Overview of Cloud computing ,platform , services and Internet of Things(IoT)

1. Cloud Computing and Internet of Things
Dr. Selvaraj Kesavan

2. Contents
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➢Cloud computing introduction
➢Cloud computing- Service, deployment models and
characteristics
➢Cloud computing service platforms and Services
➢Internet of things(IoT)- Introduction
➢IoT stack landscape
➢IoT - Sensors, Protocols and Platforms.
➢Overview of various applications/Visualization Tools for IoT

3. Cloud Computing
➢ Cloud computing is the on-demand delivery of compute power,
database storage, applications, and other IT resources through a
services platform via the internet with pay-as-you-go pricing.
➢ It is the delivery of computing as a service rather than product
where shared resources, software and information are provided to
users as an utility.
➢ Moving from traditional to on-Demand model.
➢ The analogy is , 'If you need milk , would you buy a cow ?’.
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4. Cloud Computing- service ,deployment models and
characteristics
Essential
Characteristics
Resource
pooling
Broad network
access
Measured
Service
Rapid
Elasticity
On-demand
Self-service SaaS
Software-as-a-Service
applied to
Applications
e.g. email, productivity, CRM
PaaS
Platform-as-a-Service
applied to
App Infrastructure
e.g. app runtimes, middleware, dev
tools
IaaS
Infrastructure-as-a-Service
applied to
System Infrastructure
e.g. VMs, containers, storage, network
Private Cloud
Public Cloud
Hybrid Cloud

5. Cloud Computing Service Models
• SaaS (Software as a Service)
- Access the services from provider. consumer uses an application, but does
not control the operating system, hardware or network infrastructure on which
it's running
• PaaS (Platform as a Service)
- The consumer uses a hosting environment for their applications. The
consumer controls the applications that run in the environment but does not
control the operating system, hardware or network infrastructure on which they
are running. Provides the middleware framework
• IaaS (Infrastructure as a Service)
- The consumer uses fundamental computing resources such as
processing power, storage, networking components
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6. Major Benefits
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Speed to Market
Cost Reduction
Elasticity / Scalability
Agility
• Lower capital and operational costs
• Pay-per-use
• Economies of scale
• Reduction of time to pilot and test projects
• New geographies
• Broad geographic availability
• Faster availability to customers
• Capacity only when you need it
• Ability to handle sudden load changes
• Survive infrastructure failures
• Productivity & Speed
• Deploy faster; Iterate faster
• React faster to changing business needs
• “Infinite” computing capacity

7. Major Services
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Major cloud
platform
services
Machine Learning

8. Security and Identity Services
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Authentication :
- Allow Apps to authenticate on behalf of users
- Allow users
Authorization:
- Fine-grained access control
- Attribute management
- Policy evaluation
- Policy Management
Implementation:
OpenID- Created for federated authentication. User obtain account with OpenID Provider and
use the account into sign in with any other web services. Allows communication between IDP
and SP.
OAuth (Open Authorization) is for token-based authentication and authorization. Allows
users/application to access resources.
SAML: Security Assertion Markup Language. Open standard for authentication and
authorization.
SSO( Single Sign On) for Federation and Identity Management. One user name and password
to access multiple services.

9. Authentication and authorization Flow
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10. Database
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Requirement:
➢ Relational/Non-Relational
➢ Store large amount of data
➢ Real Time Streaming Data
➢ Scalable ,Reliable and high availability
➢ Multi-tenancy
➢ Cost
Relational (SQL): Structure with defined attributes
➢ MS SQL, POSTGRESQL ,oracle SQL
➢ Can be queried using SQL
No-relational(NO-SQL): Free flow operations
➢ Utilize a variety of data models, including document, graph, key-
value and columnar
➢ Unique way to query the data
➢ Mongo DB(Document) , Redis (Key-value) ,Amazon Redshift
(Columnar) , Cassandra (Columnar), HBase (Columnar) ,Dynamo
DB(Document DB-stores JSON/XML) , GraphDB

11. Computing
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Requirement:
➢ Able to deploy application or software
➢ Run and execute
➢ Scale based on conditions
➢ Balance and distribute the load
➢ Fail fallback
• Ramp up or ramp down resource on need basis
• Compute/memory/storage/GPU optimized
• Route the load to difference instances
• Virtual Network Environment
• Examples:
➢ Virtual Machines
➢ Load Balancers
➢ Auto Scaling
➢ Virtual Private Cloud

12. Machine Learning
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➢ Machine learning is a method of data analysis
➢ Using algorithms it iteratively learn from data and allows to find insights into this
data
➢ It allows learning from previous computations to produce reliable, repeatable
decisions and results
➢ Machine Learning enables computer system to mimic the working of the human
brain
✓ Data Collection: Collect the historical Data
✓ Data Preparation: Choose and classify the data
✓ Model Training: Selecting the algorithm for model
validation and Training
✓ Model Evaluation: Feed the data and test the accuracy
✓ Performance tune and improvement: Choosing different
model
Data collection and preparation is the key in Machine
Learning

13. Machine Learning –Models
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• Training Data with Labels
• Classification ,Regression etc..
• Classifier based on input and
output data
• Used when "I know how to
classify this data, I just need
you(the classifier) to sort it."
Supervised Learning
Machine Learning
Un supervised Learning Reinforcement Learning
• Training Data without Labels
• Clustering ,Dimension
reduction etc..
• Algorithm based on input data
• Used when "I have no idea how
to classify this data, can
you(the algorithm) create a
classifier for me?"
• Iterative Rewards and
punishments
• Correct input and output pairs
are not presented a-priori.
• allows machines and software
agents to automatically
determine the ideal behavior
within a specific context.
• Training an animal.

