一种多重约束下确保成功率的云工作流调度方法

为提高多重约束下的调度成功率,提出一种满足期限和预算双重约束的云工作流调度算法。将可行工作流调度方案求解分解为工作流结构分层、预算分配、期限分配、任务选择和实例选择。工作流结构分层将所有工作流任务划分层次形成包任务,以提高并行执行程度;预算分配对整体预算在层次间进行分割;期限分配将全局期限在不同层次间分割;任务选择基于任务最早开始时间确定优先级,得到任务调度次序;实例选择根据时间和代价均衡因子,获取任务执行最佳实例。仿真结果证明,该算法在调度成功率、同步优化工作流执行时间与执行代价上相较对比算法更好。     

移动云计算中基于延时传输的多目标工作流调度

云计算和移动互联网的不断融合,促进了移动云计算的产生与发展.在移动云计算环境下,用户可将工作流的任务迁移到云端执行,这样不但能够提升移动设备的计算能力,而且可以减少电池能源消耗.但是不合理的任务迁移会引起大量的数据传输,这不仅损害工作流的服务质量,而且会增加移动设备的能耗.基于此,本文提出了基于延时传输机制的多目标工作流调度算法MOWS-DTM.该算法基于遗传算法,结合工作流的调度过程,在编码策略中考虑了工作流任务的调度位置和执行排序.由于用户在不断移动的过程中,移动设备的无线网络信号也在不断变化.当传输一定大小的数据时,网络信号越强则需要的时间越少,从而移动设备的能耗也越少.而且工作流结构中存在许多非关键任务,延长非关键任务的执行时间并不会对工作流的完工时间造成影响.因此,本文在工作流调度过程中融入了延时传输机制DTM,该机制能够同时有效地优化移动设备的能耗和工作流的完工时间.仿真结果表明,相比MOHEFT算法和RANDOM算法,MOWS-DTM算法在多目标性能上更优.     

基于Pi演算的跨组织工作流建模研究

传统的工作流建模方法主要用于描述组织内部流程,因而难以描述跨组织工作流面向流程、组合、抽象、涉及多个自治系统通信合作的新特点.针对此问题,提出了一种基于Pi演算的跨组织工作流建模方法,利用Pi演算的并发计算操作符,将跨组织业务流程建模为一组自治且并发执行的组织内子流程的组合,子流程建模为组织内本地流程定义和组织间控制约束的组合.基于Pi演算的弱互相似理论,验证了两个跨组织子流程外部行为的相等性,用于帮助组织内私有流程的外部抽象.基于该方法建立的跨组织工作流模型在子流程间建立了一种松耦合的关系,适用于动态的跨组织环境,同时基于严格的形式化方法,便于分析和验证.     

DAG分割模型下的云工作流调度策略

为了优化云工作流调度的经济代价和执行效率,提出一种基于有向无循环图（DAG）分割的工作流调度算法PBWS。以工作流调度效率与代价同步优化为目标,算法将调度求解过程划分为三个阶段进行：工作流DAG结构分割、分割结构调整及资源分配。工作流DAG结构分割阶段在确保任务间执行顺序依赖的同时求解初始的任务分割图;分割结构调整阶段以降低执行跨度为目标,在不同分割间对任务进行重分配;资源分配阶段旨在选择代价最高效的任务与资源映射关系,确保资源的总空闲时间最小。利用五种科学工作流DAG模型对算法进行了仿真实验。结果表明。PBWS算法仅以较小的执行跨度为开销,极大降低了工作流执行代价,实现了调度效率与调度代价的同步优化,其综合性能是优于同类型算法的。     

QoS约束下基于双向分层的网格工作流调度算法

为使网格工作流的执行满足用户QoS要求,应用有向无环图描述工作流,并分析其中的关键活动,把用户对工作流的整体QoS约束分割为对单个任务的QoS约束.以此为基础,提出了一种基于双向分层的网格工作流调度算法Q-TWS.该算法通过对工作流正向分层和逆向分层,可以方便并准确找到任务之间的并行关系.Q-TWS可最大程度放松对任务执行时间的约束,在增加调度灵活性的同时又满足用户的QoS要求.实验表明,Q-TWS算法与TL算法相比,在同样的截止时间约束下,工作流执行时间较短,且工作流执行费用较小.     

