基于服务质量的网格工作流系统研究

网格工作流可以定义成为完成特定目标而在异构和分布的资源上以一定次序执行的网格应用服务的集合.在网格工作流中引入服务质量,为网格服务的调度提供了依据,使得工作流的执行更加满足用户需求.通过使用服务质量可以对网格工作流进行选择和执行,从而更加符合用户的期望.提出了一种典型的基于服务质量的网格工作流管理体系结构及相关的服务质量参数体系,讨论了基于服务质量的工作流调度算法,并给出一个基于快速遗传算法的应用实例.     

一种服务质量感知的网格工作流引擎

引入服务质量等非功能特性作为网格工作流的调度依据,可以使得网格工作流的执行和调度围绕服务质量的参数体系进行,能够更好地满足最终用户的要求.提出一种服务质量感知的网格工作流引擎QGWEngine,介绍QGWEngine支持的服务质量参数体系和所基于的网格工作流模型.给出QGWEngine的系统结构,并通过一个假想的网格工作流的例子验证了QGWEngine的有效性.     

基于服务质量的网格工作流算法优化

本文介绍了网格的基本概念,结合工作流任务的服务质量(QoS)需求,提出了基于服务质量的网格工作流调度算法,对GGWF算法中的LGSS算法做了改进,提出了ILGSS算法,对该算法的算法复杂度进行了分析,并在局域网环境下做了仿真实验,并给出了实验结果和分析,提出了在网格环境下探索自适应的工作流事务机制这一十分重要的研究方向,为网格环境下工作流的调度提供了一种新的解决方案.     

基于AGWL网格工作流模型的服务质量估算研究

针对ASKALON网格工作流管理系统中缺乏对服务质量组合方面的研究,基于AGWL网格工作流模型,提出了一种网格工作流服务质量的估算算法。该算法的主要特点是:1)基于AGWL语言;2)可扩展的QoS度量;3)多维全局QoS度量。最后,用仿真实验验证了该算法的可行性。     

自定义服务质量模型的网格工作流调度算法

随着网格服务应用的发展,在网格工作流中,复杂的任务可以由多个独立的服务,通过工作流引擎等方式组合成新服务后完成。在组合服务的过程中,由不同服务提供商提供的候选服务,具有不同的服务质量参数,在网格工作流调度中,需要满足用户定义的服务质量约束。提出了方便用户定义的服务质量模型,并且在该模型的基础上,改进了网格工作流调度算法,通过实验分析证明改进后的算法优于传统的调度算法。     

一种新颖的网格工作流调度算法

针对当前网格工作流调度算法中大多只考虑DAG结构的网格工作流、涉及QoS参数较少及将多QoS参数聚合成一个单目标函数进行优化调度的现状,提出了一种新颖的网格工作流调度算法。该算法基于表达结构丰富的AGWL语言建模网格工作流,且基于MOPSO算法所设计的带多QoS约束的多目标优化的网格工作流调度算法。通过与基于NSGA-Ⅱ算法的网格工作流调度算法比较,表明了该算法的有效性。     

融合DE和NSGA-Ⅱ算法的网格工作流调度研究*

针对当前网格工作流调度算法中大多只考虑DAG结构的网格工作流、考虑QoS维数较少及将多QoS参数聚合成一个单目标函数进行优化调度的现状,基于AGWL网格工作流模型,提出了一种带QoS约束的多目标优化的网格工作流调度算法,该算法是将DE的变异和交叉算子替换NSGA-Ⅱ中的变异和交叉操作所设计的一种调度算法。通过与基于NSGA-Ⅱ的网格工作流调度算法比较,表明了该算法的有效性。     

基于GRIDSIM的网格调度Min-Min算法改进研究

本文中的网格任务调度算法是在研究异构工作流系统基于OGSA网格协同任务调度的过程中,根据网格环境中资源的可用度,在特定的相依性网格任务环境下,对经典Min—Min算法进行了部分改进,提出基于资源可用度和任务相关性的相依性网格任务映射启发式算法。在作者所设计的层次网格任务调度器中得到了较好的调度效果和调度服务质量。     

基于二维协调空间的网格工作流调度算法研究

传统的网格工作流模型中分布式工作流管理器之间没有合作,因此可能发生源调度冲突问题,另外,在现有的工作流调度算法中,参与工作流调度的工作流管理器依托于集中或半集中的层次式的资源信息服务体系,导致系统的扩展性差.为了解决这些问题,在文中,提出了一个分布式的协同工作流调度算法.该算法基于二维协调空间来管理网格中的工作流管理器.二维协调空间负责资源发现和协调调度等功能.该算法不仅可以避免性能瓶颈,而且可以增强系统的可扩展性和自主性.     

