基于多Agent的Web负载测试框架研究*

提出了一种基于角色的多代理Web负载测试框架,其核心的三种角色代理均采用一种扩展的BDI代理模型。BDI代理中可动态加载能力组件的功能显著地提高了Web测试的包容性和可扩展性。该框架描述了大量执行不同测试任务的Agent和Agent集,以及支持它们相互通信的Agent通用平台。最后通过一个简单的原型系统,对该模型的性能和可行性进行了验证。     

基于多Agent的Web负载测试框架研究

提出了一种基于角色的多代理Web负载测试框架，其核心的三种角色代理均采用一种扩展的BDI代理模型。BDI代理中可动态加载能力组件的功能显著地提高了Web测试的包容性和可扩展性。该框架描述了大量执行不同测试任务的Agent和Agent集，以及支持它们相互通信的Agent通用平台。最后通过一个简单的原型系统，对该模型的性能和可行性进行了验证。     

移动网格体系结构及其资源选择方法

针对移动计算和网格计算的发展现状及其融合趋势,在分析现有移动网格架构的基础上,提出一种新的移动网格的集成框架,包括基于资源代理、服务代理和网关的移动网格的体系结构和逻辑构成,探讨了移动网格资源的基于客户请求的资源选择和分配办法,并列举了移动网格的应用实例。结果表明,通过代理和网关可充分利用移动网络的灵活性和有线网格的资源丰富性,实现移动网格资源的无缝访问和共享。     

基于效用最优的网格计算资源调度算法

基于市场机制提出了一种以资源代理为基础面向服务的网格资源管理模型--SBAGRM,在该模型的框架下提出了一种基于效用的网格资源调度算法,该算法以满足用户的QOS需求为出发点,旨在追求系统资源的全局最优化。     

基于风险策略的多单元连续双向拍卖的网格资源分配

针对计算网格资源的特点,提出一种基于风险策略的多单元连续双向拍卖的网格资源分配机制,实现对网格资源灵活有效的管理。首先,介绍了基于多单元连续双拍卖的网格资源分配框架。其次,针对计算网格资源的有限性,提出了RB2-MCDA机制。RB2-MCDA机制是在多单元连续双向拍卖中,代理采用Risk-Based2策略进行资源交易。Risk-Based2策略是一种基于风险行为的代理策略。实验结果表明,在不同规模的有限资源的计算网格中采用RB2-MCDA机制能够实现较高的资源分配效率,当资源需求量接近供给量时,分配效率超过99%。     

基于Agent的网格资源管理系统的研究与设计

网格资源管理是网格计算中的重要组成部分和研究难点。本文分析了网格资源管理系统的体系结构模型，提出了基于代理的方法来管理网格资源系统，设计了基于Agent的网格资源管理系统的体系结构和工作流程，并应用该方法分析了资源请求过程的实例。同时，为了适应网格系统的可扩展性，我们应用多代理技术解决了代理之间的相互协作的问题。文中所提出的资源管理方案可为网格资源管理提供一种有效的方法和途径。     

一种基于市场机制的计算网格资源分配方法

针对计算网格提出了一种基于市场机制的资源分配方法，以一般均衡理论为基础，依靠市场机制，实现计算网格资源的优化分配，首先，描述了基于代理的资源分配框架，它包括3个 层次：资源层、代理层和用户层；接着，给出了计算网络资源分配的市场模型，其中效用函数用于刻画用户对给定资源的满意程度；然后定义了市场模型的均衡状态并证明了均衡状态撮优性，这意味着在均衡状态下资源分配不仅有效而且公平，最后引入了资源代理的迭代算法。     

网格资源管理模型研究

文章分析了网格资源管理中的三种模型:传统的资源管理,基于代理的资源管理和基于经济学的资源管理。以Globus为例讨论了传统的资源管理方法;在基于代理的资源管理模型中,分析了基于代理的资源发现与监控和基于代理的资源调度;在基于经济学的资源管理中,分析了7种常见的模型。最后,文章提出WSRF规范的出现将给网格资源管理技术带来新的变化。     

