一种扩展的虚拟树型网格可靠性评估模型

在已有虚拟树型网格可靠性评估模型的基础上引入了"截止时间违约失效",提出一种扩展的网格可靠性评估模型,用于支持实时网格任务的可靠性评估.该扩展模型采用队列系统来建立网格资源的负载模型,解决了原模型中不支持并行资源和无法感知动态负载的缺陷.理论分析给出了模型的有效性证明和实时性任务的可靠性计算方法.实验结果显示,在饱和负载状态下,该扩展模型能显著提高可靠性评估的准确性,其动态负载感知机制能有效降低任务的平均响应时间.     

基于动态有色Petri网的网格服务工作流模型的研究

在深入了解网格技术、网格服务和网格工作流的概念、特点及其应用的基础上,提出了一种可行的网格服务工作流系统模型,重点介绍了动态优化建模技术、动态调度算法的实现思想.定义了一种动态有色Petri网作为服务工作流的建模工具,支持服务工作流的动态优化建模和动态调度,并为服务工作流模型提供性能评价依据.验证表明采用该模型能够很好地满足用户的QoS要求,并且有助于提高资源利用率.     

基于工作流的图像处理网格平台及其遥感应用

图像处理网络平台基于工作流实现资源共享和协同计算,是一个灵活、动态的图像处理问题解决环境.其中,网格工作流技术可满足灵活多变的应用需求,通用服务请求代理能实现异构网格服务的简单调用,动态工作流模型利用具有较强描述功能的扩展有向无环图建模语言进行定义.该平台已成功地应用于遥感图像处理.     

网格环境下费用约束的科学工作流可靠调度算法

网格基础设施是目前科学工作流应用规划、部署和执行的主要支撑环境.然而由于网格资源的自治、动态及异构性,如何在保障用户QoS约束下有效调度科学工作流是一个研究热点.针对费用约束下的科学工作流调度问题,为了提高其执行的可靠性,本文使用随机服务模型描述资源节点的动态服务能力并考虑本地任务负载对资源执行性能的影响,给出一种资源可靠性的评估方法,在此基础上提出一种费用约束下的科学工作流可靠调度算法RSASW.仿真实验结果表明RSASW算法相对于GAIN3,GreedyTime-CD及PFAS算法,对工作流的执行具有很好的可靠性保障.     

基于扩展关键活动的工作流调度算法

服务资源分配和调度在开放网格服务架构环境中是一个复杂且具有挑战性的问题.为适应网格计算环境下服务资源的动态性和自治性,提出一个基于扩展关键活动的工作流调度算法,采用动态选择所需服务资源的策略,工作流完成时间和所需成本得到一定程度的平衡.实验结果表明该调度算法在完成时间、成本等方面可得到较满意的结果,能更好地满足用户实际需要.     

可视化工作流建模工具WorkFlowEditor设计与实现

针对企业业务过程建模的需求,分析目前典型的工作流过程建模方法,并结合工作流管理联盟提出的XPDL过程定义语言,设计一种可视化工作流建模工具Work Flow Editor。依据"问题分离"原则,对WFMC的工作流过程定义元模型进行了改进,使建立的模型具有易修改、元素可重用等特点。针对建模元素可扩展性的需求,通过定义建模元素类的通用接口和设计一种动态加载方案,在很好地方便企业用户定制建模元素的同时又不需要修改建模工具。针对多任务多分支环境下复杂的流程编辑问题,设计了一种有限状态机(FSM)模型,实现了复杂编辑动作的实时性处理和灵活控制,降低了时间复杂度,并且实现模块化编程,很好地实现了流程的建模。     

考虑位置失效的移动系统安全模型*

以带位置扩展的移动进程π演算模型为形式化工具,在考虑位置失效的基础上,将移动系统的安全性质转换为系统进程在给定计算环境中的位置互模拟关系,提出了考虑位置失效的一种新安全模型。针对实际安全需求,该模型可以方便地描述不同的非干扰安全性质;该模型提供了一个统一的安全分析架构,可广泛用于移动计算系统的安全性分析。     

基于截止时间满意度的网格工作流调度算法

动态网格环境中用户截止时间保障是工作流调度问题的一个挑战.利用随机服务模型来描述网格资源的动态处理能力及其动态负载压力,提出了截止时间满意度的概念和工作流截止时间满意度的计算方法.将以DAG图形式表示的任务执行关系转换为以数值表示的任务执行优先级,并根据最大截止时间满意度优先的思想,确定执行工作流子任务的候选资源;将工作流全局截止时间划分问题描述为一个约束下的非线性规划问题并通过已有方法求解该问题,提出了一种截止时间满意度增强的工作流调度算法(DSESAW).仿真实验采用实际网格应用和系统数据来验证所提出算法的性能表现,实验结果表明新算法在网格环境的自适应性和用户截止时间保障方面优于其他两种实际网格系统中的调度算法.     

