VMware, ESXI, Virtual machine overview

VMware vSphere : all component  together makes VMware's enterprise virtualization platform

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VMware hypervisor / ESXi 5 - Hypervisor Operating System that run on the physical server. This is basic package which is installed on hardware and make possible virtualization.


ESXI is also called as host


What is hypervisor ?

A software layer or OS which presents hardware to the Guest OS.



Types of hypervisor
Type 2 "hosted": in this case hypervisor runs as application service on top of traditional os, hardware is presented by host OS (window or linux)


Type 1 : bare Metal : in this case hypervisor run directly on hardware



VMware ESX 3.5 boots redhat and then hypervisor
ESXi - things are changed
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Virtual machine : a container which is capable of installing and or running an operating system

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Guest : an OS running on a hypervisor within a Virtual Machine container

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Cos : console OS which is responsible for providing a user interface on the server itself

Cos in window is shell called explore.exe and in Linux it is bash


vCenter 5 - Service that Manges ESXi host (vcenter server is services runs on server end)

vSphere client - client to connect and configure vcenter and ESXI hosts.


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Running desktop os on VM is expensive so we have

other comment :
vSphere desktop and VMware View (low license cost vm to host desktop os)
VMWare workstation ( run VM on desktop)


other vendor

Citrix : Citrix XenServer
Microsoft Hyper -V

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AI ML DL DS

·         AI: Artificial Intelligence

·         ML: Machine Learning

·         DL: Deep Learning

·         DS: Data Science.

·         AI: enable machine to think. That means without any human intervention machine will take decision.

o   E.g Driverless CAR

·         ML: Machine learning is subset of AI, provide statistical tools to explore and analyze the data

o   There are 3 different approaches

ü Supervised

ü Un-supervised

ü Reinforcement learning / semi supervised ML

o   Supervised: we will have data in hand to be processed, past labeled data and we know what will        be output of data

o   Un-supervised: we don’t have data, we solve clustering problem.  Clustering mean based on similarity of data it will try to group data together. We have 3 different algorithm K Means clustering, db scan clustering, Hierarchical clustering

o   Reinforcement learning / semi supervised ML: Some part of data will be labeled, and some will not be learned, in this model computer learn slowly using past data and new data

·         Deep Learning: Multineural network architecture, make machine to learn like human.

o   ANN : Artificial neural network , if data is in form of number we use ANN

o   CNN : Convolutional neural network , if  data is in form of images we use CNN

o   TL (Advanced Neural network based on CNN)

o   RNN : Recurrent neural network , if our data  is in form of time series data

·         Data Science: use all above technique and use mathematical tool like statistic, probability and linear analyses