基于云计算SME-FFD算法的概率优度虚拟机资源配置

针对云计算虚拟机资源配置过程中存在的NP难问题,提出一种基于云计算SME-FFD的概率优度虚拟机资源配置算法。给出虚拟机资源配置的优度评价方案,利用模拟进化算法较强的爬坡优化能力,对虚拟资源配置的选择、评价和排序过程进行迭代进化。在模拟进化操作获取资源配置排序基础上,利用首次适应下降规则,对已排序的虚拟机和物理主机资源进行二次配置,提高资源配置效率和效果。在墨尔本大学CloundSim网格实验室以及Gridbus云仿真平台上进行实验对比,结果表明,该算法CPU使用率与内存使用率分别达到55%和60%以上,能够有效降低物理机器使用数量,实现节约能耗的目的。

虚拟机资源概率配置的云计算SEFFD算法

为增强虚拟机资源分配过程性能,有效解决云计算环境下虚拟资源分配的NP-hard问题,利用模拟进化算法结合首次下降算法构建虚拟资源分配优化过程(SEFFD)。首先,构建全新的虚拟资源分配的评估方式,并结合模拟进化过程较强的算法寻优爬坡效果,采用迭代方式实现虚拟资源分配过程的个体选择、评估以及排序进化;其次,以模拟进化(SE)过程所获得资源分配结果为基础,结合首次下降(FFD)算法准则,实现物理主机及虚拟机资源的二次分配,从而获得资源分配效果和效率的同步提升;最后,利用Clound Sim及Gridbus云计算仿真平台对算法性能进行对比测试,实验结果表明所提策略的内存利用率高于60%,处理器利用率大于55%,可有效减少所需物理主机数量,从而降低能耗。

Key parameter optimization and analysis of stochastic seismic inversion

Stochastic seismic inversion is the combination of geostatistics and seismic inversion technology which integrates information from seismic records, well logs, and geostatistics into a posterior probability density function （PDF） of subsurface models. The Markov chain Monte Carlo （MCMC） method is used to sample the posterior PDF and the subsurface model characteristics can be inferred by analyzing a set of the posterior PDF samples. In this paper, we first introduce the stochastic seismic inversion theory, discuss and analyze the four key parameters： seismic data signal-to-noise ratio （S/N）, variogram, the posterior PDF sample number, and well density, and propose the optimum selection of these parameters. The analysis results show that seismic data S/N adjusts the compromise between the influence of the seismic data and geostatistics on the inversion results, the variogram controls the smoothness of the inversion results, the posterior PDF sample number determines the reliability of the statistical characteristics derived from the samples, and well density influences the inversion uncertainty. Finally, the comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more reliable information of the subsurface character.

Outage Probability Minimization for Low-Altitude UAV-Enabled Full-Duplex Mobile Relaying Systems

In this paper, a novel unmanned aerial vehicle(UAV)-enabled full duplex decode-and-forward(DF) technique is used in mobile relaying system. Compared with conventional static relaying, mobile relaying provides more degree of freedom for experiencing better channel conditions and further improving the system reliable performance. The source and relay transmit power as well as relay trajectory are jointly optimized for sum outage probability minimization in mobile relaying system. Due to the non-convex nature of the original problem, block coordinate decent optimization techniques are employed to decompose it into two sub-problems, which leads to an efficient iterative algorithm. Specifically, for the pre-determined relay trajectory, the optimal transmit power of source and relay are obtained by solving Karush-Kuhn-Tucker(KKT) conditions. For the given source/relay power allocation, the optimal UAV trajectory is obtained by solving dual problem. Based on the two steps, an iterative algorithm is proposed to jointly optimize source/relay power allocation and UAV trajectory alternately. Numerical results show the performance gain of our proposed scheme.

Forecasting and optimal probabilistic scheduling of surplus gas systems in iron and steel industry

To make full use of the gas resource, stabilize the pipe network pressure, and obtain higher economic benefits in the iron and steel industry, the surplus gas prediction and scheduling models were proposed. Before applying the forecasting techniques, a support vector classifier was first used to classify the data, and then the filtering was used to create separate trend and volatility sequences. After forecasting, the Markov chain transition probability matrix was introduced to adjust the residual. Simulation results using surplus gas data from an iron and steel enterprise demonstrate that the constructed SVC-HP-ENN-LSSVM-MC prediction model prediction is accurate, and that the classification accuracy is high under different conditions. Based on this, the scheduling model was constructed for surplus gas operating, and it has been used to investigate the comprehensive measures for managing the operational probabilistic risk and optimize the economic benefit at various working conditions and implementations. It has extended the concepts of traditional surplus gas dispatching systems, and provides a method for enterprises to determine optimal schedules.

基于概率积分变换与似然比的高维相关性检验

高维相关性的拟合优度检验是Copula尤其是高维Copula实践应用中遇到的复杂课题。为了解决这一难题,该文将Berkowitz在2001年提出的基于概率积分变换的似然比检验单变量分布的方法推广用来做多变量分布的拟合优度检验,特别是用来检验高维copula的拟合优度。通过Monte Carlo模拟对比实验,结果表明基于概率积分变换的似然比检验统计量对高维相关性的拟合优度具有很强的检测能力,同时对小样本数据同样有效。

UNDERSTANDING OF FUZZY OPTIMIZATION:THEORIES AND METHODS

A brief summary on and comprehensive understanding of fuzzy optimizationis presented. This summary is made on aspects of fuzzy modelling and fuzzy optimization,classification and formulation for the fuzzy optimization problems, models and methods.The importance of interpretation of the problem and formulation of the optimal solutionin fuzzy sense are emphasized in the summary of the fuzzy optimization.

Uncertainty analysis and probabilistic design optimization of hybrid rocket motors for manned lunar landing

To obtain a conceptual design for a hybrid rocket motor(HRM)to be used as the Ascent Propulsion System in the Apollo lunar module,the deterministic design optimization(DDO)method is applied to the HRM design.Based on the results of an uncertainty analysis of HRMs,an uncertainty-based design optimization(UDO)method is also adopted to improve the design reliability.The HRM design process,which is a multidisciplinary system,is analyzed,and a mathematical model for the system design is established to compute the motor performance from the input parameters,including the input variables and model parameters.The input parameter uncertainties are quantified,and a sensitivity analysis of the model parameter uncertainties is conducted to identify the most important model parameter uncertainties for HRMs.The DDO and probabilistic UDO methods are applied to conceptual designs for an HRM to be used as a substitute for the liquid rocket motor(LRM)of the Ascent Propulsion System.The conceptual design results show that HRMs have several advantages as an alternative to the LRM of the Ascent Propulsion System,including nontoxic propellant combination,small motor volume,and comparable functions,such as restarting and throating.Comparisons of the DDO and UDO results indicate that the UDO method achieves more robust and reliable optimal designs than the DDO method.The probabilistic UDO method can be used to develop better conceptual designs for HRMs.

Robust hypothesis testing for asymmetric nominal densities under a relative entropy tolerance

In this paper, we address an open problem raised by Levy(2009) regarding the design of a binary minimax test without the symmetry assumption on the nominal conditional probability densities of observations. In the binary minimax test, the nominal likelihood ratio is a monotonically increasing function and the probability densities of the observations are located in neighborhoods characterized by placing a bound on the relative entropy between the actual and nominal densities. The general minimax testing problem at hand is an infinite-dimensional optimization problem, which is quite difficult to solve. In this paper, we prove that the complicated minimax testing problem can be substantially reduced to solve a nonlinear system of two equations having only two unknown variables, which provides an efficient numerical solution.