Software rejuvenation is a recently proposed practive fault-tolerance approach to counteract software-aging phenomenon. Compared with clusters of a flat architecture, all the nodes share the same functions. The applic...Software rejuvenation is a recently proposed practive fault-tolerance approach to counteract software-aging phenomenon. Compared with clusters of a flat architecture, all the nodes share the same functions. The application of software rejuvenation on dispatcher-based web server farms is discussed, which employ rejuvenation both on the dispatcher and the worker pool. Stochastic reward net (SRN)models for time-based and prediction-based rejuvenation policies are constructed respectively and solved by stochastic Petri net package (SPNP). Numerical results show that appropriate rejuvenation strategies on the dispatcher and the worker pool could significantly reduce the expected downtime cost of the whole web server farm.展开更多
将分布式温室控制系统监控数据库系统划分成参数采集库、参数设定库、经济成本库、作物生态数据库以及设备管理库,并采用SQL Server 2000构建和管理温室环境监控数据库系统,介绍了开发这一系统的具体步骤、方法以及OLE DB和ADO的数据库...将分布式温室控制系统监控数据库系统划分成参数采集库、参数设定库、经济成本库、作物生态数据库以及设备管理库,并采用SQL Server 2000构建和管理温室环境监控数据库系统,介绍了开发这一系统的具体步骤、方法以及OLE DB和ADO的数据库应用编程。展开更多
针对云服务器中存在软件老化现象,将造成系统性能衰退与可靠性下降问题,借鉴剩余使用寿命(Remaining useful life,RUL)概念,提出基于支持向量和高斯函数拟合(Support vectors and Gaussian function fitting,SVs-GFF)的老化预测方法.首...针对云服务器中存在软件老化现象,将造成系统性能衰退与可靠性下降问题,借鉴剩余使用寿命(Remaining useful life,RUL)概念,提出基于支持向量和高斯函数拟合(Support vectors and Gaussian function fitting,SVs-GFF)的老化预测方法.首先,提取云服务器老化数据的统计特征指标,并采用支持向量回归(Support vector regression,SVR)对统计特征指标进行数据稀疏化处理,得到支持向量(Support vectors,SVs)序列数据;然后,建立基于密度聚类的高斯函数拟合(Gaussian function fitting,GFF)模型,对不同核函数下的支持向量序列数据进行老化曲线拟合,并采用Fréchet距离优化算法选取最优老化曲线;最后,基于最优老化曲线,评估系统到达老化阈值前的RUL,以预测系统何时发生老化.在OpenStack云服务器4个老化数据集上的实验结果表明,基于RUL和SVs-GFF的云服务器老化预测方法与传统预测方法相比,具有更高的预测精度和更快的收敛速度.展开更多
文摘Software rejuvenation is a recently proposed practive fault-tolerance approach to counteract software-aging phenomenon. Compared with clusters of a flat architecture, all the nodes share the same functions. The application of software rejuvenation on dispatcher-based web server farms is discussed, which employ rejuvenation both on the dispatcher and the worker pool. Stochastic reward net (SRN)models for time-based and prediction-based rejuvenation policies are constructed respectively and solved by stochastic Petri net package (SPNP). Numerical results show that appropriate rejuvenation strategies on the dispatcher and the worker pool could significantly reduce the expected downtime cost of the whole web server farm.
文摘针对云服务器中存在软件老化现象,将造成系统性能衰退与可靠性下降问题,借鉴剩余使用寿命(Remaining useful life,RUL)概念,提出基于支持向量和高斯函数拟合(Support vectors and Gaussian function fitting,SVs-GFF)的老化预测方法.首先,提取云服务器老化数据的统计特征指标,并采用支持向量回归(Support vector regression,SVR)对统计特征指标进行数据稀疏化处理,得到支持向量(Support vectors,SVs)序列数据;然后,建立基于密度聚类的高斯函数拟合(Gaussian function fitting,GFF)模型,对不同核函数下的支持向量序列数据进行老化曲线拟合,并采用Fréchet距离优化算法选取最优老化曲线;最后,基于最优老化曲线,评估系统到达老化阈值前的RUL,以预测系统何时发生老化.在OpenStack云服务器4个老化数据集上的实验结果表明,基于RUL和SVs-GFF的云服务器老化预测方法与传统预测方法相比,具有更高的预测精度和更快的收敛速度.
