计算网格中动态负载平衡的分布调度模式

网格计算下对资源进行有效的管理和调度可以提高系统的利用率.在对现有若干调度方法的研究和分析基础上,针对计算网格中的负载平衡问题,提出了一种分布式网格作业调度模型,并给出相关算法.算法通过建立主从模式的负载信息收集机制,提供给节点全局负载信息,加速重负载节点的负载转移速度.通过有效的负载平衡模式,解决资源调度中负载平衡及其可靠性问题.     

基于移动Agent的网格计算管理模型研究

基于当前的移动aget问题以及网格计算特点,在萌人的理论基础上,文中结合agent技术和网格计算研究基于移动agent的网格计算管理模型,运用系统中各个元素进行agent建模.介绍移动agent技术,以及网格计算管理模型应具备的特征,最后提出基于移动Agent的网格计算管理模型来解决网格中资源管理,作业调度,负载平衡的...     

计算网格调度系统的探讨

本文介绍了网格的概念,详细阐述了计算网格调度系统的分类和性能指标,最后介绍了相关研究工作。     

基于移动代理在网格计算中的结构模型研究

网格计算环境下的资源管理、作业调度和分配、负载平衡等问题成为网格计算发展的障碍。本文介绍了网格计算的体系结构和运用网格计算环境进行作业计算、资源管理、负载平衡等方法存在的问题,详细论述了在网格计算环境下引入移动代理技术对于解决网格计算问题的作用和优点。然后分别对网格计算中的移动Agent结构、资源管理模式、作业分配与调度方法、负载平衡等方面进行了研究。     

一种冗余调度的可靠网格计算模型

为了解决动态、不稳定的网格环境下的可靠计算问题,提出一种基于冗余调度的可靠网格计算模型.首先给出计算网格系统可靠性的定义,并基于系统可靠性定义给出了冗余调度的可靠网格计算模型,设计了冗余调度算法,模拟实验结果证明了提出的模型可以提高计算网格任务调度的可靠性.为了使提出的模型更好应用于实际网格计算环境,给出基于概率的冗余度优化公式,将该公式引入到冗余调度模型,可以获得优化的调度冗余度,不仅可以提高任务调度系统的可靠性,而且能提高资源的利用率.     

一种多集群网格的负载均衡算法

网格环境由于其可扩展性、异构性以及大量的传输延迟,使得网格环境下的负载均衡不同于传统的分布式系统。提出了一种动态的分布式负载均衡算法,该算法综合考虑网格站点的处理能力和站点之间的传输延迟,采用即时分配策略来降低作业的执行成本,目标是使系统平均作业响应时间最小化。仿真结果显示该算法显著减少了作业的平均响应时间。     

网格计算中费用约束的最优时间调度算法

在网格资源处理速度和资源价格异构的网格环境下,讨论基于用户费用约束的最优时间调度问题,提出一种相应的调度算法,将该任务调度问题转化为线性规划问题,采用单纯形算法获得近似最优解,从而获得费用约束下资源的最优执行时间以及该任务的最小完成时间。仿真结果表明,该算法的性能优于其他同类算法。     

基于移动代理网格计算的资源管理模型研究

为了研究基于移动代理的网格计算资源管理模型,介绍了网格计算环境和其体系结构应有的特征,并分析了基于其上的应用层模型应有的特征;根据各种不同的资源管理策略,研究了各种策略的优点和不足,并提出了各种改进方法;同时,提出了将移动代理引入网格计算中以解决资源管理、作业调度等问题的思想.     

网格计算中一种改进的工作流调度算法

网格计算中如何有效地实现工作流的调度问题是目前的研究热点。文中综合考虑了资源节点的动态负载和服务能力,提出了一种改进的调度算法（AWSA）。该算法首先对任务的优先级进行降序排列,然后依次为它们选择具有最大截止时间约束的服务站点作为其候选资源,最后,依据资源站点的任务分配情况和负载变化趋势,白适应地实现从任务资源请求到站点的映射。仿真实验结果表明,文中方法是有效的,在作业拒绝率和作业调度长度方面,AWSA的性能要优于已有的方法。     

容错计算网格作业调度模型的研究

网格技术的发展对网格系统的效率和服务质量提出了更高要求．在综合研究目前网格作业调度环境的基础上,提出一种容错计算网格作业调度的随机Petri网模型,并给出了网格作业分派策略和计算站点内的作业选择策略,以及容错计算网格的性能评价指标．仿真实验对容错计算网格的性能进行有效的分析,反映故障对网格中不同类别作业的影响．     

基于格架模型的角点网格生成算法

三维油藏地质建模是石油勘探与开发领域的一个关键技术。该文在参考国外主流地质建模软件的基础上,提出了一种基于格架网格模型的角点网格生成技术,并成功应用到数字化油藏表征软件DIRECT-Mod中,取得了很好的效果。实验结果证明了该算法的有效性。     