网格工作流研究现状及存在问题

网格计算成为当前业界研究的热点,网格工作流能够方便地构建、执行、管理和监控网格应用,使得网格应用能够自动、高效地实施。由于网格的动态性、分布性、异构性和自治性导致传统工作流的一些方法和技术不能有效地处理网格环境中的有关问题。本文首先比较了网格工作流与传统工作流的不同,指出了网格工作流研究的必要性,然后介绍了当前网格工作流研究的现状,归纳了三种网络工作流规范及四种网格工作流系统。最后,给出了网格工作流研究存在的问题及研究方向。     

混合计算环境中截止期约束下的科学工作流调度策略

整合云和网格基础设施,增强科研机构现有网格系统的计算能力并向应用提供截止时间保障的服务是科学研究领域的热点。在这种"网格-云"混合计算环境中,对何时租借云虚拟资源以及如何租借做出有效决策是一个难题。现有的一些调度策略主要在网格资源静态能力特征的基础上,以作业等待时间作为决策依据,缺乏对资源动态服务能力的有效评估,无法保证科学应用的截止时间需求。本文提出了一种混合环境下的科学工作流执行系统架构并对其核心组件进行了阐述。针对其中的工作流调度问题,利用随机服务模型建模已有网格系统中的资源的动态服务能力,以任务违约风险作为是否租借外部虚拟资源的判断指标,提出了一个科学工作流调度算法HCA_SASWD。实验结果表明,HCA_SASWD相对于其他算法,能有效保证用户的截止时间要求,为需要提供截止时间保障的系统架构提供了参考。     

Infrastructure Aware Scientific Workflows and Infrastructure Aware Workflow Managers in Science Gateways

The workflow interoperability problem was successfully solved by the SHIWA project if the workflows to be integrated were running in the same grid infrastructure. However, in the more generic case when the workflows were running in different infrastructures the problem has not been solved yet. In the current paper we show a solution for this problem by introducing a new type of workflow called infrastructure-aware workflow. These are scientific workflows extended with new node types that enable the on-the-fly creation and destruction of the required infrastructures in the clouds. The paper shows the semantics of these new types of nodes and workflows and also how they can solve the workflow interoperability problem. The paper also describes how these new type of workflows can be implemented by a new service called Occopus, and how this service can be integrated with the existing SHIWA Simulation Platform services like the WS-PGRADE/gUSE portal to provide the required functionalities of solving the workflow interoperability problem.     

基于随机良构工作流网的工作流进程性能分析

工作流进程中的QoS受很多非功能性因素的影响,例如性能、可靠性以及安全性等等。对QoS度量的管理直接影响到参与工作流具体应用的服务能否顺利完成。因此,当服务被工作流或Web进程创建或管理时,底层的工作流引擎必须估计、检测并控制用户的QoS。基于已有的工作流模型分解算法,利用随机良构工作流网的数值分析,给出了分析工作流进程QoS的一种新方法,并结合实例加以验证。     

Overhead Analysis of Scientific Workflows in Grid Environments

Scientific workflows are a topic of great interest in the grid community that sees in the workflow model an attractive paradigm for programming distributed wide-area grid infrastructures. Traditionally, the grid workflow execution is approached as a pure best effort scheduling problem that maps the activities onto the grid processors based on appropriate optimization or local matchmaking heuristics such that the overall execution time is minimized. Even though such heuristics often deliver effective results, the execution in dynamic and unpredictable grid environments is prone to severe performance losses that must be understood for minimizing the completion time or for the efficient use of high-performance resources. In this paper, we propose a new systematic approach to help the scientists and middleware developers understand the most severe sources of performance losses that occur when executing scientific workflows in dynamic grid environments. We introduce an ideal model for the lowest execution time that can be achieved by a workflow and explain the difference to the real measured grid execution time based on a hierarchy of performance overheads for grid computing. We describe how to systematically measure and compute the overheads from individual activities to larger workflow regions and adjust well-known parallel processing metrics to the scope of grid computing, including speedup and efficiency. We present a distributed online tool for computing and analyzing the performance overheads in real time based on event correlation techniques and introduce several performance contracts as quality-of-service parameters to be enforced during the workflow execution beyond traditional best effort practices. We illustrate our method through postmortem and online performance analysis of two real-world workflow applications executed in the Austrian grid environment.     

A Framework for Adaptive Fault-Tolerant Execution of Workflows in the Grid: Empirical and Theoretical Analysis

In this paper, we propose and evaluate a framework for fault tolerant workflow execution in Grid environments. Different from previous work in the literature, our system dynamically chooses an appropriate fault tolerance technique while using a user-defined rule-based system. We also provide a generic interface that can be used to add fault tolerance techniques to the framework. The results obtained with real workflows in an experimental Grid environment show that the overhead introduced by our framework in a failure-free execution is, in the worst evaluated case, approximately 10 %. Moreover, we show that, using our framework, workflows are able to execute successfully in the presence of failures and that the framework can dynamically choose an appropriate fault tolerance technique. The main contributions of our work are twofold: the developed framework and the model-based dependability analysis we performed on it. The purpose in carrying out a model-based dependability analysis consists on evaluating the interaction between our framework and the distributed Grid environment beyond the physical limitations of an empirical evaluation. By doing this, we provide means to plan the assurance of QoS in the Grid resource allocation, while applying the fault-tolerance mechanisms we implement in our framework regardless of the underlying middleware.     