Bi-level fuzzy based advanced reservation of Cloud workflow applications on distributed Grid resources

The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners of Cloud applications expect to fulfill the requested Quality of Services (QoS) by the Grid environment, an adaptive scheduling mechanism is needed which enables to distribute a large number of related tasks with different computational and communication demands on multi-cluster Grid computing environments. Addressing the problem of scheduling large-scale Cloud workflow applications onto multi-cluster Grid environment regarding the QoS constraints declared by application’s owner is the main contribution of this paper. Heterogeneity of resource types (service type) is one of the most important issues which significantly affect workflow scheduling in Grid environment. On the other hand, a Cloud application workflow is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea which forms the soul of all the algorithms and techniques introduced in this paper is to match the heterogeneity in Cloud application’s workflow to the heterogeneity in Grid clusters. To obtain this objective a new bi-level advanced reservation strategy is introduced, which is based upon the idea of first performing global scheduling and then conducting local scheduling. Global-scheduling is responsible to dynamically partition the received DAG into multiple sub-workflows that is realized by two collaborating algorithms: (1) The Critical Path Extraction algorithm (CPE) which proposes a new dynamic task overall critically value strategy based on DAG’s specification and requested resource type QoS status to determine the criticality of each task; and (2) The DAG Partitioning algorithm (DAGP) which introduces a novel dynamic score-based approach to extract sub-workflows based on critical paths by using a new Fuzzy Qualitative Value Calculation System to evaluate the environment. Local-scheduling is responsible for scheduling tasks on suitable resources by utilizing a new Multi-Criteria Advance Reservation algorithm (MCAR) which simultaneously meets high reliability and QoS expectations for scheduling distributed Cloud-base applications. We used the simulation to evaluate the performance of the proposed mechanism in comparison with four well-known approaches. The results show that the proposed algorithm outperforms other approaches in different QoS related terms.     

一种满足可靠性和能效的云工作流调度方法

为了同步解决云工作流调度时的失效和高能耗问题,提出一种基于可靠性和能效的工作流调度算法.算法为了在截止时间的QoS约束下最大化系统可靠性并最小化调度能耗,将工作流调度过程划分为四个阶段:计算任务优先级、工作流任务聚簇、截止时间子分配和任务调度.算法在满足执行次序的情况下对任务进行拓扑排序,并以通信代价最小为目标对任务进...     

基于CORBA的多Agent工作流运行管理系统

本文设计并实现了一种基于CORBA环境的多Agent工作流运行管理系统。用工作流过程定义语言(WFPDL)定义工作流程后,以脚本文件形式交给工作流引擎进行解释执行。由于定义了通用接口,工作流引擎能够给各Agent动态分配工作流的任务,这样实现多Agent协作完成工作流,并且任务执行流程可随工作流定义的修改而改变,它无需修改源程序,这样提高了系统的动态重构能力。同时,文中还提供了对一类Legacy系统的封装方法,以提高软件的重用性。     

TAMAP: a new trust-based approach for mobile agent protection

Human activities are increasingly based on the use of distant resources and services, and on the interaction between remotely located parties that may know little about each other. Mobile agents are the most suited technology. They must therefore be prepared to execute on different hosts with various environmental security conditions. This paper introduces a trust-based mechanism to improve the security of mobile agents against malicious hosts and to allow their execution in various environments. It is based on the dynamic interaction between the agent and the host. Information collected during the interaction enables generation of an environment key. This key allows then to deduce the host’s trust degree and permits the mobile agent to adapt its execution accordingly to the host trustworthiness, its behavior history and the provided Quality of Service (QoS). An adaptive mobile agent architecture is therefore proposed. It endows the mobile agent with the ability to react with an unexpected behavior.     

