Assignment1 P1, CPSC5207E Virtualization and s24 v7

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Assignment 1 P1 CPSC 5207E Spring 2024 Page 1 of 11 Assignment-1 P1: Introduction to Virtualization Instructions: 1. This is an individual homework. Each student must submit his/her solution to the problem through Assignment-1 dropbox. 2. All submissions are to be via the D2L dropbox; no other way of submission is accepted. 3. All submissions must be submitted through the dropbox by the due date or by the closure date, the Closure Date allows 3 days for late submissions. Purpose and Objective This is an introductory assignment that addresses the basic concepts of cloud transformation. Success Criteria Successful identification of the concepts accurately as well as accurately demonstrating the ML Lab component. Keywords Virtualization, hypervisor, cluster, node, cell, fallback, container, data training, model, Image, container, volume, network, machine learning, SciPy, service, composition. Readings 1. Architecture of Virtual Machines by R. P. Goldberg, https://www.cse.psu.edu/~buu1/teaching/spring06/papers/goldberg.pdf . 2. Formal Requirements for Virtualizalble Third Generation Architecture, Geral Popek,Robert Goldberg. 3. A virtual machine time-sharing system by R. A. Meyer and L. H. Seawright https://www.seltzer.com/margo/teaching/CS508.19/papers/meyer-1970.pdf 4. An efficient virtual machine implementation by RONALD J. SRODAWA and LEE A. BATES V/ayne State University, https://dl.acm.org/doi/pdf/10.1145/1499586.1499668 5. A PROGRAM SIMULATOR BY PARTIAL INTERPRETATION, Kazuhiro Fuchi, Hozumi Tanaka , Yuriko Manago and Toshitsugu Yuba , Electrotechnical Laboratory, Japanese Government, https://dl.acm.org/doi/pdf/10.1145/961053.961092 6. A Performance Comparison Between Enlightenment and Emulation in Microsoft Hyper-V By Hasan Fayyad-Kazan, Luc Perneel & Martin Timmerman: https://globaljournals.org/GJCST_Volume13/4-A-Performance-Comparison.pdf 7. Hyper V: https://learn.microsoft.com/en-us/windows-server/virtualization/hyper- v/hyper-v-on-windows-server a. https://learn.microsoft.com/en-us/virtualization/hyper-v-on-

Assignment 1 P1 CPSC 5207E Spring 2024 Page 2 of 11 windows/reference/hyper-v-requirements . b. https://learn.microsoft.com/en-us/virtualization/hyper-v-on-windows/about/ c. https://learn.microsoft.com/en-us/system-center/vmm/?view=sc-vmm-2022 d. https://learn.microsoft.com/en-us/windows-server/failover- clustering/failover-clustering-overview e. https://www.vmware.com/ca/products/aria.html f. https://www.vmware.com/ca/products/hyper-converged-infrastructure.html 8. Machine Learning Studio (Classic): a. Azure Machine Learning i. https://learn.microsoft.com/en-us/azure/machine-learning/migrate- overview ii. https://azure.microsoft.com/en-us/products/machine-learning/ b. Documentation: https://learn.microsoft.com/en-us/previous- versions/azure/machine-learning/classic/

Assignment 1 P1 CPSC 5207E Spring 2024 Page 3 of 11 1. Introduction to Virtualization [15 marks] a. Based on the Goldberg Architecture of Virtual machine Model, answer the following questions: 1. How are resources addressed and abstracted? 2. Contrast between the Φ -map and the f-map. 3. Explain how virtual machine deployment facilitates new advantages that system programmers would achieve. 4. Identify one operating system that supported a virtual machine as well as three computer systems. 5. Interpret/explain the process map/state machine diagram displayed below: b. Based on Goldberg’s paper “Formal Requirements for Virtualizable Third Generation Architectures” [2], what is a virtual machine is? c. Based on [2], Formally identify the components of a virtual machine. d. Based on [2], what are the elements of Program Status Word? e. Based on [2], what is a Virtual Machine Monitor? f. Based on [2], list the properties of virtual machines. g. Based on [2], what is Recursive Virtualization?

