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。     

改进的自动回归网格主机负载预测模型

网格计算中对资源的有效预测能很好的改进任务分配和作业调度的策略，提高它们的执行效率，作为网格资源预测的核心？主机负载的预测显得尤为重要。文中提出了一种基于AR改进的主机负载预测模型，它不仅具有AR模型本身的计算成本小、预测性能稳定的优点，还对AR模型只对未来某个固定时间段的负载预测进行了改进，使之能根据作业的预测执行时间进行主机负载动态预测，同时该改进模型还充分体现了主机负载变化的自相似性和长期依赖性，实验结果表明，该模型达到了预期的效果。     

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.     

基于资源预测的网格任务调度模型

跨越虚拟组织中多个域(或集群)的网格任务调度由于资源的不确定性（如动态性和异构性）而成为网格应用中亟待解决的问题。提出了一种有效的基于资源预测的网格任务调度模型——RPTS,该模型利用加权最小二乘方法进行参数估计的自回归滑动平均(ARMA)预测方法对网格环境下的主机负载进行预测。利用上述资源预测结果和一类数据并行性网格任务的建模结果,对它们进行预处理、匹配并调度执行。RPTS充分考虑了网格环境下资源的动态性和异构性,为解决网格环境下任务调度问题提供了一种较好的方法。与其他一些网格任务调度方法进行了一系列的仿真实验,结果表明RPTS模型具有任务执行时间最短和稳定性较好的特点。     

基于动态遗传算法的网格计算任务调度

针对网格任务调度的动态特性,提出一种改进的遗传算法——动态遗传算法(DGA),设计了新的编码机制和适应度函数,以及相应的选择、交叉和变异算子。根据网格系统各服务节点的计算能力、负载及网络状态进行动态调度,不仅使总的完成时间最短,尽量使主机的空闲时间最短,同时满足每个任务的截止时间的要求。在OPNET环境中构建了一个局部网格仿真模型,对所提出的动态遗传算法进行了仿真实验,并与其他常见网格任务调度算法进行了对比,结果表明动态遗传算法具有很好的优化能力,提供了较好的服务质量。     

基于骨架的结构化网格编程模型

为了解决网格编程中可编程性和性能之间的冲突,提出一种基于骨架的结构化网格编程模型。将结构骨架的性能预测模型引入到网格编程的早期阶段,使用远程求值的机制进行结构骨架参数的远程传递。实验结果证明,该模型可克服传统方法引起的性能问题,且能有效提高网格软件的开发效率和可重用性。     

基于曲面三角形网格模型的数控编程技术

本文介绍了曲面数控加工编程方法,提出了基于曲面三角形网格模型的数控编程方法。实验结果表明,该方法能有效地解决复杂曲面的数控加工问题。     

基于曲面三角形网格模型的数控编程技术

本文介绍了曲面数控加工编程方法，提出了基于曲面三角形网格模型的数控编程方法，实验结果表明，该方法能有效地解决复发曲面的数控加工问题。     

一种基于自动回归的改进网格主机负载预测模型

提出了一种基于自动回归（AR）改进的主机负载预测模型,它不仅具有AR模型本身的计算成本小、预测性能稳定的优点,还对AR模型只对未来某个固定时间段的负载预测进行了改进,使之能根据作业的预测执行时间进行主机负载动态预测,同时该模型还充分体现了主机负载变化的自相似性和长期依赖性。实验结果表明,该模型达到了预期的效果。     

基于Hadoop的海量网格数据建模

针对网格实验的实际需要和现有网格仿真工具存在的不足,提出了一种结合Hadoop技术进行海量网格数据建模的方法。利用提出的建模方法,研究人员可以从海量数据中挖掘出实验所需核心数据,并建立这些数据所满足的数学模型。在网格仿真实验中使用这些数学模型生成网格负载,将会提高网格仿真实验的准确性和可信度。     

Design, implementation, and performance of an extensible toolkit for resource prediction in distributed systems

RPS is a publicly available toolkit that allows a practitioner to straightforwardly create flexible online and offline resource prediction systems in which resources are represented by independent, periodically sampled, scalar-valued measurement streams. The systems predict the future values of such streams from past values and are composed at runtime out of a large and extensible set of communicating components that are in turn constructed using RPS's extensible sensor, prediction, wavelet, and communication libraries. This paper describes the design, implementation, and performance of RPS. We have used RPS extensively to evaluate predictive models and build online prediction systems for host load, Windows performance data, and network bandwidth. The computation and communication overheads involved in such systems are quite low.     