基于混沌遗传算法的网格工作流调度应用

动态网格环境中, 多QoS(服务质量)约束下的工作流调度问题是决定其任务执行成功与否及效率高低的关键。现有的网格工作流调度算法难以满足实际应用中的不同需求, 同时算法欠优化, 难以提供多种策略, 由此提出了一种基于期限与预算两个QoS约束的改进型混沌遗传算法。首先, 为避免算法出现收敛停滞将混沌机制引入遗传算法并对变异概率进行自适应处理。其次, 提出时间和预算的线性结合概念, 将目标函数转换为适应值函数。最终基于工作流调度中的平衡结构和非平衡结构测试了算法的有效性。     

一个市场驱动的QoS网格工作流任务调度算法

网格调度关系到整个网格任务运行的效率,因此在网格的研究过程中,已经提出了很多调度算法.但这些算法大部分是对元任务(Meta-task)进行调度,很少是针对关联任务的.在考虑用户QoS(Quality of Service)需求的情况下,提出了一个市场驱动的QoS网格工作流任务调度算法.仿真实验结果表明了该算法的合理性和有效性.     

A grid workflow Quality-of-Service estimation based on resource availability prediction

Accurate estimation of workflow Quality of Service (QoS) enhances the efficiency of scheduling algorithms. The availability and performance variations of Grid computing resources have made this estimation a great challenge. Most workflow QoS estimation algorithms are based on static performance of resources. In this paper, based on resources availability prediction, we propose an algorithm called WQE for estimating the QoS of a Grid workflow. WQE consists of two phases: resource monitoring and analysis and workflow QoS computation. In the first phase, two prediction algorithms are proposed to stochastically predict the availability state of resources. In the second phase, the QoS of each activity is estimated based on the host availability prediction result. The QoS of basic structures is computed by aggregating the QoS of their operands. Using a tree structure corresponding to the workflow, the QoS of basic structures is used to compute the total QoS of the workflow. The simulation results on Notre Dame University trace showed that the proposed method has higher estimation accuracy in comparison with HEFT.     

基于微粒群算法的网格工作流优化调度问题的研究

网格工作流中的调度问题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及效率的高低.针对具有时序和因果约束关系的网格工作流优化调度问题进行了研究,建立了网格工作流的任务调度模型和调度问题的目标模型,并应用微粒群算法来优化网格工作流中任务的调度.实验结果证明该算法优于传统的调度算法.     

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.     

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。     

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.     

Dependable Grid Workflow Scheduling Based on Resource Availability

Due to the highly dynamic feature, dependable workflow scheduling is critical in the Grid environment. Various scheduling algorithms have been proposed, but seldom consider the resource reliability. Current Grid systems mainly exploit fault tolerance mechanism to guarantee the dependable workflow execution, which, however, wastes system resources. The paper proposes a dependable Grid workflow scheduling system (called DGWS). It introduces a Markov Chain-based resource availability prediction model. Based on the model, a reliability cost driven workflow scheduling algorithm is presented. The performance evaluation results, including the simulation on both parametric randomly generated DAGs and two real scientific workflow applications, demonstrate that compared to present workflow scheduling algorithms, DGWS improves the success ratio of tasks and diminishes the makespan of workflow, so improves the dependability of workflow execution in the dynamic Grid environments.     

Comparison of selected algorithms for scheduling workflow applications with dynamically changing service availability

This paper compares the quality and execution times of several algorithms for scheduling service based workflow applications with changeable service availability and parameters. A workflow is defined as an acyclic directed graph with nodes corresponding to tasks and edges to dependencies between tasks. For each task, one out of several available services needs to be chosen and scheduled to minimize the workflow execution time and keep the cost of service within the budget. During the execution ofa workflow, some services may become unavailable, new ones may appear, and costs and execution times may change with a certain probability. Rescheduling is needed to obtain a better schedule. A solution is proposed on how integer linear pro- gramming can be used to solve this problem to obtain optimal solutions for smaller problems or suboptimal solutions for larger ones. It is compared side-by-side with GAIN, divide-and-conquer, and genetic algorithms for various probabilities of service unavailability or change in service parameters. The algorithms are implemented and subsequently tested in a real BeesyCluster environment.     

Real-time workflows oriented online scheduling in uncertain cloud environment

Workflow scheduling has become one of the hottest topics in cloud environments, and efficient scheduling approaches show promising ways to maximize the profit of cloud providers via minimizing their cost, while guaranteeing the QoS for users’ applications. However, existing scheduling approaches are inadequate for dynamic workflows with uncertain task execution times running in cloud environments, because those approaches assume that cloud computing environments are deterministic and pre-computed schedule decisions will be statically followed during schedule execution. To cover the above issue, we introduce an uncertainty-aware scheduling architecture to mitigate the impact of uncertain factors on the workflow scheduling quality. Based on this architecture, we present a scheduling algorithm, incorporating both event-driven and periodic rolling strategies (EDPRS), for scheduling dynamic workflows. Lastly, we conduct extensive experiments to compare EDPRS with two typical baseline algorithms using real-world workflow traces. The experimental results show that EDPRS performs better than those algorithms.