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Assignment 1 P1 CPSC 5207E Spring 2024 Page 4 of 11 2. Virtualization Models [15 marks] The Hyper-V lets you create a virtualized computing environment where you can create and manage virtual machines. 2.1 Does Hyper-V support type 1 virtualization or type 2 virtualization? 2.2 The paper [Timmerman et al] identifies performance metrics used for evaluation tests purposes, list those metrics. 2.3 What approaches/modes does Hyper-V support? 2.4 Explain how would paravirtualization differ from hardware emulation approaches. 2.5 How does Enlightened partition differ from Unenlighted partition? 2.6 What is the core requirement for Emulated Virtualization? 2.7 What is failover clustering and identify why would recommend its deployment? 2.8 An associated software to Hyper-V is Virtual Machine Manager; explain what functionalities the software provides. Identify its capacity limits in terms of number of physical hosts, number of virtual machines, number of services, number of clouds, roles classifications, and number of logical networks. 2.9 VMware is one of the leading virtualization product providers; VMware HCI (Hyperconverged infrastructure) is a software-defined, unified system that combines all the elements of a traditional data center: storage, compute, networking and management. Identify why would your recommend such a scheme to your organization, and identify four Vmware products that would facilitates the deployment of and HCI system

Assignment 1 P1 CPSC 5207E Spring 2024 Page 5 of 11 3. “ Machine Learning as a Service ” Lab [20 marks] 3.1 sign-in into Studio: https://studio.azureml.net/ 3.2 In this lab, you will create a Machine Learning Studio experiment. 3.3 Click on Experiment 3.4 Create a new experiment by clicking +NEW at the bottom of the Machine Learning Studio (classic) window. 3.5 Select EXPERIMENT > Blank Experiment . 3.6 The experiment is given a default name that you can see at the top of the canvas. Select this text and rename it to A utomobile Price Prediction. 3.7 To the left of the experiment canvas is a palette of datasets and modules. Type automobile in the Search box at the top of this palette to find the dataset labeled Automobile price data (Raw) . Drag this dataset to the experiment canvas. 3.8 Hover on the 1 st nod, right-click the mouse and visualize the dataset:

Assignment 1 P1 CPSC 5207E Spring 2024 Page 6 of 11 3.9 In this dataset, each row represents an automobile, and the variables associated with each automobile appear as columns. We'll predict the price in far-right column (column 26, titled "price") using the variables for a specific automobile. 3.10 Observe the normalized-losses columns, many values are missing; a screen snapshot and add it to you documentation. 3.11 Take a screen snapshot of the set of the columns that ends with the Price column and add it to your documentation. 3.12 Close the visualization window by clicking the " x " in the upper-right corner. A. Prepare the data: A dataset usually requires some preprocessing before it can be analyzed. You might have noticed the missing values present in the columns of various rows. These missing values need to be cleaned so the model can analyze the data correctly. We'll remove any rows that have missing values. Also, the normalized-losses column has a large proportion of missing values, so we'll exclude that column from the model altogether. - First, we add a module that removes the normalized- losses column completely. Then we add another module that removes any row that has missing data. 1. Type select columns in the search box at the top of the module palette to find the Select Columns in Dataset module. Then drag it to the experiment canvas. This module allows us to select which columns of data we want to include or exclude in the model. 2. Connect the output port of the Automobile price data (Raw) dataset to the input port of the Select Columns in Dataset. 3. Click the Select Columns in Dataset module and click Launch column selector in the Properties pane. 1. On the left, click With rules 2. Under Begin With , click All columns . These rules direct Select Columns in Dataset to pass through all the columns (except those columns we're about to exclude). 3. From the drop-downs, select Exclude and column names , and then click inside the text box. A list of columns is displayed. Select normalized-losses , and it's added to the text box. 4. Click the check mark (OK) button to close the column selector (on the lower right). 5. Now the properties pane for Select Columns in Dataset indicates that it will pass through all columns from the dataset except normalized- losses . • You can add a comment to a module by double- clicking the module and entering text. This can help you see at a glance what the module is doing in your experiment. In this case double-click the Select

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Assignment 1 P1 CPSC 5207E Spring 2024 Page 7 of 11 Columns in Dataset module and type the comment "Exclude normalized losses." 6. Drag the Clean Missing Data module to the experiment canvas and connect it to the Select Columns in Dataset module. In the Properties pane, select Remove entire row under Cleaning mode . These options direct Clean Missing Data to clean the data by removing rows that have any missing values. Double-click the module and type the comment "Remove missing value rows." 7. Run the experiment by clicking RUN at the bottom of the page. • When the experiment has finished running, all the modules have a green check mark to indicate that they finished successfully. Notice also the Finished running status in the upper-right corner. • Now we have clean data. If you want to view the cleaned dataset, click the left output port of the Clean Missing Data module and select Visualize . Notice that the normalized-losses column is no longer included, and there are no missing values. Now that the data is clean, we're ready to specify what features we're going to use in the predictive model. B. Define Features: In machine learning, features are individual measurable properties of something you're interested in. In our dataset, each row represents one automobile, and each column is a feature of that automobile. Finding a good set of features for creating a predictive model requires experimentation and knowledge about the problem you want to solve. Some features are better for predicting the target than others. Some features have a strong correlation with other features and can be removed. For example, city- mpg and highway-mpg are closely related so we can keep one and remove the other without significantly affecting the prediction. Let's build a model that uses a subset of the features in our dataset. You can come back later and select different features, run the experiment again, and see if you get better results. But to start, let's try the following features: • make, • body-style, • wheel-base, • engine-size, • horsepower, • peak-rpm,