基于可扩展组织的柔性过程建模

企业经营的业务过程如何柔性地适应企业组织的变化,成为企业提高竞争力的关键。该文提出一种基于可扩展组织的柔性过程建模方法,构建了一种工作流模型。在组织模型中引入子组织概念,使组织模型具有可扩展性。在过程模型中允许组织模型中的各级子组织和工作组独立地创建并动态地引用子过程。与传统可定义子过程的工作流模型相比,该过程建模方法具有良好的柔性和广阔的应用前景。     

Partitioning rules for orchestrating mobile information systems

New mobile technologies such as Bluetooth or Wi-Fi suffer from many limitations and problems, especially when they are used in combination, whereas they are quite stable in small networks. The lack of specialised mobile middleware requires new methods in the design and execution of mobile information systems. We propose a two-phase approach to manage a mobile business process by partitioning a given workflow into several workflows, with each one governed by a controller. In the first phase, we introduce synchronisation tasks between different controllers. In the second phase, we create for each controller a local process view. Thanks to added tasks, the overall execution of all local workflows achieve the same result as the original one. The mobile scenario and the necessity for more automation lead us to choose the Business Process Execution Language for Web Services (BPEL4WS) as the language for the process definition.     

Mobile crowdsensing with mobile agents

We introduce mobile agents for mobile crowdsensing. Crowdsensing campaigns are designed through different roles that are implemented as mobile agents. The role-based tasks of mobile agents include collecting data, analyzing data and sharing data in the campaign. Mobile agents execute and control the campaign autonomously as a multi-agent system and migrate in the opportunistic network of participants’ devices. Mobile agents take into account the available resources in the devices and match participants’ privacy requirements to the campaign requirements. Sharing of task results in real-time facilitates cooperation towards the campaign goal while maintaining a selected global measure, such as energy efficiency. We discuss current challenges in crowdsensing and propose mobile agent based solutions for campaign execution and monitoring, addressing data collection and participant-related issues. We present a software framework for mobile agents-based crowdsensing that is seamlessly integrated into the Web. A set of simulations are conducted to compare mobile agent-based campaigns with existing crowdsensing approaches. We implemented and evaluated a small-scale real-world mobile agent based campaign for pedestrian flock detection. The simulation and evaluation results show that mobile agent based campaigns produce comparable results with less energy consumption when the number of agents is relatively small and enables in-network data processing with sharing of data and task results with insignificant overhead.     

基于代理的工作流邦联及子流调用框架

在分析层次工作流建模和工作流执行时互操作研究的基础上,设计了一个基于代理的邦联及子流调用框架,将层次化建模技术、流程互操作技术和异构数据模型映射和转换技术集成在一起,同时提供了异常处理和灾难恢复功能,真正从业务层次上解决了流程协作问题,并在TiPLM2.9上实现了该解决方案.流程实例之间通过代理进行交互,各个流程独立运行在自己的工作流引擎中,实现了松耦合和隔离性,增强了系统健壮性.     

Modeling, run-time optimization and execution of distributed workflow applications in the JEE-based BeesyCluster environment

The paper presents a complete solution for modeling scientific and business workflow applications, static and just-in-time QoS selection of services and workflow execution in a real environment. The workflow application is modeled as an acyclic directed graph where nodes denote tasks and edges denote dependencies between the tasks. The BeesyCluster middleware is used to allow providers to publish services from sequential or parallel applications, from their servers or clusters. Optimization algorithms are proposed to select a capable service for each task so that a global criterion is optimized such as a product of workflow execution time and cost, a linear combination of those or minimization of the time with a cost constraint. The paper presents implementation details of the multithreaded workflow execution engine implemented in JEE. Several tests were performed for three different optimization goals for two business and scientific workflow applications. Finally, the overhead of the solution is presented.     

服务质量感知的网格工作流调度

在网格工作流中引入服务质量,可以使网格中的资源更好地围绕用户的要求进行组织和分配,服务质量为工作流执行过程中选择成员服务提供了依据.工作流服务质量的估算和服务质量感知的工作流调度是实现服务质量感知的网格工作流的两个关键问题.基于一种网格工作流模型讨论了网格工作流的服务质量参数体系,提出了工作流服务质量的估算算法和网格工作流调度数学模型,并提出了基于遗传算法的调度方法.仿真实验表明,该调度算法具有较好的收敛性.     

