Intelligent Automation with VMware

Book Description

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.

Publisher Resources

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Table of Contents

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

  • Title: Intelligent Automation with VMware
  • Author(s): Ajit Pratap Kundan
  • Release date: March 2019
  • Publisher(s): Packt Publishing
  • ISBN: 9781789802160