Workload assessment in building construction related activities in India

A field study was conducted to highlight the occupational risk factors related to building construction activities in India among female workers. These workers were engaged in eight different types of activities and related work parameters were studied in detail. From field environmental parameters, the calculated WBGT was obtained as 30.26+/-1.52 degrees C, indicated that these workers worked under a positive heat load condition. Whole day work study was conducted on 11 adult female workers performing concreting operation. They were having age of 28-32 years with 5-7 years of work experience. These workers were mainly performing two types of operations in the field: (A) asymmetric lifting during concreting a boundary wall formwork of a lift unit and (B) carrying the concrete mixture. During asymmetric lifting, the average field working heart rate (HR) was calculated as 124.1+/-12.5 beats min(-1), equivalent to 45.03+/-6.93% of VO(2) max level. These working heart rates (HRs) were significantly (p相似文献   

基于方差检验的并行负载抖动检测

负载抖动是影响负载模型准确性和代表性的关键因素。本文通过两个负载抖动例子给出负载抖动的定义，研究了抖动日志对负载模型的影响情况，提出了一种通过分析总资源向量和最大资源向量的方差检测负载抖动的算法，研究时间刻度参数△t对算法的影响情况，并给出了其较合理的取值范围。     

基于作业进展描述的计算网格作业检查点

分析了计算网格环境中实现检查点机制的特殊性,提出了一种新的应用级检查点方法：基于作业进展描述的检查点方法。介绍了这种检查点方法的基本思想,定义了构成作业进展描述的作业进展状态对象和作业进展描述对象,这些对象的方法构成了检查点API;讨论了检查点作业的构建。     

考务工作量快速统计中的Excel数据应用

考务工作量的统计是整个监考工作的一个重要环节,笔者通过具体的考试安排案例,阐释剖析一下如何利用Excel排序、透视、分列、润色等数据功能,更经济地统计出监考工作量。     

Workload dynamics on clusters and grids

This paper presents a comprehensive statistical analysis of a variety of workloads collected on production clusters and Grids. The applications are mostly computational-intensive and each task requires single CPU for processing data, which dominate the workloads on current production Grid systems. Trace data obtained on a parallel supercomputer is also included for comparison studies. The statistical properties of workloads are investigated at different levels, including the Virtual Organization (VO) and user behavior. The aggregation procedure and scaling analysis are applied to job arrivals, leading to the identifications of several basic patterns, namely pseudo-periodicity, long range dependence (LRD), and multifractals. It is shown that statistical measures based on interarrivals are of limited usefulness and count based measures should be trusted when it comes to correlations. Other job characteristics like run time and memory consumption are also studied. A “bag-of-tasks” behavior is empirically evidenced, strongly indicating temporal locality. The nature of such dynamics in the Grid workloads is discussed. This study has important implications on workload modeling and performance predictions, and points out the need of comprehensive performance evaluation studies given the workload characteristics.

Workload analysis for scientific literature digital libraries

Workload studies of large-scale systems may help locating possible bottlenecks and improving performances. However, previous workload analysis for Web applications is typically focused on generic platforms, neglecting the unique characteristics exhibited in various domains of these applications. It is observed that different application domains have intrinsically heterogeneous characteristics, which have a direct impact on the system performance. In this study, we present an extensive analysis into the workload of scientific literature digital libraries, unveiling their temporal and user interest patterns. Logs of a computer science literature digital library, CiteSeer, are collected and analyzed. We intentionally remove service details specific to CiteSeer. We believe our analysis is applicable to other systems with similar characteristics. While many of our findings are consistent with previous Web analysis, we discover several unique characteristics of scientific literature digital library workload. Furthermore, we discuss how to utilize our findings to improve system performance.     

一种云环境下的主机负载预测方法

云计算是一种基于信息网络的计算模式和服务模式,它将信息技术资源以服务方式动态、弹性地提供给用户,使用户可以按需使用。由于受到主机的启动时间、资源分配时间以及任务调度时间等因素的影响,在云环境下提供给用户的服务存在时延问题。因此,工作负载预测是云环境下一种重要的能源优化的方式。此外,由于云中工作负载的变化具有十分大的波动性,因此增加了预测模型的预测难度。提出了一种基于自回归模型和Elman神经网络的预测模型(Hybrid Auto Regressive Moving Average model and Elman neural network,HARMA-E),其使用ARMA模型进行预测,再使用ENN模型对ARMA模型的误差进行预测,通过修正ARMA的输出值得到最终的预测值。仿真实验结果表明,该预测模型能够较好地提升主机负载预测值的准确度。     

Workload perception in drone flight training simulators

Workload perception was measured in a drone flight training Simulator computerized situation. There has been increasing research in recent years on the topic of Remotely piloted aircrafts (RPA). Eleven participants were tested for workload perception during a drone flight simulator training. Reliability, sensitivity and correlations were studied for the workload scale and its relationship with the simulator training tasks. Overall, there were clear effects of mental demand as showed in the workload perception during the training tasks. Reliability for the workload scale showed good score and sensitivity showed mental demand as the most important factor compared to the other parameters measured obtaining highest correlations with landing tasks and number of errors. In our results, we have seen how the AWT (adapted from NASA-TLX) showed good sensitivity in assessing the mental burden of participants. In our research, participants scoring higher in the mental demand subscale showed greater difficulty finishing training tasks, and also showed longer time delays in performing both training sections of the simulation. These types of tools measuring workload perception and virtual training systems can be used in future research, to see how this cognitive aspect affects piloting skills and its possible safety and training implications.