基于主机负载的任务执行时间预测研究

在网格计算中,预测网格节点性能的动态变化和网格任务的执行情况是影响网格任务高效调度是否成功的最重要因素之一.结合主机负载和任务执行时间的线性关系,研究分析了一种基于主机负载的任务执行时间预测算法.利用仿真实现了这种算法,对基于主机负载实现任务执行时间预测的可靠性进行了实验论证,并给出了预测精度的评价结果.     

一种网格工作流动态调度算法

由于网格系统异构和资源动态变化,网格工作流多个任务对资源的不同需求,以及任务之间的时序、因果和数据依赖关系,使得网格工作流调度问题非常复杂,低性能的资源和任务调度策略,将会增加任务的执行时间并降低整个网格系统的吞吐量。本文针对网格工作流的特点提出了一种动态调度算法,该算法追求优化执行时间和系统负载均衡的双重目的,最后通过实验验证了该算法的可行性和优越性。     

高性能存储系统的读请求的建模和预测

为了改善高性能计算系统的I／0性能，本文设计和实现了一个读请求的空间和时间特性的综合预测模型-马尔可夫空间模型与自回归集成的移动平均时间模型，该模型在应用程序执行过程中被自动识别；每个新的请求到达时模型参数被实时递归地估计，并根据应用程序本身和运行环境自适应改变。经过实验检验我们的建模和预测比较有效，测试例子表明时间模型的预测值与观测值的相对误差的平均值为0．16％，并且预测的稳定性较好；空间模型的预测准确性也较好。     

服务网格中资源可用性评估方法研究*

资源可用性的预测与评估是动态网格环境下合理资源选择和保证服务质量的前提和基础。基于相关资源和任务的历史信息,利用概率论方法对资源可用性进行了预测与评估,提出了资源离线时间、本地任务执行时间、等待队列长度、等待时间等可用性尺度,证明并给出这些尺度的分布函数。实验表明,基于相关历史信息对资源可用性进行预测方法有效,并且根据资源可用性评估及提取的相关可用性尺度来确定任务调度的候选资源,可大大减少候选资源数目,从而降低调度的时间复杂度。     

网格资源管理系统的体系结构与应用

本文介绍了一个名为简单网格资源管理(Simple Grid Resource Manager SGRM)的网格资源管理系统体系结构,在这个管理系统中能够以命令方式提交任务,查询网格资源或使正在运行的任务退出运行序列。使用该系统能够免去手工编写复杂的任务配置文件的过程,提供了一种在具有很大运算能力的网格系统中解决计算密集型问题的途径。实验比较了使用SGRM和没有使用SGRM的情况下任意串行程序运行的时间,结果表明SGRM能使在网格系统中运行的应用程序的执行时间缩短。     

基于蚁群优化算法的服务网格的作业调度

提出了利用蚁群算法来优化服务网格的作业调度系统的方法和一个两层的作业调度模型，该模型可以在网格的动态和异构环境下实现对作业执行时间的预测，然后根据作业的预测执行时间并利用蚁群优化算法使适应函数取得最小值，从而得到最优化的作业调度。基于开发的校园网格实验床，通过实验显示该方法可以优化服务网格的性能，减少作业的平均执行时问，提高系统的吞吐率。     

一种基于预测的反馈网格作业调度模型*

网格计算中的资源是动态和异构的,常规的静态作业调度方法不适宜网格计算环境,对于网格计算中一类并行计算的有效执行有赖于网格资源（CPU和网络带宽等）与作业的有效匹配。提出了一种基于资源预测结果对作业进行调度的策略,首先阐述了网格主机负载预测的研究成果——IAR模型,并提出了一种预测网络带宽的工具——网络性能平面,利用资源预测结果构造了一种反馈作业调度模型并对一类基于时间平衡的作业进行实验。结果表明,该模型在与其他诸多方法比较中,取得了执行时间较短和稳定性较好的效果。     

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.     

