Intelligent Automation with VMware

Apply machine learning techniques to VMware virtualization and networking

Ajit Pratap Kundan

• Publisher: Packt Publishing
• Year: 2019
• Print ISBN: 9781789802160
• eBook ISBN: 9781789806793
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Use self-driven data centers to reduce management complexity by deploying Infrastructure as Code to gain value from investments.

Key Features

• Add smart capabilities in VMware Workspace ONE to deliver customer insights and improve overall security
• Optimize your HPC and big data infrastructure with the help of machine learning
• Automate your VMware data center operations with machine learning

Book Description

This book presents an introductory perspective on how machine learning plays an important role in a VMware environment. It offers a basic understanding of how to leverage machine learning primitives, along with a deeper look into integration with the VMware tools used for automation today.

This book begins by highlighting how VMware addresses business issues related to its workforce, customers, and partners with emerging technologies such as machine learning to create new, intelligence-driven, end user experiences.

You will learn how to apply machine learning techniques incorporated in VMware solutions for data center operations. You will go through management toolsets with a focus on machine learning techniques.

At the end of the book, you will learn how the new vSphere Scale-Out edition can be used to ensure that HPC, big data performance, and other requirements can be met (either through development or by fine-tuning guidelines) with mainstream products.

What you will learn

• Orchestrate on-demand deployments based on defined policies
• Automate away common problems and make life easier by reducing errors
• Deliver services to end users rather than to virtual machines
• Reduce rework in a multi-layered scalable manner in any cloud
• Explore the centralized life cycle management of hybrid clouds
• Use common code so you can run it across any cloud

Who this book is for

This book is intended for those planning, designing, and implementing the virtualization/cloud components of the Software-Defined Data Center foundational infrastructure. It helps users to put intelligence in their automation tasks to get self driving data center. It is assumed that the reader has knowledge of, and some familiarity with, virtualization concepts and related topics, including storage, security, and networking.

Section 1: VMware Approach with ML Technology

In this section, we will see how VMware is addressing the business issues related to its workforce, customers, and partners by means of emerging technology, such as machine learning, in order to create new, intelligence-driven end user experiences that accelerate productivity and responsiveness to achieve real business value with digital transformation. Readers will learn how to apply machine learning techniques incorporated in VMware solutions to their data center operations.

This section contains the following chapters:

• Chapter 1, ML Capabilities with vSphere 6.7
• Chapter 2, Proactive Measures with vSAN Advanced Analytics
• Chapter 3, Security with Workspace One Intelligence
• Chapter 4, Proactive Operations with VMware vRealize Suite
• Chapter 5, Intent-Based Manifest with AppDefense

Machine Learning Capabilities with vSphere 6.7

This book will brief you about how different VMware tools will help customers to reap the benefits of intelligent automatic detection and prediction. This offering will enable them to detect, visualize, and troubleshoot anomalies, future needs, and potential modern applications and infrastructure issues—all this without any additional skillset for statistical or algorithm expertise from the user's side. VMware provides customers unified visibility into the health and typical behavior of metrics across all services without user assistance, and therefore reduces troubleshooting time. These tools will also help in forecasting to understand future capacity needs and to maximize application and infrastructure efficiency cost-effectively.

This chapter briefs you on how to virtualize GPU in a vSphere environment by using different modes, creating profiles for specific groups of users, and information about different NVIDIA graphics cards and their usage. We will also learn about configuring NVIDIA GPU in vSphere for machine learning (ML)-based workloads with different use cases to achieve specific business objectives while reducing cost.

