基于多任务拍卖的资源调度算法

资源调度是计算网格资源管理系统的主要内容之一。文中借鉴市场模型中的拍卖机制，通过用户代理、资源代理、拍卖师和资源的交互作用，设计并实现了一个基于多任务拍卖的网格资源调度原型系统，系统使用多拍卖师结构，提高了作业的吞吐率并且可以有效地防止拍卖过程中双方的欺骗行为，改进了网格资源的调度性能。同时，与传统算法相比，该算法能够有效地配置资源和满足用户的服务质量需求。     

基于拍卖机制的网格资源调度性能的提高

资源调度是计算网格资源管理系统的最主要内容之一。基于计算经济的计算网格体系结构设计并实现了一个多拍卖师结构的网格资源调度原型系统。系统提高了作业的吞吐率并且可以有效地防止拍卖过程中双方的欺骗行为,改进了网格资源的调度性能,提高了资源的利用率。     

一种支持服务网格的动态负载平衡系统

为了实现服务网格系统内负载的均衡分布,提高资源利用率和系统的吞吐率,设计并实现了一种基于服务网格环境的动态负载平衡系统。提出了层次式负载平衡调度模式,给出了本系统结构形式,设计并实现了一种综合考虑各局部代理作业数和各个局部代理性能以及当前的负载情况的动态双阈值作业分配算法。实验结果表明,此算法能有效地基于负载分派作业,达到了提高网格内分布资源的利用率和减少作业调度时间的目的。     

基于计算经济的网格资源管理研究

网格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,资源管理是网格的关键技术之一。但是,资源的分布性、异构性、自治性、动态性等使得网格资源的管理变得异常复杂。目前,基于市场的经济资源管理和调度算法非常适合解决网格中的资源管理问题。本文提出了网格环境下基于经济模型的各种代理,给出了一种新的资源管理模型
型,并定义了效用函数,给出了基于效用最优的资源调度算法。为解决网格资源管理的问题提供了一个有效的途径。     

基于多物品拍卖的网格映射算法设计与模拟

由于网格底层资源的异构、广域分布、自治等特性和网格高层的服务质量 (QoS)要求,使得网格资源映射算法的设计和模拟具有相当的难度。提出了基于多物品拍卖的网格资源映射算法,解决了网格系统中相互独立任务模式的资源映射问题。同时对比了当前的网格资源映射模拟工具,阐述了利用GridSim建立基于多物品拍卖的资源映射算法模拟平台的主要步骤。最后模拟实验表明此算法具有优良性能。     

一种基于历史信息的自适应动态网格作业调度方法

目前,国内外围绕着网格中的作业调度算法已做了大量研究,先后提出了很多调度算法.但是,这些算法并不能很好地适应网格的动态性、自治性和分布性等特征.对此,提出了一种动态的网格作业调度方法-基于历史信息的自适应动态网格作业调度方法ASHI.该方法利用每个资源上最近作业的执行信息自适应调整预测模型,然后再根据网格的动态性和实时性等因素,对资源进行反馈选择后将作业提交负载较轻的资源上执行.实验证明,ASHI不但能及时有效地对作业进行调度,而且还可有效提高整个网格的吞吐量和均衡系统的负载.     

网格环境下基于信任机制的资源调度研究

信任是网格资源调度中一个很重要的因素,也是影响网格计算有效性和性能的关键技术之一。将信任机制引入到网格资源调度中,提出了网格环境下的信任模型和基于信任机制的资源调度模型,在调度策略上对传统的Min-Min算法进行了改进,提出了基于信任机制的Trust-Min-Min算法。仿真结果表明,算法不仅可以缩短任务的总执行时间,而且可以有效地平衡负载,是网格环境下一种有效的资源调度方法。     

网格计算中工作流调度的非合作博弈优化*

在异构网络计算问题中,网格计算方法通过引入资源共享机制,可解决复杂的计算任务。然而在网格环境中,需要对网络可获得的资源进行合理调度和协调,才可以获得良好的网络工作流,以及合适的网络性能和网络响应时间。为了提高网格计算方法的任务调度和资源分配的能力和性能,提出了一种基于非合作博弈方式的博弈模型。该模型通过设定使用户的资源分配所需时间和代价降低的解来增加代理的利润,激励资源代理使用一种优化调度算法,使资源调度的时间和代价都最小。仿真结果表明了该模型的可行性和适用性,并且基于该模型的遗传算法是最好的资源调度算法。     

一种基于最短完成时间的网格计算任务调度算法的研究

资源调度技术是网格核心服务之一。良好的资源调度能有效地协调和分配网格资源,有效降低网格计算的总执行时间和总耗费量,从而使网格计算达到最佳性能。本文就网格计算中一种基于最短完成时间的任务调度算法进行研究和阐述。     

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。     

基于计算经济模型改善网格资源分发和发现的性能

该文在基于计算经济的计算网格体系结构Nimrod/G的基础上,结合NWS设计并实现了一个网格资源分发和发现原型系统,实验表明:该系统可以有效地改进网格系统中的资源分发和发现的性能,进一步提高了网格资源的管理和调度的性能。     

A fault-tolerant scheduling system for computational grids

Fault-tolerant scheduling is an important issue for computational grid systems, as grids typically consist of strongly varying and geographically distributed resources. The main scheduling strategy of most fault-tolerant scheduling systems depends on the response time and fault index when selecting a resource to execute a certain job.In this paper, a scheduling system is presented that depends on a new factor called scheduling indicator in selecting resources. This factor comprises of the response time and the failure rate of grid resources. Whenever a grid scheduler has jobs to schedule on grid resources, it uses the scheduling indicator to generate the scheduling decisions. The main scheduling strategy of the system is to select resources that have the lowest tendency to fail. Extensive simulation experiments are conducted to quantify the performance of the proposed system. Experiments have shown that the proposed system can considerably improve grid performance in terms of throughput, unavailability, turnaround time, and fail tendency.     