基于Java的网格计算框架及其实现

为了提高网格计算的可靠性,适应大规模计算的要求,采用了一种基于Java的分布式网格计算框架,它利用移动部署代理管理计算任务,可以最大限度地减轻中心主机的负担,并通过在Java运行环境中引入两个组件,解决了用Java构建网格计算时存在的安全和资源管理问题,是一种理想的基于Internet的网格应用项目,具有良好的实用性和推广价值。     

一种基于效用函数的网格资源优化策略*

基于市场机制提出了一种以资源代理为基础、面向服务的网格资源管理模型——SBAGRM,在该模型的框架下提出了一种基于效用函数的网格资源分配方法,该方法以满足用户的QoS需求为出发点,旨在追求系统资源的全局最优化。SBAGRM模型可以避免非线性优化带来过高的计算复杂度,以市场模式根据效用函数配置资源,因此计算复杂度将大大降低,模拟结果显示性能明显提高。     

Design and implementation of economics-based resource management system in ad hoc grid

The ad hoc grid is a spontaneous organization of cooperating heterogeneous nodes into a logical community without a fixed infrastructure and with only minimal administrative requirements. Resource management for ad hoc grids is challenging due to the participation of heterogeneous, dynamic, autonomous and ephemeral grid nodes. The paper proposes an ad hoc grid resource management system, the producers and consumers of ad hoc grid resource are modeled as the self-interested decision-makers described in microeconomic theory. All market participants in the ad hoc grid environment including grid resources and services can be represented as agents. We apply economic agents to build ad hoc grid resource management, where ad hoc grid resource consumers and providers can buy and sell ad hoc grid resource based on an underlying economic architecture. The main processes involved in ad hoc grid resource management are resource registration, discovery, and resource allocation. The experiments are conducted to compare ad hoc grid resource allocation algorithm with other ad hoc grid resource allocation algorithm. Simulation results show that our proposed algorithm is more efficient than compared allocation scheme.     

基于智能多代理的工作流资源分配

提出了一种基于智能多代理实现分布式工作流资源分配的方法。该方法采用KQML语言来设计资源分配代理和工作流引擎之间面向知识交换的通信协议,并且由Router提供消息存储和转发机制。工作流资源分配代理与工作流管理系统的其他代理程序通过KQML消息交流知识,从而达到在分布式、异构环境下的互操作。     

Collaborative assignment using belief-desire-intention agent modeling and negotiation with speedup strategies

In this paper, we propose a distributed agent model that applies belief-desire-intention (BDI) reasoning and negotiation for addressing the linear assignment problem (LAP) collaboratively. In resource allocation, LAP is viewed as seeking a concurrent allocation of one different resource for every task to optimize a linear sum objective function. The proposed model provides a basic agent-based foundation needed for efficient resource allocation in a distributed environment. A distributed agent algorithm that has been developed based on the BDI negotiation model is examined both analytically and experimentally. To improve performance in terms of average negotiation speed and solution quality, two initialization heuristics and two different reasoning control strategies are applied, with the latter yielding different variants of the basic algorithm. Extensive simulations suggest that all the heuristic-algorithm combinations can produce a near optimal solution soon enough in some specific sense. The significance and applicability of the research work are also discussed.     

A distributed multiple dimensional QoS constrained resource scheduling optimization policy in computational grid

This paper is to solve efficient QoS based resource scheduling in computational grid. It defines a set of QoS dimensions with utility function for each dimensions, uses a market model for distributed optimization to maximize the global utility. The user specifies its requirement by a utility function. A utility function can be specified for each QoS dimension. In the grid, grid task agent acted as consumer pay for the grid resource and resource providers get profits from task agents. The task agent' utility can then be defined as a weighted sum of single-dimensional QoS utility function. QoS based grid resource scheduling optimization is decomposed to two subproblems: joint optimization of resource user and resource provider in grid market. An iterative multiple QoS scheduling algorithm that is used to perform optimal multiple QoS based resource scheduling. The grid users propose payment for the resource providers, while the resource providers set a price for each resource. The experiments show that optimal QoS based resource scheduling involves less overhead and leads to more efficient resource allocation than no optimal resource allocation.     

