientific and commercial applications. Process scheduling remains a central issue to be effectively resolved in order to exploit the full potential that the grid or multicluster environment can offer. We use a directed acyclic graph (DAG) to model a process or an application where the nodes of the DAG represent the tasks of the process. Prior to the execution of a process in a multicluster environment, the tasks are required to be mapped onto the clusters. In this article, it is shown that the algorithm developed by He et al. [L. He, S.A. Jarvis, D.P. Spooner, D. Bacigalupo, G. Tan, and G.R. Nudd, Mapping DAG-based applications to multiclusters with background workload, Proceedings of the 2005 IEEE International Symposium on Cluster Computing and the Grid, Cardiff, 2005, pp 855–862.] for the multicluster DAG mapping problem can be significantly improved by incorporating the task duplication strategy. The proposed process scheduling algorithm has a time complexity O(| V|²(r+d+1)), where |V| represents the number of tasks; r, the number of clusters; and d, the maximum in-degree of tasks.     

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.     

Benchmarking and Comparison of the Task Graph Scheduling Algorithms

The problem of scheduling a parallel program represented by a weighted directed acyclic graph (DAG) to a set of homogeneous processors for minimizing the completion time of the program has been extensively studied. The NP-completeness of the problem has stimulated researchers to propose a myriad of heuristic algorithms. While most of these algorithms are reported to be efficient, it is not clear how they compare against each other. A meaningful performance evaluation and comparison of these algorithms is a complex task and it must take into account a number of issues. First, most scheduling algorithms are based upon diverse assumptions, making the performance comparison rather meaningless. Second, there does not exist a standard set of benchmarks to examine these algorithms. Third, most algorithms are evaluated using small problem sizes, and, therefore, their scalability is unknown. In this paper, we first provide a taxonomy for classifying various algorithms into distinct categories according to their assumptions and functionalities. We then propose a set of benchmarks that are based on diverse structures and are not biased toward a particular scheduling technique. We have implemented 15 scheduling algorithms and compared them on a common platform by using the proposed benchmarks, as well as by varying important problem parameters. We interpret the results based upon the design philosophies and principles behind these algorithms, drawing inferences why some algorithms perform better than others. We also propose a performance measure called scheduling scalability (SS) that captures the collective effectiveness of a scheduling algorithm in terms of its solution quality, the number of processors used, and the running time.     

Enhancing list scheduling heuristics for dependent job scheduling in grid computing environments

Job scheduling plays a critical role in resource utilisation in a grid computing environment. The heterogeneity of grid resources adds some challenges to the work of job scheduling especially when jobs have dependencies which can be represented as Direct Acyclic Graphs (DAGs). Heuristics have been developed for job scheduling optimisation. This paper presents six heuristic enhancements—MMSTFT for minimising both makespan and task finish time, levelU for upward DAG levelling, TMWD for matching tasks with data, Slack for prioritising task scheduling based on slack time, LSlack for levelling the Slack heuristic, and NLPETS for non-levelling of performance effective task scheduling (PETS). The performance of LSlack is amongst the best heuristics evaluated (with BL and LMT). Additionally, heuristic enhancements MMSTS and TMWD can significantly improve the makespan of generated schedules. To facilitate performance evaluation, a DAG simulator is implemented which provides a set of tools for DAG job configuration, execution and monitoring. The components of the DAG simulator are also presented in this paper.     

一种基于任务全局迁移的静态调度算法

任务调度是分布实时系统中的一个关键问题.TDS等典型算法在优化条件下可得到该问题调度长度上的最优解.但是TDS等算法在节点分配时存在节点选择范围和节点执行时间范围的局限,无法最小化算法所需处理器数目.任务全局迁移调度算法GTT（global task-transferring）在保证调度长度最优的前提下,从全局范围内选择并调度任务节点,有效利用了处理器,可最小化调度所需处理器数目.优化条件下对各种算法的调度实验表明,GTT算法在加速比和效率上比TDS等同类算法有显著提高.GTT算法的时间复杂度是O（d ｜ V｜^ 2）.这里｜V｜是DAG图中的节点数,d是图中各节点入度或出度的最大值.     

网格环境下的静态启发式任务调度算法

针对网格环境中应用程序常为复杂的计算密集型的并行分布式应用程序,提出了一个新的基于复制和插入的启发式任务调度算法(duplication-and-insertion-based scheduling,DIBS),可以同时执行多个应用程序,利用决定路径对任务进行排序,缩短了应用程序总的执行时间,该算法还平衡了处理器间的负载.实验结果表明,该算法更加符合网格的复杂环境,能够更好地满足不同用户的实际需要.     

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。     

Analysis of a Heuristic for Code Partitioning

In this paper, we analyze the time complexity and performance of a heuristic for code partitioning for Distributed Memory Multiprocessors (DMMs). The partitioning method is data-flow based where all levels of parallelism are exploited. Given a weighted Directed Acyclic Graph (DAG) representation of the program, our algorithm automatically determines the granularity of parallelism by partitioning the graph into tasks to be scheduled on the DMM. The granularity of parallelism depends only on the program to be executed and on the target machine parameters. The output of our algorithm is passed on as input to the scheduling phase. Finding an optimal solution to this problem is NP-complete. Due to the high cost of graph algorithms, it is nearly impossible to come up with close to optimal solutions that do not have very high cost (higher order polynomial). Our proposed heuristic gives good performance and has relatively low cost.