基于网格计算市场模型的资源管理和调度的研究

网格计算市场模型是把经济学的概念应用到网格资源管理和调度的模型。基于计算市场模型的网格资源管理系统借鉴人类社会竞争的市场调节机制，根据用户的经济需求进行资源管理与任务调度，不仅使资源所有者和资源消费者都能实现各自的经济目标，而且使资源消费者使用轻负栽和廉价的资源，达到整个网格资源整体的全局最优及合理利用。     

基于遗传编程的网格资源调度算法

网格资源调度是一个非常重要的研究课题。由于因特网的开放、动态性,传统的资源调度和分配方法已经不再适用网格计算,基于经济模型的资源管理和调度成为研究热点。在计算市场模型中,构造有效的效益函数又是提高算法性能的关键。有关文献中采用的是线性效益函数,虽然降低了复杂度,但不能很好地反映用户的效益。文中提出了基于遗传编程来寻找和构造非线性效益函数的方法,并将其应用到网格调度算法中。实验结果表明该算法可以提高网格中的资源调度性能。     

基于遗传编程的网格资源调度算法

网格资源调度是一个非常重要的研究课题.由于因特网的开放、动态性,传统的资源调度和分配方法已经不再适用网格计算,基于经济模型的资源管理和调度成为研究热点.在计算市场模型中,构造有效的效益函数又是提高算法性能的关键.有关文献中采用的是线性效益函数,虽然降低了复杂度,但不能很好地反映用户的效益.文中提出了基于遗传编程来寻找和构造非线性效益函数的方法,并将其应用到网格调度算法中.实验结果表明该算法可以提高网格中的资源调度性能.     

渲染网格调度策略研究

针对渲染网格的特点提出了渲染网格的二个调度模型.模型一提出了在局部资源的计算结点内,采用基于用户优先级的CPU频度轮转调度方案,达到提高系统吞吐率的目的.模型二提出了适合市场机制的渲染网格调度模型,并给出了竞标的报价算法.该算法在公共网格开放软件Globus Toolkit4.0上实现,符合国际标准WSRF规范.实验结果表明本调度策略在处理渲染网格任务时性能得到了很好的体现.     

An agent-based approach for dynamic adjustment of scheduled jobs in computational grids

Grid computing is a newly developed technology for complex systems with large-scale resource sharing, wide-area communication, and multi-institutional collaboration. Grid scheduling is an important infrastructure in the grid computing environment. Most of the existing grids scheduling methods focus on maximizing processor utilization without taking grid load into consideration. This may lead to significant inefficiencies in performance such as large job queues and processing delays. In this paper, we propose a multiagent-based scheduling system for computational grids with a new approach. Agent technology is suitable for a computational grid because of the dynamic, heterogeneous, and autonomous nature of the grid. The main idea of the proposed system is a combination of a static scheduling using a fixed scheduling algorithm and a dynamic adjustment through the autonomous behavior of agents. The superiority of the proposed system, in reducing the load of the grid and minimizing the response time for executing user applications, is demonstrated by simulation experiments.     

An efficient grid-scheduling strategy based on a fuzzy matchmaking approach

Computational grids have become an appealing research area as they solve compute-intensive problems within the scientific community and in industry. A grid computational power is aggregated from a huge set of distributed heterogeneous workers; hence, it is becoming a mainstream technology for large-scale distributed resource sharing and system integration. Unfortunately, current grid schedulers suffer from the haste problem, which is the schedule inability to successfully allocate all input tasks. Accordingly, some tasks fail to complete execution as they are allocated to unsuitable workers. Others may not start execution as suitable workers are previously allocated to other peers. This paper is the first to introduce the scheduling haste problem. It also presents a reliable grid scheduler. The proposed scheduler selects the most suitable worker to execute an input grid task using a fuzzy inference system. Hence, it minimizes the turnaround time for a set of grid tasks. Moreover, our scheduler is a system-oriented one as it avoids the scheduling haste problem. Experimental results have shown that the proposed scheduler outperforms traditional grid schedulers as it introduces a better scheduling efficiency.     

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.     

An efficient system-oriented grid scheduler based on a fuzzy matchmaking approach

Computational grids have become an appealing research area as they solve compute-intensive problems within the scientific community and in industry. A Grid computational power is aggregated from a huge set of distributed heterogeneous workers; hence, it is becoming a mainstream technology for large-scale distributed resource sharing and system integration. Unfortunately, current grid schedulers suffer from the haste problem, which is the schedule inability to successfully allocate all input tasks. Accordingly, some tasks fail to complete execution as they are allocated to unsuitable workers. Others may not start execution as suitable workers are previously allocated to other peers. This paper is the first to introduce the scheduling haste problem. It also presents a reliable grid scheduler. The proposed scheduler selects the most suitable worker to execute an input grid task using a fuzzy inference system. Hence, it minimizes the turnaround time for a set of grid tasks. Moreover, our scheduler is a system-oriented one as it avoids the scheduling haste problem. Experimental results have shown that the proposed scheduler outperforms traditional grid schedulers as it introduces a better scheduling efficiency.