工作流技术在Web OA开发平台中的应用研究

以化工企业信息管理系统为基础,概述了在.NET上使用C#语言设计和实现WebOA软件平台,对工作流技术在开发平台中的应用进行了研究。     

面向资源架构的知识协同工作流模型和应用框架

传统的工作流模型注重流程的结构化、自动化控制,难于应付复杂的跨组织、多角色、知识密集型流程的建模和管理.提出一个面向知识和内容协作、支持复杂多角色、多主体(人机、机机)交互的工作流系统模型和应用框架.具体工作有:引入了虚拟资源抽象层,将结构化、半结构化和非结构化资源以及元数据资源纳入统一的管理;提出一个面向资源协同的工作流系统(Resource-Oriented Collaborative Workflow,ROCWF)的过程模型和资源控制模型,过程模型在Petri-Net的基础上扩充了条件变迁和上下文状态控制,资源控制模型基于ICN(Information Control Net),提出一种支持多角色多任务资源协同的控制模型(Task Resource Multi-Role Collaboration Model,TRRC);在此基础上,对流程服务进行了扩展,设计和实现了一组面向资源的REST( Representational State Transfer,表现状态转移)风格的接口.最后给出的实例表明基于此模型和架构,可以更好的支持跨组织、多角色、知识密集型流程的建模和管理,且基于REST架构的扩展提供了一种异构系统间协作的新方法.     

工作流合并规则中的冲突检测方法

在关系型规则和活动型规则形式描述工作流合并需求的基础上,提出一种基于可达关系的合并规则冲突检测算法。通过记录每条规则应用后的可达关系矩阵,以及新增可达关系对应的规则集方式,为合并规则集中存在冲突的若干规则组合进行较为准确的定位。实例分析表明,该算法为规则集的合理调整提供直接依据,可有效实施工作流的合并。     

基于AOV网的抽象工作流模型

针对项目管理中的工作流控制需求,提出一个基于顶点活动(AOV)网的抽象工作流模型。给出该模型的形式化定义以及各结点的时序关系,阐述工作流的设计与执行规则,包括分支设计规则和回路设计规则,定义工作流图分支结构中汇聚结点的时序同步规则,并给出一个优化的回路检测方法。实例应用结果证明了该模型的有效性。     

基于GAPSO混合算法的网格工作流调度研究

网格工作流调度关注大规模的资源和任务调度,是一个复杂且具有挑战性的问题,它影响着网格工作流执行成功与否以及效率的高低。提出了基于遗传粒子群(GAPSO)的混合算法,引用了特殊的适应度函数,设定了动态的交叉和变异概率,并提出了动态切换算法的方法。结合各自算法的优势,在算法运行初期利用遗传算法的全局搜索能力进行优化搜索,在后期利用粒子群较强的局部搜索能力加快收敛速度。仿真结果表明该算法在执行时间方面有一定的优越性,能更有效地解决网格工作流调度问题。     

On-demand minimum cost benchmarking for intermediate dataset storage in scientific cloud workflow systems

Many scientific workflows are data intensive: large volumes of intermediate datasets are generated during their execution. Some valuable intermediate datasets need to be stored for sharing or reuse. Traditionally, they are selectively stored according to the system storage capacity, determined manually. As doing science on clouds has become popular nowadays, more intermediate datasets in scientific cloud workflows can be stored by different storage strategies based on a pay-as-you-go model. In this paper, we build an intermediate data dependency graph (IDG) from the data provenances in scientific workflows. With the IDG, deleted intermediate datasets can be regenerated, and as such we develop a novel algorithm that can find a minimum cost storage strategy for the intermediate datasets in scientific cloud workflow systems. The strategy achieves the best trade-off of computation cost and storage cost by automatically storing the most appropriate intermediate datasets in the cloud storage. This strategy can be utilised on demand as a minimum cost benchmark for all other intermediate dataset storage strategies in the cloud. We utilise Amazon clouds’ cost model and apply the algorithm to general random as well as specific astrophysics pulsar searching scientific workflows for evaluation. The results show that benchmarking effectively demonstrates the cost effectiveness over other representative storage strategies.     

网格工作流引擎的设计与实现

针对传统工作流调度策略只强调任务的执行顺序、忽略任务本身相互间的逻辑关系等不足,基于GT4网格环境,以网格服务及其间的协作关系为出发点,设计并实现了用DAG(directed acyclic graph)模型描述网格工作流,并通过网格服务流语言(gridserviceflowlanguage,GSFL)形式化解析工作流的网格工作流引擎系统架构。在该架构中,设计并实现了基于Dijkstra的工作流调度算法,通过实例对该网格工作流引擎和调度算法进行了实验,实现了预期效果。     

Automated dynamic detection of busy–wait synchronizations

With the advent of multicores, multithreaded programming has acquired increased importance. In order to obtain good performance, the synchronization constructs in multithreaded programs need to be carefully implemented. These implementations can be broadly classified into two categories: busy–wait and schedule‐based. For shared memory architectures, busy–wait synchronizations are preferred over schedule‐based synchronizations because they can achieve lower wakeup latency, especially when the expected wait time is much shorter than the scheduling time. While busy–wait synchronizations can improve the performance of multithreaded programs running on multicore machines, they create a challenge in program debugging, especially in detecting and identifying the causes of data races. Although significant research has been done on data race detection, prior works rely on one important assumption—the debuggers are aware of all the synchronization operations performed during a program run. This assumption is a significant limitation as multithreaded programs, including the popular SPLASH‐2 benchmark have busy–wait synchronizations such as barriers and flag synchronizations implemented in the user code. We show that the lack of knowledge of these synchronization operations leads to unnecessary reporting of numerous races. To tackle this problem, we propose a dynamic technique for identifying user‐defined synchronizations that are performed during a program run. Both software and hardware implementations are presented. Furthermore, our technique can be easily exploited by a record/replay system to significantly speedup the replay. It can also be leveraged by a transactional memory system to effectively resolve a livelock situation. Our evaluation confirms that our synchronization detector is highly accurate with no false negatives and very few false positives. We further observe that the knowledge of synchronization operations results in 23% reduction in replay time. Finally, we show that using synchronization knowledge livelocks can be efficiently avoided during runtime monitoring of programs. Copyright © 2009 John Wiley & Sons, Ltd.     

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.     

工作流资源分配中约束规则的建模研究

传统工作流系统在对任务节点进行资源分配时,只进行了组织、角色、人员的分配,而较少对不同任务节点上执行人间的潜在关系进行建模.根据企业的实际应用需求,将这种潜在的约束关系提取出来进行建模,并以规则的形式进行定义.最后采用强有力的推理工具--规则引擎--来完成对定义的规则冲突检测以及规则的实现.