基于WEB工作流的动态绑定建模技术研究

工作流技术是实现业务流程建模、仿真、优化、集成的有效手段之一。尤其是基于WEB的工作流动态建模技术,是增强信息系统业务流程互联互通、实现业务流程动态可配置、流程优化重组的有力措施。文章首先分析了工作流控制模型,然后构建了基于WEB的多流程工作流协同控制体系。采用基于XML的技术方案给出了流程初始化定义的配置框架,通过工作流引擎调用WEB服务描述语言WSDL,实现了面向web服务的多流程动态绑定。达到了实际业务流程逻辑与流程组织逻辑分离的目的。     

一种基于OGSI的网格工作流描述语言

网格被称为下一代的Internet。网格工作流平台是实现网格计算的重要基础软件,而网格工作流描述语言则是定义和使用工作流的重要工具。该文提出了一种基于OGSI规范的网格工作流描述语言GBPEL。     

Abstract,link, publish,exploit: An end to end framework for workflow sharing

Scientific workflows are increasingly used to manage and share scientific computations and methods to analyze data. A variety of systems have been developed that store the workflows executed and make them part of public repositories However, workflows are published in the idiosyncratic format of the workflow system used for the creation and execution of the workflows. Browsing, linking and using the stored workflows and their results often becomes a challenge for scientists who may only be familiar with one system. In this paper we present an approach for addressing this issue by publishing and exploiting workflows as data on the Web with a representation that is independent from the workflow system used to create them. In order to achieve our goal, we follow the Linked Data Principles to publish workflow inputs, intermediate results, outputs and codes; and we reuse and extend well established standards like W3C PROV. We illustrate our approach by publishing workflows and consuming them with different tools designed to address common scenarios for workflow exploitation.     

A Survey of Data-Intensive Scientific Workflow Management

Nowadays, more and more computer-based scientific experiments need to handle massive amounts of data. Their data processing consists of multiple computational steps and dependencies within them. A data-intensive scientific workflow is useful for modeling such process. Since the sequential execution of data-intensive scientific workflows may take much time, Scientific Workflow Management Systems (SWfMSs) should enable the parallel execution of data-intensive scientific workflows and exploit the resources distributed in different infrastructures such as grid and cloud. This paper provides a survey of data-intensive scientific workflow management in SWfMSs and their parallelization techniques. Based on a SWfMS functional architecture, we give a comparative analysis of the existing solutions. Finally, we identify research issues for improving the execution of data-intensive scientific workflows in a multisite cloud.     

A Provenance-based Adaptive Scheduling Heuristic for Parallel Scientific Workflows in Clouds

In the last years, scientific workflows have emerged as a fundamental abstraction for structuring and executing scientific experiments in computational environments. Scientific workflows are becoming increasingly complex and more demanding in terms of computational resources, thus requiring the usage of parallel techniques and high performance computing (HPC) environments. Meanwhile, clouds have emerged as a new paradigm where resources are virtualized and provided on demand. By using clouds, scientists have expanded beyond single parallel computers to hundreds or even thousands of virtual machines. Although the initial focus of clouds was to provide high throughput computing, clouds are already being used to provide an HPC environment where elastic resources can be instantiated on demand during the course of a scientific workflow. However, this model also raises many open, yet important, challenges such as scheduling workflow activities. Scheduling parallel scientific workflows in the cloud is a very complex task since we have to take into account many different criteria and to explore the elasticity characteristic for optimizing workflow execution. In this paper, we introduce an adaptive scheduling heuristic for parallel execution of scientific workflows in the cloud that is based on three criteria: total execution time (makespan), reliability and financial cost. Besides scheduling workflow activities based on a 3-objective cost model, this approach also scales resources up and down according to the restrictions imposed by scientists before workflow execution. This tuning is based on provenance data captured and queried at runtime. We conducted a thorough validation of our approach using a real bioinformatics workflow. The experiments were performed in SciCumulus, a cloud workflow engine for managing scientific workflow execution.     

A rotary chaotic PSO algorithm for trustworthy scheduling of a grid workflow

In this paper, a rotary chaotic particle swarm optimization (RCPSO) algorithm is presented to solve trustworthy scheduling of a grid workflow. In general, the grid workflow scheduling is a complex optimization problem which requires considering various scheduling criteria so as to meet a wide range of QoS requirements from users. Traditional researches into grid workflow scheduling mainly focus on the optimization constrained by time and cost. The key requirements for reliability, availability and security are not considered adequately. The main contribution of this study is to propose a new approach for trustworthy workflow scheduling in a large-scale grid with rich service resources, and present the RCPSO algorithm to optimize the scheduling performance in a multi-dimensional complex space. Experiments were done in two grid applications with at most 120 candidate services supplied to each task of various workflows. The results show better performance of the RCPSO in solving trustworthy scheduling of grid workflow problems as compared to GA, ACO and other recent variants of PSO.