Intelligent Automation with VMware: Apply machine learning techniques to VMware virtualization and networking

✍ Scribed by Ajit Pratap Kundan

• Publisher: Packt Publishing
• Tongue: English
• Leaves: 328
• Category: Library

✦ Synopsis

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.

Table of Contents

1. Machine Learning Capabilities with vSphere 6.7
2. Proactive Measures with vSAN Advanced Analytics
3. Security with Workspace ONE Intelligence
4. Proactive Operations with VMware vRealize Suite
5. Intent-Based Manifest with AppDefense
6. ML-Based Intelligent Log Management
7. ML as a Service in the Cloud
8. ML-Based Rule Engine with Skyline
9. DevOps with vRealize Code Stream
10. Transforming VMware IT Operations Using ML
11. Network Transformation with IoT
12. Virtualizing Big Data on vSphere
13. Cloud Application Scaling
14. High-Performance Computing

✦ Table of Contents

Cover
Title Page
Copyright and Credits
About Packt
Contributors
Table of Contents
Preface
Section 1: VMware Approach with ML Technology
Chapter 1: Machine Learning Capabilities with vSphere 6.7
Technical requirements
ML and VMware
ML-based data analysis
Using virtualized GPUs with ML
Modes of GPU usage
Comparing ML workloads to GPU configurations
DirectPath I/O 
Scalability of GPU in a virtual environment
Containerized ML applications inside a VM
vGPU scheduling and vGPU profile selection
Power user and designer profiles 
Knowledge and task user profiles
Adding vGPU hosts to a cluster with vGPU Manager
ML with NVIDIA GPUs
Pool and farm settings in Horizon
Configuring hardware-accelerated graphics
Virtual shared graphics acceleration
Configuring vSGA settings in a virtual machine
Virtual machine settings for vGPU
GRID vPC and GRID vApps capabilities
GRID vWS to Quadro vDWS
Summary
Further reading
Chapter 2: Proactive Measures with vSAN Advanced Analytics
Technical requirements
Application scalability on vSAN
Storage and network assessment
Storage design policy
VMware best practices recommendations 
Network design policy
VMware best practices recommendations 
VMware's Customer Experience Improvement Program/vSAN ReadyCare
Intelligent monitoring
General monitoring practices
vSAN Health Check plugin
vSAN Observer
vRealize Operations Manager monitoring
Challenges affecting business outcomes
Business benefits
Technical Issues
Technical solution
Log Intelligence advantages
HA configuration in stretched clusters
Two-node clusters
Witness appliance for the vSAN cluster
Configuring the vSAN cluster
vSAN policy design with SPBM
Defining a policy based on business objectives
FTT policy with RAID configurations
Summary
Further reading
Chapter 3: Security with Workspace ONE Intelligence
Technical requirements
Workspace ONE Intelligence
Business objectives of Workspace ONE Intelligence
Integrated deep insights
App analytics for smart planning
Intelligent automation driven by decision engines
Design requirements
Conceptual designs
Top ten use cases of Workspace ONE Intelligence
Identifying and mitigating mobile OS vulnerabilities
Insights into Windows 10 OS updates and patches
Predicting Windows 10 Dell battery failures and automating replacement
Identifying unsupported OS versions and platforms
Tracking OS upgrade progress
Monitoring device utilization or usage
Increasing compliance across Windows 10 devices
Comprehensive mobile app deployment visibility
Tracking migration and adoption of productivity applications
Adopting internal mobile applications
Workspace ONE Trust Network
Workspace ONE AirLift
Workspace ONE platform updates
Expanded Win32 app delivery
Simplified macOS adoption
Extended security for Microsoft Office 365 (O365) applications
VMware Boxer with Intelligent Workflows
Extended management for rugged devices
Summary
Chapter 4: Proactive Operations with VMware vRealize Suite
Technical requirements
Unified end-to-end monitoring
Intelligent operational analytics
The vRealize Operations Manager architecture
Application architecture overview
Capacity planning
Critical success factors
Kubernetes solution from VMware
Pivotal Container Service and VMware Kubernetes Engine 
SDDC journey stages
VMware container-based services
Deploying NSX-T for network virtualization on ESXi and deploying PKS for use in a private cloud
Deploying the NSX-T foundation
Deploying and running containerized workloads
VMware Cloud on AWS
VMware Cloud on AWS differs from on-premises vSphere
VMware Cloud on the AWS implementation plan
Implementation plan for VMware Cloud on AWS
Detailed initial steps to configure VMC on AWS
Installation, configuration, and operating procedures
Hybrid-linked-mode testing functionality
Support and troubleshooting
Summary
Further reading
Chapter 5: Intent-Based Manifest with AppDefense
Technical requirements
VMware innovation for application security
Digital governance and compliance
Intelligent government workflows with automation
Transforming networking and security
Business outcomes of the VMware approach
Expanding globally with AppDefense
Application-centric alerting for the SOC
Transforming application security readiness
Innovating IT security with developers, security, and the Ops team
Least-privilege security for containerized applications
Enhanced security with AppDefense
AppDefense and NSX
Detailed implementation and configuration plan