Agent based sensors resource allocation in sensor grid

Sensor enabled grid may combine real time data about physical environment with vast computational resources derived from the grid architecture. One of the major challenges of designing a sensor enabled grid is how to efficiently schedule sensor resource to user jobs across the collection of sensor resources. The paper presents an agent based scheme for assigning sensor resources to appropriate sensor grid users on the basis of negotiation results among agents. The proposed model consists of two types of agents: the sensor resource agents that represent the economic interests of the underlying sensor resource providers of the sensor grid and the sensor user agents that represent the interests of grid user application using the grid to achieve goals. Interactions between the two agent types are mediated by means of market mechanisms. We model sensor allocation problems by introducing the sensor utility function. The goal is to find a sensor resource allocation that maximizes the total profit. This paper proposes a distributed optimal sensor resource allocation algorithm. The performance evaluation of proposed algorithm is evaluated and compared with other resource allocation algorithms for sensor grid. The paper also gives the application example of proposed approach.     

基于移动Agent的集群企业资源管理技术的实现

针对分布计算环境下企业资源管理中的诸多问题，提出基于移动Agent的集群企业资源的全局整合与动态分配方法和机制，给出了系统的体系结构。提出了基于资源类的资源整合模式和资源类的信息模型，构建了企业集群资源的分级整合。提出了基于移动Agent寻优和基于市场原则的资源分配调度算法。最后给出了S2-ARIMS应用系统。     

Resource allocation on computational grids using a utility model and the knapsack problem

This work introduces a utility model (UM) for resource allocation on computational grids and formulates the allocation problem as a variant of the 0–1 multichoice multidimensional knapsack problem. The notion of task-option utility is introduced, and it is used to effect allocation policies. We present a variety of allocation policies, which are expressed as functions of metrics that are both intrinsic and external to the task and resources. An external user-defined credit-value metric is shown to allow users to intervene in the allocation of urgent or low priority tasks. The strategies are evaluated in simulation against random workloads as well as those drawn from real systems. We measure the sensitivity of the UM-derived schedules to variations in the allocation policies and their corresponding utility functions. The UM allocation strategy is shown to optimally allocate resources congruent with the chosen policies.     

Energy efficient and robust allocation of interdependent tasks on mobile ad hoc computational grid

A mobile ad hoc computational grid is a distributed computing infrastructure that allows mobile nodes to share computing resources in a mobile ad hoc environment. Compared to traditional distributed systems such as grids and clouds, resource allocation in mobile ad hoc computational grids is not straightforward because of node mobility, limited battery power and an infrastructure‐less network environment. The existing schemes are either based on a decentralized architecture that results in poor allocation decisions or assume independent tasks. This paper presents a scheme that allocates interdependent tasks and aims to reduce task completion time and the amount of energy consumed in transmission of data. This scheme comprises two key algorithms: resource selection and resource allocation. The resource selection algorithm is designed to select nodes that remain connected for a longer period, whereas the resource assignment or allocation algorithm is developed to allocate interdependent tasks to the nodes that are accessible at the minimum transmission power. The scheme is based on a hybrid architecture that results in effective allocation decisions, reduces the communication cost associated with the exchange of control information, and distributes the processing burden among the nodes. The paper also investigates the relationship between the data transfer time and transmission energy consumption and presents a power‐based routing protocol to reduce data transfer costs and transmission energy consumption. Copyright © 2014 John Wiley & Sons, Ltd.     

基于Agent的网格资源共享联盟

网格资源分配是网格计算中的关键问题之一,并引起网格研究者越来越多的关注。提出了一种新颖的网格资源分配机制,该机制主要由基于Agent技术的搜索最佳联盟策略和多党协商机制组成,通过形成资源共享联盟来实现网格资源的分配。模拟结果显示计算网格中的自利Agent对网格资源的分配具有很大的影响。     

基于经济模型的资源代理

网格计算是为解决大规模资源密集型问题而提出的新一代计算平台,是当前并行和分布处理技术的一个发展方向,而资源管理是计算网格的关键技术之一。对各种各样可利用资源的整合和管理是网格应用的基础,而资源的分布性、动态性、异构性、自治性和需要协调一致性使得网格资源的管理调度成为一个棘手的问题。目前基于市场的经济资源管理和调度算法非常适合计算网格中的资源管理问题,但有调度价格不能更改、负载平衡等问题。文中提出了“网格环境下基于经济模型的资源代理”,依靠多维QoS指导的调度策略和经济模型的启发式调节资源价格,改进和优化计算网格资源的分配。