Assignment 1 P1 CPSC 5207E Spring 2024 Page 8 of 11 • highway-mpg, • price 3.13 Drag another Select Columns in Dataset module to the experiment canvas. Connect the left output port of the Clean Missing Data module to the input of the Select Columns in Dataset module. 3.14 Double-click the module and type "Select features for prediction." 3.15 Click Launch column selector in the Properties pane. 3.16 Click With rules . 3.17 Under Begin With , click No columns . In the filter row, select Include and column names and select our list of column names in the text box. This filter directs the module to not pass through any columns (features) except the ones that we specify. 3.18 Click the check mark (OK) button. This module produces a filtered dataset containing only the features we want to pass to the learning algorithm we'll use in the next step. Later, you can return and try again with a different selection of features. C. Choose and apply an algorithm - Now that the data is ready, constructing a predictive model consists of training and testing. We'll use our data to train the model, and then we'll test the model to see how closely it's able to predict prices. - Classification and regression are two types of supervised machine learning algorithms. Classification predicts an answer from a defined set of categories, such as a color (red, blue, or green). Regression is used to predict a number. - Because we want to predict price, which is a number, we'll use a regression algorithm. For this example, we'll use a linear regression model. - We train the model by giving it a set of data that includes the price. The model scans the data and look for correlations between an automobile's features and its price. Then we'll test the model - we'll give it a set of features for automobiles we're familiar with and see how close the model comes to predicting the known price. - We'll use our data for both training the model and testing it by splitting the data into separate training and testing datasets. 1. Select and drag the Split Data module to the experiment canvas and connect it to the last Select Columns in Dataset module. 2. Click the Split Data module to select it. Find the Fraction of rows in the first output dataset (in the Properties pane to the right of the canvas) and set it to 0.75. This way, we'll use

Assignment 1 P1 CPSC 5207E Spring 2024 Page 9 of 11 75 percent of the data to train the model, and hold back 25 percent for testing. By changing the Random seed parameter, you can produce different random samples for training and testing. This parameter controls the seeding of the pseudo-random number generator. 3. Run the experiment. When the experiment is run, the Select Columns in Dataset and Split Data modules pass column definitions to the modules we'll be adding next. 4. To select the learning algorithm, expand the Machine Learning category in the module palette to the left of the canvas, and then expand Initialize Model . This displays several categories of modules that can be used to initialize machine learning algorithms. For this experiment, select the Linear Regression module under the Regression category, and drag it to the experiment canvas. (You can also find the module by typing "linear regression" in the palette Search box.) 5. Find and drag the Train Model module to the experiment canvas. Connect the output of the Linear Regression module to the left input of the Train Model module, and connect the training data output (left port) of the Split Data module to the right input of the Train Model module. 6. Click the Train Model module, click Launch column selector in the Properties pane, and then select the price column. Price is the value that our model is going to predict. You select the price column in the column selector by moving it from the Available columns list to the Selected columns list. 7. Run the experiment.

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Assignment 1 P1 CPSC 5207E Spring 2024 Page 10 of 11 We now have a trained regression model that can be used to score new automobile data to make price predictions. D. Predict new automobile prices. Now that we've trained the model using 75 percent of our data, we can use it to score the other 25 percent of the data to see how well our model functions. 1. Find and drag the Score Model module to the experiment canvas. Connect the output of the Train Model module to the left input port of Score Model . Connect the test data output (right port) of the Split Data module to the right input port of Score Model . 2. Run the experiment and view the output from the Score Model module by clicking the output port of Score Model and select Visualize . The output shows the predicted values for price and the known values from the test data. 3. Finally, we test the quality of the results. Select and drag the Evaluate Model module to the experiment canvas, and connect the output of the Score Model module to the left input of Evaluate Model. The final experiment should look something like this:

Assignment 1 P1 CPSC 5207E Spring 2024 Page 11 of 11 To view the output from the Evaluate Model module, click the output port, and then select Visualize . 4. Interpret the values of the of the statistic Coefficient of Determination. 5. Each activity/module should include “ECE 1779H” as a comment clause, as below

Assignment1 P1, CPSC5207E Virtualization and s24 v7

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