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。     

一个生物计算网格的设计与实现*

提出一种基于OGSA的生物计算网格体系结构,分析研究了生物应用标准化、资源描述、作业描述和作业调度等,对序列拼接应用Phrap在该网格环境下的运行情况进行了讨论。     

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。     

计算网格资源管理的并行实现

对网格资源的含义及目的作了介绍，给出了资源管理的实现过程。在计算网格资源管理模型Globus的基础上，提出了网格资源管理中作业管理的并行化，对各个管理部分做了具体的描述。提出了作业并行分析器，实现作业管理的并行化，在一定程度上缩短了作业管理的时间，提高了作业管理的效率。其中对作业并行分析器进行了详尽的描述，使其根据各任务的依赖关系将作业中的任务划分为不同的任务组，并对每个任务组进行适当描述后提交给资源分配器。     

基于网格市场模型的动态资源描述的研究

首先分析了原有资源描述语言在进行动态资源描述时的局限性,并且阐述了市场模型下对动态资源描述提出的新要求。为了能体现资源描述的市场特性和动态性,在原有资源描述语言RSL、RDF和JDL基础上,作了相应的扩展和修改,从而实现在市场模型下对资源进行更加动态、灵活的描述。     

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。     

基于计算期望的网格资源管理模型

网格环境下的计算资源具有分布性,异构性,动态性的特征,并分别属于不同的组织,使网格资源管理相对分布式计算环境或集群环境下的资源管理更加复杂.在计算经济模型的基础上,提出了一种基于计算期望的网格资源管理模型.模型将计算任务抽象成对计算期望的描述,以实现灵活高效的网格资源管理和调度.通过可变价格策略,反映了网格内资源的供求关系,并对基于预留的Qos保证进行了初步的讨论.     

网格资源管理体系结构模型研究

网格中的资源是在地理上分布的、异构的,并且由多个组织所拥有的具有不同的使用、访问及消费模型的资源。在如此巨大而又分布式的环境中对资源进行管理是一项十分复杂的任务。该文介绍了并讨论了目前主要的三种网格资源管理体系结构模型及其在网格计算系统中的应用。分层模型是目前大多数网格计算系统进行资源管理时所使用的模型;抽象所有者模型在作业提交和结果收集时遵循定购和交货模式;计算经济模型综合了分层模型和抽象所有者模型的实质,并且体现了计算经济在网格资源管理系统中的应用。最后,分析了三种模型的特性并对文章进行总结。     

一种基于网格的分布计算协同机制

通过对网格计算中资源协同调度机制的研究,根据该机制的资源协同分配需求,提出一个以Globus Toolkit为平台的基于网格计算的分布协同计算模型。在基于经济模型的作业调度机制的竞价机制中,给出一个较为完善的价格函数模型以及相应的作业预测完成时间模型,利用设计的价格函数模型实现资源提供的主观能动性,使得资源的调配更加合理、快速。同时,在分布式计算模型中还给出一个基于备份的作业调度容错机制。     

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。     

Using IDS fitted Q to develop a real-time adaptive controller for dynamic resource provisioning in Cloud's virtualized environment

Reinforcement learning (RL) is a powerful solution to adaptive control when no explicit model exists for the system being controlled. To handle uncertainty along with the lack of explicit model for the Cloud's resource management systems, this paper utilizes continuous RL in order to provide an intelligent control scheme for dynamic resource provisioning in the spot market of the Cloud's computational resources. On the other hand, the spot market of computational resources inside Cloud is a real-time environment in which, from the RL point of view, the control task of dynamic resource provisioning requires defining continuous domains for (state, action) pairs. Commonly, function approximation is used in RL controllers to overcome continuous requirements of (state, action) pair remembrance and to provide estimates for unseen statuses. However, due to the computational complexities of approximation techniques like neural networks, RL is almost impractical for real-time applications. Thus, in this paper, Ink Drop Spread (IDS) modeling method, which is a solution to system modeling without dealing with heavy computational complexities, is used as the basis to develop an adaptive controller for dynamic resource provisioning in Cloud's virtualized environment. The performance of the proposed control mechanism is evaluated through measurement of job rejection rate and capacity waste. The results show that at the end of the training episodes, in 90 days, the controller learns to reduce job rejection rate down to 0% while capacity waste is optimized down to 11.9%.     