网格资源管理系统的体系结构与应用

本文介绍了一个名为简单网格资源管理(Simple Grid Resource Manager SGRM)的网格资源管理系统体系结构．在这个管理系统中能够以命令方式提交任务，查询网格资源或使正在运行的任务退出运行序列。使用该系统能够免去手工编写复杂的任务配置文件的过程，提供了一种在具有很大运算能力的网格系统中解决计算密集型问题的途径。实验比较了使用SGRM和没有使用SGRM的情况下任意串行程序运行的时间，结果表明SGRM能使在网格系统中运行的应用程序的执行时间缩短。     

一种基于网络属性的信息服务机制的设计

网格技术为地理上广泛分布的资源的共享和协作提供了很大的便利,信息服务是其重要组成部分,而信息服务的性能属性直接影响整个网格平台的服务质量.本文设计了一种基于网络属性的新的信息服务机制,该机制将IP属性与网络的带宽和链路的传输延迟时间相联系,将每一用户任务调度至最合理的资源上执行,使任务向资源的总提交时间最短,从而优化了资源的分配调度过程,提高了网格性能.     

Adaptive middleware supporting scalable performance for high-end network services

Network service-based computation is a promising paradigm for both scientific and engineering, and enterprise computing. The network service allows users to focus on their application and obtain services when needed, simply by invoking the service across the network. In this paper, we show that an adaptive, general-purpose run-time infrastructure in support of effective resource management can be built for a wide range of high-end network services running in a single-site cluster and in a Grid. The primary components of the run-time infrastructure are: (1) dynamic performance prediction; (2) adaptive intra-site resource management; and (3) adaptive inter-site resource management. The novel aspect of our approach is that the run-time system is able to dynamically select the most appropriate performance predictor or resource management strategy over time. This capability not only improves the performance, but also makes the infrastructure reusable across different high-end services. To evaluate the effectiveness and applicability of our approach, we have transformed two different classes of high-end applications—data parallel and distributed applications—into network services using the infrastructure. The experimental results show that the network services running on the infrastructure significantly reduce the overall service times under dynamically varying circumstances.     

Programming Grid Applications with GRID Superscalar

The aim of GRID superscalar is to reduce the development complexity of Grid applications to the minimum, in such a way that writing an application for a computational Grid may be as easy as writing a sequential application. Our assumption is that Grid applications would be in a lot of cases composed of tasks, most of them repetitive. The granularity of these tasks will be of the level of simulations or programs, and the data objects will be files. GRID superscalar allows application developers to write their application in a sequential fashion. The requirements to run that sequential application in a computational Grid are the specification of the interface of the tasks that should be run in the Grid, and, at some points, calls to the GRID superscalar interface functions and link with the run-time library.GRID superscalar provides an underlying run-time that is able to detect the inherent parallelism of the sequential application and performs concurrent task submission. In addition to a data-dependence analysis based on those input/output task parameters which are files, techniques such as file renaming and file locality are applied to increase the application performance. This paper presents the current GRID superscalar prototype based on Globus Toolkit 2.x, together with examples and performance evaluation of some benchmarks.     

基于虚拟机的自适应网格运行环境

网格应用必须适应动态变化的运行环境。该文探讨动态且自适应的资源管理模式,设计并实现一种面向服务的分布式虚拟机——Abacus虚拟机。根据资源管理策略与运行时的可用资源情况,Abacus虚拟机自适应地在分布式环境中为应用程序分配资源。实验结果显示,该自适应的资源管理方式是可行有效的。     

QoS guided Min-Min heuristic for grid task scheduling

Task scheduling is an integrated component of computing.With the emergence of Grid and ubiquitous computing,new challenges appear in task scheduling based on properties such as security,quality of service,and lack of central control within distributed administrative domains.A Grid task scheduling framework must be able to deal with these issues.One of the goals of Grid task scheduling is to achivev high system throughput while matching applications with the available computing resources.This matching of resources in a non-deterministically shared heterogeneous environment leads to concerns over Quality of Service (QoS).In this paper a novel QoS guided task scheduling algorithm for Grid computing is introduced.The proposed novel algorithm is based on a general adaptive scheduling heuristics that includes QoS guidance.The algorithm is evaluated within a simulated Grid environment.The experimental results show that the nwe QoS guided Min-Min heuristic can lead to significant performance gain for a variety of applications.The approach is compared with others based on the quality of the prediction formulated by inaccurate information.     

基于信息流的整数漏洞插装和验证

为降低整数漏洞插装验证的运行开销,提出基于信息流的整数漏洞插装方法.从限定分析对象范围的角度出发,将分析对象约减为污染信息流路径上的所有危险整数操作,以降低静态插装密度.在GCC平台上,实现了原型系统DRIVER(detect and run-time check integer-based vulnerabilities with information flow).实验结果表明,该方法具有精度高、开销低、定位精确等优点.     