基于基因表达式编程的电力负荷预测算法

针对电力负荷预测的特点,本文结合基因表达式编程的思想,设计电力负荷预测基因和适应度函数,在此基础上,提出基于基因表达式编程的电力负荷预测算法(Power Load Forecasting based on Gene Expression Programming,PLF-GEP)。仿真实验表明,PLF-GEP算法的预测精度与实际值之间的误差率最小约1%,大大提高了电力负荷预测的精度。     

网格环境中节点CPU负载的分形预测

网格环境下，常常需要知道网格资源在未来某一时刻具有什么样的性能，比如，调度器需要该性能估测以便进行高效的资源调度、提供满足要求的QoS以及保证整个网格系统的负载平衡。正如在其他任何计算环境中一样，计算能力是所有网格资源中最为重要的资源，通常用CPU负载来刻画节点主机的忙碌程度、衡量节点所能提供的计算能力。已有的研究表明CPU负载具有自相似性和长相关性，这启发我们使用本文介绍的分形的方法进行CPU负载的预测。实验结果证明该方法具有较高的预测精度，因而具有较好的实用价值。     

Electric vehicles charging control in a smart grid: A model predictive control approach

The paper presents an event driven model predictive control (MPC) framework for managing charging operations of electric vehicles (EV) in a smart grid. The objective is to minimize the cost of energy consumption, while respecting EV drivers' preferences, technical bounds on the control action (in compliance with the IEC 61851 standard) and both market and grid constraints (by seeking the tracking of a reference load profile defined by the grid operator). The proposed control approach allows “flexible” EV users to participate in demand side management (DSM) programs, which will play a crucial role in improving stability and efficiency of future smart grids. Further, the natural MPC formulation of the problem can be recast into a mixed integer linear programming problem, suitable for implementation on a calculator. Simulation results are provided and discussed in detail.     

一种基于统计思想的网格负载平衡调度算法

针对层次化网格模型结构,运用统计思想提出了一种新的资源分配与任务调度算法,不仅能够提高资源的利用率和系统的吞吐率,而且能够实现网格系统内部的负载平衡。算法主要包含三个功能模块,即负载跟踪模块、作业分配模块和负载监视模块。在解释了方案中各功能部件的作用及其相互之间关系的基础上,给出了相应的算法伪码。仿真实验表明,该算法是有效的。     

Security Risk Assessment of Cyber Physical Power System Based on Rough Set and Gene Expression Programming

Risk assessment is essential for the safe and reliable operation of cyber physical power system. Traditional security risk assessment methods do not take integration of cyber system and physical system of power grid into account. In order to solve this problem, security risk assessment algorithm of cyber physical power system based on rough set and gene expression programming is proposed. Firstly, fast attribution reduction based on binary search algorithm is presented. Secondly, security risk assessment function for cyber physical power system is mined based on gene expression programming. Lastly, security risk levels of cyber physical power system are predicted and analyzed by the above function model. Experimental results show that security risk assessment function model based on the proposed algorithm has high efficiency of function mining, accuracy of security risk level prediction and strong practicality.      

基于预测函数模型的APF补偿电流控制研究

为了有效地改善电网电流中因接入非线性负载所引入的谐波分量和削弱控制系统的延时特点,提出了一种基于预测函数模型的有源电力滤波器(APF)补偿电流控制方法,由当前时刻采样数据和最近历史时刻的数据进行构建预测函数模型,实现了有源滤波器谐波补偿电流的预测控制.仿真结果表明:该控制方法不仅对负载电流有精确的预测能力,且对系统电流中谐波电流具有较好的抑制效果和补偿精度.     

Model predictive control of a permanent magnet synchronous motor with experimental validation

This paper develops new results on the use of model predictive control to regulate the speed of a permanent magnet synchronous motor where the design is based on a linearized state-space representation of the nonlinear model that describes the dynamics. The model predictive controller employs integral action, resulting in zero steady-state error and load torque disturbance rejection. Design in the presence of constraints is considered, where a quadratic programming procedure is used to solve the resulting on-line optimization problem during experimental verification by application to an industry-sized motor.     

A technical note on the paper “hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems”

This paper provides a corrected formulation to the mixed integer programming model proposed by Aydogan et al. (2012) [1]. They proposed a genetic algorithm to learn fuzzy rules for a fuzzy rule-based classification system and developed a Mixed Integer Programming model (MIP) to prune the generated rules by selecting the best set of rules to maximize predictive accuracy. However, their proposed MIP formulation contains errors, which are described in this technical note. We develop corrections and improvements to the original formulation and test it with non-parametric statistical tests on the same data sets used to evaluate the original model. The statistical analysis shows that the results of the correction formulation are significantly different from the original model.