两种形式语言:RSL与Z的分析比较

RSL(RAISE规格说明语言)和Z是目前广泛应用的软件规格说明语言,本文从软件开发生命周期的角度对两种语言进行了比较,提出了将不同规格说明语言结合形式地描述系统的设想。     

一个面向JSD方法的规格说明语言转换系统

ＪＳＬＣ系统是一个规格说明语言ＪＳＬ的转换系统．它的实现反映了一种规格说明语言的转换方法．本文详细介绍了ＪＳＬＣ系统的组成结构、实现基础和技术要点．     

A self-adaptive Model based on Multi-Objective Programming for Grid Resource Management

Abstract : The growing computational power requirements of grand challenge applications have positioned computational grid as promising next generation computing platform.However, resource management and application with varied requirements in grid environment continue to be a complex undertaking. In order to address complex resource management issues, we provide a self-adaptive model, which is based on multi-objective programming. The model make use of virtues of market mechanism efficiently, meanwhile, the shortcomings of market mechanism, such as too frequent fluctuations of price,are avoided by means of the method of changing prices after trading. Through using atom allocation of resource group, the cooperating allocation is improved, and some problems, such as deadlock of resource and inefficiently occupying resource, are solved. What' s more important, efficiently using various resources in grid system is guaranteed through importing multi-objective programming mechanism in our resource management solution. A frame of resource allocation is given at first, then, the mathematical model of the method is constructed. An algorithm is proposed to get the approximate solution in this paper.     

Multi-objective flexible job shop schedule: Design and evaluation by simulation modeling

Flexible job shop schedule is very important in both fields of combinatorial optimization and production management. In this paper, a simulation model is presented to solve the multi-objective flexible job shop scheduling problem. The proposed model has been coded by Matlab which is a special mathematical computation language. After modeling the pending problem, the model is validated by five representative instances based on practical data. The results obtained from the computational study have shown that the proposed approach is a feasible and effective approach for the multi-objective flexible job shop scheduling problem.     

基于Python的求职信息采集分析系统设计与实现

为了便于求职者和招聘单位更精准地把握当前就业市场的变化与需求,提出了基于Python语言的求职信息采集分析系统,利用Scrapy分布式爬虫获取招聘信息,利用数据挖掘技术分析求职者意向,最后将数据清理并格式化后进行可视化展示。所得出的数据模型可以帮助求职者有效的评估出合适岗位和薪资,从而提高求职者的求职效率,同时招聘单位也可以通过此模型对招聘市场的趋势进行总体把握,提高企业的竞争力。     

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。     

An SCP-based heuristic approach for scheduling distributed data-intensive applications on global grids

Data-intensive Grid applications need access to large data sets that may each be replicated on different resources. Minimizing the overhead of transferring these data sets to the resources where the applications are executed requires that appropriate computational and data resources be selected. In this paper, we consider the problem of scheduling an application composed of a set of independent tasks, each of which requires multiple data sets that are each replicated on multiple resources. We break this problem into two parts: one, to match each task (or job) to one compute resource for executing the job and one storage resource each for accessing each data set required by the job and two, to assign the set of tasks to the selected resources. We model the first part as an instance of the well-known Set Covering Problem (SCP) and apply a known heuristic for SCP to match jobs to resources. The second part is tackled by extending existing MinMin and Sufferage algorithms to schedule the set of distributed data-intensive tasks. Through simulation, we experimentally compare the SCP-based matching heuristic to others in conjunction with the task scheduling algorithms and present the results.