## 计算机代写|云计算代写cloud computing代考|CS4740

2022年10月8日

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础
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## 计算机代写|云计算代写cloud computing代考|Workload Data

In order to make a simulation-based evaluation applicable in real world, it is crucial to use workload traces from a real system in experiments [12]. Therefore, the performance of RTDVMC and other DVMC algorithms have been measured with real Cloud workload traffic traces representing time varying resource utilization. Real workload data is provided as part of the CoMon project, a monitoring infrastructure for PlanetLab [49]. Data of CPU usage of thousands of VMs has been collected every $5 \mathrm{~min}$, while these VMs had been hosted in PMs spread globally across 500 locations. Both algorithms have been tested with the PlanetLab workload data of four different days: 6 March, 9 March, 9 April, and 20 April featuring different sets of varying resource demand over time. The characteristics of different PlanetLab workload data is articulated in Table $3.3$.

For each workload, the associated VMs’ release time or workload finishing time have been drawn from monthly VMRT traces of real Cloud, namely, Nectar Cloud. Traces of VMs created in Nectar Cloud over a month along with respective release time of those VMs constitutes the monthly VMRT data. The latest available VMRT data of three different months: November 2013, December 2013, and January 2014 have been used for experiments. To explain more, a single day’s PlanetLab workload data is tested with Nectar VMRT data of three different months offering diverse VMRT distributions, so that the impact of heterogeneous workload finishing time or release time can be analyzed. Histogram of different months of Nectar VMRT data has been articulated through Figs. 3.1, 3.2, and 3.3. The number of VMs in Nectar VMRT data of a month is greater than the number of VMs in the PlanetLab workload data of a day. Therefore, a uniformly distributed random variable has been used to select a smaller set of VMs from monthly Nectar data to match the number of VMs of the daily PlanetLab data. Uniformly distributed random variable proffers the smaller set of VMs with similar VMRT distribution present in the monthly Nectar data.

## 计算机代写|云计算代写cloud computing代考|Simulation Results Analysis

SRTDVMC and RTDVMC have been simulated under different workload scenarios. Four different days of PlanetLab workload data has been randomly selected (i.e., 6 March, 9 March, 9 April, and 20 April). PlanetLab workload data of every single day featuring varying resource demand over time has then been blended with three diverse sets of VMRT data originated from three different months of Nectar Cloud Data (i.e., Nectar Nov, Nectar Dec, and Nectar Jan) featuring heterogeneous VMRT. Thus, from a single set of daily PlanetLab workload data, three diverse sets of workload data are produced featuring time variant resource demand and diverse workload finishing time, which matches with real Cloud. Both algorithms are reiterated over multiple times for each set of time variant workload representing a unique combination of PlanetLab and Nectar Cloud data, to produce corresponding $\bar{E}_{\mathrm{CDC}}$ and $\bar{\psi}$

Values of $\bar{E}^R$ and $\bar{E}^S$ representing mean CDC energy consumption by RTDVMC set representing difference between mean energy consumption by $R T D V M C$ and mean energy consumption by SRTDVMC for different workload scenarios. In other words, $X_{N T, P L}^E$ represents the minimization of mean energy consumption proffered by SRTDVMC compared to RTDMC for diverse workloads, as articulated in Table 3.4.
$$X^{\bar{E}}=\left{X_{N T, P L}^{\bar{E}}\right}_{\mid \text {Nectar }|\cdot| P L a b \mid}=\left{\bar{E}{N T, P L}^R-\bar{E}{N T, P L}^S\right}_{\mid \text {Nectar }|\cdot| P L a b \mid}$$
From experimental results, as portrayed in Fig. $3.7$ and Table 3.4, we can observe that $S R T D V M C$ significantly reduces $\mathrm{CDC}$ energy consumption compared to existing DVMC algorithm. However, one might reject the superiority of SRTDVMC over existing DVMC algorithm based on the argument that no proof of statistical significance has been provided. To address such arguments, in the following section, we have presented diverse statistical testing.

# 云计算代考

## 计算机代写|云计算代写cloud computing代考|Workload Data

. cloud computing . cloud computing

## 计算机代写|云计算代写cloud computing代考|模拟结果分析

.模拟结果分析 SRTDVMC和RTDVMC在不同的工作负载场景下进行了模拟。随机选择了PlanetLab工作负载数据的四个不同的日子(即3月6日、3月9日、4月9日和4月20日)。PlanetLab每天的工作量数据随着时间的推移呈现不同的资源需求，然后与三组不同的VMRT数据混合，这些数据来自三个不同月份的花蜜云数据(即花蜜11月、花蜜12月和花蜜1月)，具有不同的VMRT。这样，从一组每日PlanetLab工作负载数据中，可以生成三组不同的工作负载数据，这些数据具有时变的资源需求和不同的工作负载完成时间，与真实的Cloud相匹配。对于每一组代表PlanetLab和Nectar Cloud数据独特组合的时变工作负载，这两种算法都要重复多次，以生成相应的$\bar{E}_{\mathrm{CDC}}$和$\bar{\psi}$

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。