14. Machine Learning – algorithm and use case
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Algorithms:
✓ Linear Regression
✓ Logical Regression
✓ K-Means
✓ Random Forest
Use cases:
✓ Forecasting ,Decision Making, Classification, Detecting Anomalies etc..
✓ ML from user preferences to push ads to the respective users
✓ Banking & Financials:- Identify loan & credit card defaulters and
customers who can be granted loans and credit cards
✓ Healthcare: Diagnose deadly diseases (e.g. cancer) based on the
symptoms of patients and tallying them with the past data of similar kind
of patients
Programming and Framework:
✓ Python
✓ R Programming (Statistical computing )
✓ Matlab
✓ Java
✓ Tensor flow
✓ Amazon Machine Learning
✓ Spark Mlib
✓ H2O
✓ Azure ML studio

15. Services Platform – Cloud ,IoT and IIoT
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• Amazon Web Services
• Microsoft Azure
• Google Cloud platform
• IBM Bluemix
• Oracle Cloud
• GE Predix
• ThingWorx

16. Platform Services- Example
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AWS RDS
AWS Aurora
AWS DynamoDB
Azure SQL
Google Cloud SQL
Azure Table Storage
Azure Cosmos DB
Google Bigtable
Google Cloud Datastore
Relational DB
Non-relational DB
as-a-Service
Data Streaming
as-a-Service
AWS Kinesis
Azure Event Hubs
Google Cloud Pub/Sub
Machine Learning
as-a-Service
AWS Lex
AWS Poly
AWS Rekognition
Azure Computer Vision API
Face API
Custom Vision Service
Google Cloud Natural Language API
Cloud Vision API
Cloud Translation API

17. Programming/Connecting to cloud Services
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Programming Toolkits:
• Portal
• CLI
• Power Shell
• REST API’s
Application to Cloud Services:
• SDK’s
• REST API’s
Device/Gateway to Cloud Services:
• Device SDK
• REST API’s

18. Internet of Things (IoT) and Industrial Internet of Things (IIoT)
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People
Things
IIOT
IOT

19. What is IOT
➢ Internet is no longer just a global network for people to communicate
with one another using computers, but it is also a platform for devices
to communicate electronically with the world around them.
➢ Sensors/Things connected to server via network and deliver
connected industry solutions for efficient control and improved human
experience.
➢ Billions of connected devices is an indicator of IoT. The connectivity is
just an enabler but the real value of IoT is on data (business
insight/data-driven economy).
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20. Technology Landscape
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Industry verticals -
Dashboard
Platform and
services
Protocols and
Communication
Sensors ,Devices and
Gateway
Application
End Device
Light
Sensor
Voltage
Sensor
Temp/
humidity
Vibration
Sensor
Ultrasonic
Sensor
Gas
Sensor
BLE
Sensor
GPS
Pi 3
gateway
Gateway
PLC

21. Field Devices, Sensors and Gateway
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Monitor
Sensor Data
Aggregate
Analyze
Send to Cloud
Gateway Provides
➢ Authentication
➢ Data Filtering
➢ Edge Analytics
➢ Control and management
Communication between
sensors and Gateway ,
Gateway to cloud platform using
➢ Zigbee
➢ BLE
➢ Wi-Fi
➢ RF
➢ LoRa
➢ MQTT
➢ AMQP
➢ CoAP
➢ HTTP/HTTPS
➢ NFC,TCP/UDP
➢ UART,SPI
Different field sensors/Devices
✓ Sensors: Temperature, pressure, accelerometer ,vibration
,RPM, Beacons etc..
✓ Devices: Camera, activity tracker, smart glass etc..

22. IoT As a Service from Cloud platform
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Major Functionalities:
➢Device Registration
➢Device Authentication and
Authorization
➢Device to cloud message
➢Cloud to Device message
➢Device state
➢Rules Engine
➢Communication with other Services
➢Communication with Edge devices

23. How to Visualize Raw/Processed/Analytics/Machine learning output?
Applications/Visualization Tools
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Web Applications:
• Application that is accessed via a web browser over a
network
• JavaScript, CSS, and HTML5
• Web apps became really popular when HTML5 came
around and people realized that they can obtain native-
like functionality in the browser.
Native Applications:
• Native apps are written in languages that the platform
accepts
• Swift or Objective-C for iOS
• Java for Android
• C# for Windows Mobile
Hybrid Application:
• Combination of Native with Web Component
• Xamarin -Slack, Pinterest.
• React Native -Facebook, Walmart, Tesla, and Airbnb
• Titanium -eBay, ZipCar, PayPal
• Angular JS -PubNub Chat, YouTube on PS3

24. Thank You
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