Grid harvest service: A performance system of grid computing

Conventional performance evaluation mechanisms focus on dedicated systems. Grid computing infrastructure, on the other hand, is a shared collaborative environment constructed on virtual organizations. Each organization has its own resource management policy and usage pattern. The non-dedicated characteristic of Grid computing prevents the leverage of conventional performance evaluation systems. In this study, we introduce the grid harvest service (GHS) performance evaluation and task scheduling system for solving large-scale applications in a shared environment. GHS is based on a novel performance prediction model and a set of task scheduling algorithms. GHS supports three classes of task scheduling, single task, parallel processing and meta-task. Experimental results show that GHS provides a satisfactory solution for performance prediction and task scheduling of large applications and has a real potential.     

一种快速的网格任务调度算法的设计

网格任务的执行环境具有动态性、分布性等特征,为了能顺利完成任务并使其具有较好的执行效率,需要一种有效的策略来进行任务的调度.结合信息处理的特点,提出一种快速有效的网格任务调度算法.该算法采用历史信息预测任务的执行时间,根据任务的截止时间要求对子任务进行合理分组.最后,给出了该算法在网格模拟器上的测试结果,并与一些算法进行了比较.结果表明,本算法对大作业以及截止期限紧急的作业具有较好的调度效果.     

An adaptive scheme for predicting the usage of grid resources

In order to manage the grid resources more effectively and provide a more suitable job scheduling strategy, the prediction information is needed for applications in the Grid computing system, such as the high performance computing and sharing computational resources, etc. In this paper, we propose a prediction system that can predict most information in the grid environment. Whether the repetitive time series pattern of the information exists or not, the proposed system can provide prediction results. We label the environment information in the grid and use the periodicity detector to detect the iterative patterns. The detected patterns can be used to predict several future values. Before the repetitive patterns have been found, a simple scheme that does not require a lot of resource has been used to generate prediction values. A prototype of this model is developed and tested with several test cases. The experimental results by the simulation show that our prediction system is able to capture different kinds of time series patterns and provide accurate prediction for the grid environment.     

Dynamic Instrumentation, Performance Monitoring and Analysis of Grid Scientific Workflows

While existing work concentrates on developing QoS models of business workflows and Web services, few tools have been developed to support the monitoring and performance analysis of scientific workflows in Grids. This paper describes novel Grid services for dynamic instrumentation of Grid-based applications, performance monitoring and analysis of Grid scientific workflows. We describe a Grid dynamic instrumentation service that provides a widely accessible interface for other services and users to conduct the dynamic instrumentation of Grid applications during the runtime. We introduce a Grid performance analysis service for Grid scientific workflows. The analysis service utilizes various types of data including workflow graphs, monitoring data of resources, execution status of activities, and performance measurements obtained from the dynamic instrumentation of invoked applications, and provides a rich set of functionalities and features to support the online monitoring and performance analysis of scientific workflows. Workflows and their relevant information including performance metrics are stored and utilized for comparing the performance of constructs of different workflows and for supporting multi-workflow analysis. The work described in this paper is supported in part by the Austrian Science Fund as part of the Aurora Project under contract SFBF1104 and by the European Union through the IST-2002-511385 project K-WfGrid.     

Performance metrics and ontologies for Grid workflows

Many Grid workflow middleware services require knowledge about the performance behavior of Grid applications/services in order to effectively select, compose, and execute workflows in dynamic and complex Grid systems. To provide performance information for building such knowledge, Grid workflow performance tools have to select, measure, and analyze various performance metrics of workflows. However, there is a lack of a comprehensive study of performance metrics which can be used to evaluate the performance of a workflow executed in the Grid. Moreover, given the complexity of both Grid systems and workflows, semantics of essential performance-related concepts and relationships, and associated performance data in Grid workflows should be well described. In this paper, we analyze performance metrics that performance monitoring and analysis tools should provide during the evaluation of the performance of Grid workflows. Performance metrics are associated with multiple levels of abstraction. We introduce an ontology for describing performance data of Grid workflows and illustrate how the ontology can be utilized for monitoring and analyzing the performance of Grid workflows.