The topics that we we will cover in this chapter are as follows:

• ML and VMware
• Different modes of using GPUs
• ML with NVIDIA GPUs

Technical requirements

You can download the following:

• VMware vSphere 6.7 from https://my.vmware.com/en/web/vmware/info/slug/datacenter_cloud_infrastructure/vmware_vsphere/6_7
• VMware vCenter Server 6.5 U1 https://my.vmware.com/web/vmware/details?downloadGroup=VC65U1&productId=676&rPId=28154
• VMware Horizon 7 https://my.vmware.com/en/web/vmware/evalcenter?p=horizon-7&src=WWW_HrzOld_TopNav_DownloadFreeTrail

ML and VMware

VMware is extensively leveraging AI and ML techniques in most of its products. We can see this with the new version of vSphere 6.7 features and also with products such as AppDefense and Workspace. This is one intelligence that will help in achieving the future vision of self-driving data centers. VMware has already done the performance benchmarking regarding how it can help its customers to run their own ML workloads on vSphere with improved support from NVIDIA GPUs.

ML is important for VMware mixed cloud vision, as ML has the ability to absorb a huge amount of data to utilize it for precise learning and with end users.

ML-based data analysis

ML is helping customers to fetch better granular-level information from large datasets. This will give a huge competitive advantage in business, as the customer is able to integrate their data from different sources, which will help the management to take major decisions ahead of their competitors. As we get this result with the relevant reasons behind it, we can help customers with accurate and productive data. The potential of AI in our daily life is immense.

We have a new development in ML every day, and ML will be extended even further. All the biggest public cloud vendors use ML-based techniques in their daily operations. Apple, GE, and Bosch are also gathering massive amounts of data and applying machine learning techniques to filter out only useful data. GE is accumulating data analytics through its industrial internet, and Apple has a huge amount of consumer and health data from its millions of end users, which helps them to emerge as major players in AI.

Embedding AI within present cloud technologies is helping businesses and consumers to grow and also creating new opportunities with all the relevant information to plan for the future. We are moving toward intelligent infrastructure where AI uses machines to adopt human intelligence. AI is based on rules-based logic, decision trees, and methodology to enable it to behave like a human. ML analyzes data and enhances the performance of repetitive tasks. DL will help machines learn by locating and checking various options against one another in order to get the best result or solution.

Customers have adopted the cloud and are now embedding ML techniques and its capabilities to extend its dynamics and delivering values to customers. Customers get a safe and secure environment with the scale up and out capabilities. The cloud provider receives loyal long-term customers in return. Every cloud provider is better than others in specific fields, from a business and an AI perspective. This will give customers diverse offerings with specialized intelligence for their unique requirements.

VMware will help customers with intelligent infrastructure that can deliver a comparable and secure solution across mixed clouds to choose the right cloud provider for their unique business requirements, such as security, backup, disaster recovery, networking, storage, graphics, and management with basic compute resources.

A good example of intelligent technology is Google Maps. When we leave our office for a meeting, with Google Map's guidance, we are able to identify alternative routes via AI, saving us valuable time.

Using virtualized GPUs with ML

ML is being extensively used in research and development these days, and the computing power enhancement of accelerators such as GPUs has enabled a rapid adoption of ML applications.

Designers, engineers, and architects are the extensive end users who frequently use 3D graphics for a wide-range of use cases and expect their IT teams to assist them in this. They use high-end graphics workstations handling 3D models of automobiles, manufacturing compon...

Table of contents

• Title Page
• Copyright and Credits
• About Packt
• Contributors
• Preface
• Section 1: VMware Approach with ML Technology
• Machine Learning Capabilities with vSphere 6.7
• Proactive Measures with vSAN Advanced Analytics
• Security with Workspace ONE Intelligence
• Proactive Operations with VMware vRealize Suite
• Intent-Based Manifest with AppDefense
• Section 2: ML Use Cases with VMware Solutions
• ML-Based Intelligent Log Management
• ML as a Service in the Cloud
• ML-Based Rule Engine with Skyline
• DevOps with vRealize Code Stream
• Transforming VMware IT Operations Using ML
• Section 3: Dealing with Big Data, HPC , IoT, and Coud Application Scalability through ML
• Network Transformation with IoT
• Virtualizing Big Data on vSphere
• Cloud Application Scaling
• High-Performance Computing
• Other Books You May Enjoy