Environment preparation for AppDefense deployment
Summary
Section 2: ML Use Cases with VMware Solutions
Chapter 6: ML-Based Intelligent Log Management
Technical requirements
Intelligent log management with vRealize Log Insight
Log Intelligence value propositions
Log Intelligence key benefits for service providers
Audit log examples
Cloud operations stages
Standardize
Service Broker
Strategic partner
The Log Insight user interface
Indexing performance, storage, and report export
The user experience
Events
VMware vReaIize Network Insight
Supported data sources
Summary
Chapter 7: ML as a Service in the Cloud
Technical requirements
MLaaS in a private cloud
VMware approach for MLaaS
MLaaS using vRealize Automation and vGPU
NVIDIA vGPU configuration on vSphere ESXi
Customizing the vRealize Automation blueprint 
LBaaS overview
LBaaS design use cases
Challenges with network and security services 
The NaaS operating model
LBaaS network design using NSX
BIG-IP DNS high-level design
Customizing the BIG-IP DNS component
The BIG-IP DNS load-balancing algorithm
Global availability
Ratio
Round robin
The LBaaS LTM design
Configuring BIG-IP LTM objects
Designing the LTM load-balancing method
Designing the LTM virtual server
Summary
Chapter 8: ML-Based Rule Engine with Skyline
Technical requirements
Proactive support technology – VMware Skyline 
Collector, viewer, and advisor
Release strategy
Overview of Skyline Collector 
The requirements for Skyline Collector
Networking requirements
Skyline Collector user permissions
VMware Skyline Collector admin interface
Linking with My VMware account
Managing endpoints
Configuring VMware Skyline Collector admin interface
Auto-upgrade
CEIP
Types of information that are collected
Product usage data utilization
Summary
Chapter 9: DevOps with vRealize Code Stream
Technical requirements
Application development life cycles
CD pipeline
CI pipeline
Planning
SDLC
SCM
CI
AR
Release pipeline automation (CD)
CM
HC
COM
Feedback
Request fulfillment 
Change management 
Release management 
Compliance management 
Incident management
Event management 
Capacity management 
Wavefront dashboard
Getting insights by monitoring how people work
Automation with vRealize
Deploying Infrastructure as Code
vRealize Code Stream
Pipeline automation model – the release process for any kind of software
 vRCS deployment architecture
 System architecture
Integrating vRCS with an external, standalone vRA
Summary
Further reading
Chapter 10: Transforming VMware IT Operations Using ML
Overview on business and operations challenges
The challenges of not having  services owners for the operations team
A solution with service owners
Responsibilities of the service owner
Transforming VMware technical support operations
SDDC services
Service catalog management
Service design, development, and release
Cloud business management operations
Service definition and automation
NSX for vSphere
Recommendations with priority
Recommendations with priority 1
Recommendations with priority 2
Recommendations with priority 3
Virtual data centers
IaaS solution using vRealize Suite
Business-level administration and organizational grouping
vRA deployment
vRA appliance communication
Services running as part of the identity service 
A complete solution with the desired result
Summary
Section 3: Dealing with Big Data, HPC , IoT, and Coud Application Scalability through ML
Chapter 11: Network Transformation with IoT
Technical requirements
IoT
VMware Pulse
The queries that arise related to VMware Pulse
Pulse IoT Center infrastructure management blueprint
Deploying and configuring the OVA
Configuring IoT support
Virtual machines in the OVA
IoT use cases with VMware Pulse
Powering the connected car (automotive industry)
Entertainment, parks, and resorts
Smart hospitals (medical)
Smart surveillance (higher education)
Smart warehouse (retail industry)
The internet of trains (transportation and logistics)
The financial industry
Smart weather forecasting
IoT data center network security
NSX distributed firewall
Prerequisites to any automation
Hybrid cloud for scale and distribution
Summary
Chapter 12: Virtualizing Big Data on vSphere
Technical requirements
Big data infrastructure
Hadoop as a service
Deploying the BDE appliance
Configuring the VMware BDE
The BDE plugin
Configuring distributions on BDE
The Hadoop plugin in vRO
Open source software
Considering solutions with CapEx and OpEx
Benefits of virtualizing Hadoop
Use case – security and configuration isolation
Case study – automating application delivery for a major media provider
Summary
Further reading
Chapter 13: Cloud Application Scaling
Technical requirements
Cloud-native applications
Automation with containers
Container use cases 
Challenges with containers
PKS on vSphere
PKS availability zone 
PKS/NSX-T logical topologies
Use cases with different configurations
PKS and NSX-T Edge Nodes and Edge Cluster
PKS and NSX-T communications
Storage for K8s cluster node VMs
Datastores
Summary
Chapter 14: High-Performance Computing
Technical requirements
Virtualizing HPC applications
Multi-tenancy with guaranteed resources
Critical use case – unification 
High-performance computing cluster performances
A standard Hadoop architecture 
Standard tests
Intel tested a variety of HPC benchmarks
Summary
Other Books You May Enjoy
Index