A3M: an agent architecture for automated manufacturing

This paper proposes a software architecture based on mobile agents for distributed process control applications. A set of agents is employed to handle, in a single manufacturing cell, automatic assignment of control tasks to controllers, monitoring of cell functionalities and dynamic cell reconfiguration. The agents operate in a two‐layered structure: at the highest level, the planning agents analyse the inputs of the system designer and automatically create the field agents, which operate at the lowest level and embed the control tasks to be executed. Field agents, which are mobile, are able to reach autonomously the controllers of the cell, in order to perform the control activity there. Exploiting the mobility enables a field agent to change its running device when the variation of the design parameters or a system fault requires a new task distribution. A load‐balancing algorithm is introduced, with the objective of assigning each field agent to a controller of the manufacturing cell in order to fairly distribute the computation load. The algorithm uses a branch‐and‐bound technique to explore all possible solutions and applies two heuristics to throw away non‐feasible solutions and select the best branch to analyse. The algorithm is designed to run on‐line in order to allow a fast task redistribution when a fault condition occurs in the process control environment. Copyright © 2008 John Wiley & Sons, Ltd.     

Execution coordination in mobile agent-based distributed job workflow execution

Mobile agent-based distributed job workflow execution requires the use of execution coordination techniques to ensure that an agent executing a subjob can locate its predecessors’ execution results. This paper describes the classification, implementation, and evaluation of execution coordination techniques in the mobile agent-based distributed job workflow execution system. First, a classification of the existing execution coordination techniques is developed for mobile agent systems. Second, to put the discussion into perspective, our framework for mobile agent-based distributed job workflow execution over the Grid (that is, MCCF: Mobile Code Collaboration Framework) is described. How the existing coordination techniques can be applied in the MCCF is also discussed. Finally, a performance study has been conducted to evaluate three coordination techniques using real and simulated job workflows. The results are presented and discussed in the paper.     

允许违反局部时间约束的科学工作流调度策略

提高科学工作流在云环境中的执行效率、降低执行费用受到广泛关注。用户期望的局部QoS约束与工作流的总体执行效率之间往往存在矛盾。针对该现象,在前期的研究基础上提出一种允许违反局部时间约束的科学工作流调度策略。通过对已聚簇的工作流任务集使用任务后向优先合并的方法,可实现任务间空闲时间片的合理利用,进而优化科学工作流的执行时间;另外,为充分利用任务的松弛时间,提高工作流的整体执行效率,允许部分任务的调度违反局部最晚完成时间的约束。实验结果表明,该策略能提前科学工作流的最早完成时间,提高处理机的利用率,并最终降低工作流的执行费用。     

Workflow-and-Platform Aware task clustering for scientific workflow execution in Cloud environment

A scientific workflow, usually consists of a good mix of fine and coarse computational granularity tasks displaying varied runtime requirements. It has been observed that fine grained tasks incur more scheduling overhead than their execution time, when executed on widely distributed platforms. Task clustering is extensively used, in such situations, as a runtime optimization method which involves combining multiple short duration tasks into a cluster, to be scheduled on a single resource. This helps in minimizing the scheduling overheads of the fine grained tasks. However, tasks grouping curtails the degree of parallelism and hence needs to be done optimally. Though a number of task clustering techniques have been developed to reduce the impact of system overheads, they fail to identify the appropriate number of clusters at each level of workflow in order to achieve maximum possible parallelism. This work proposes a level based autonomic Workflow-and-Platform Aware (WPA) task clustering technique which takes into consideration both; the workflow structure and the underlying resource set size for task clustering. It aims to achieve maximum possible parallelism among the tasks at a level of a workflow while minimizing the system overheads and resource wastage. A comparative study with current state of the art task clustering approaches on four well-known scientific workflows show that the proposed method significantly reduces the overall workflow execution time and at the same time is able to consolidate the load onto minimum possible resources.     

Web服务组合的马氏策略规划

针对Web服务存在的业务逻辑与服务质量的不确定性,以及时序、时间窗约束,本文提出了利用马尔可夫决策理论来解决Web服务组合中最优策略规划问题的方法。该方法首先将Web服务组合描述为有向无环图表示的任务网络,网络中每个节点代表一个任务。任务是由相应的Web服务来实现,任务之间的弧线代表任务间时序的约束,任务执行应满足时间窗的约束。在此基础上,建立Web服务组合的马尔可夫决策模型,从而获得Web服务组合的最优策略。