This document proposes a smart refrigerator concept that uses cloud computing and big data techniques. Sensors in the refrigerator would generate and store data in the cloud. This data could then be used to detect malfunctions and provide notifications to users. It also allows third-party vendors regulated access to analyze the data for purposes like sending discount offers or analyzing refrigerator use patterns.

1. Smart Refrigerator Concept with Cloud and
Big Data technique
By:
Pushkar Bhandari

4. VM1
VM6VM5VM4
VM2 VM3
Service
Refrigerator
[ Sensors deployed ]
Private cloud
Third party vendor
1-Daily Ice-cream
2-Dairy product co.
3- - - - - - - - - - --
Registered account
Of 3rd party vendors
To access data either –
1-Discount offer
2-Payment
Data sensed
Mediator
Service Provider
Griblink
Fig. 1

5. User
Refrigerator
Wi-Fi
available
Registered user
Creating account on cloud
Should I
connect to
internet
Sensors generating data & storing on cloud
Cloud B
A
Service
Offer to access
Yes
Yes
No
No
Normal
Execution

6. Analytics
If any malfunction detected
Analysis of malfunction
Generate notice & send to user
Accepted ?
3rd party vendors
Registered account
Users
offer
User
accepted ?
No
locate
Data owner is 3rd party vendor
Yes
Data analysis by 3rd party vendor

9. What is cloud computing ?
Cloud Computing refers to manipulating, configuring, and accessing the applications online. It offers online
data storage, infrastructure and application.
Cloud Computing provides us a means by which we can access the applications as utilities, over the
internet. It allows us to create, configure, and customize the business applications online.
What is Cloud ?
The term Cloud refers to a Network or Internet. In other words, we can say that Cloud is
something, which is present at remote location. Cloud can provide services over network, i.e.,
on public networks or on private networks, i.e., WAN, LAN or VPN.

10. We need not to install a piece of software on our local PC and this is how the cloud
computing overcomes platform dependency issues. Hence, the Cloud Computing is
making our business application mobile and collaborative.

11. Public cloud
The Public Cloud allows systems
and services to be easily
accessible to the general public.
Public cloud may be less secure
because of its openness, e.g., e-
mail.

12. Private cloud
The Private Cloud allows systems and
services to be accessible within an
organization. It offers increased
security because of its private nature.

13. Community cloud
The Community Cloud allows
systems and services to be
accessible by group of
organizations.

14. Hybrid cloud
The Hybrid Cloud is mixture of
public and private cloud. However,
the critical activities are performed
using private cloud while the non-
critical activities are performed
using public cloud.

15. INFRASTRUCTURE AS A SERVICE
(IAAS)
IaaS provides access to fundamental
resources such as physical machines,
virtual machines, virtual storage, etc.

16. PLATFORM AS A SERVICE
(PAAS)
PaaS provides the runtime
environment for applications,
development & deployment tools, etc.

17. SOFTWARE AS A SERVICE
(SAAS)
SaaS model allows to use
software applications as a
service to end users.

18. In addition to the above main layer, some
other layers are also introduced such as
Database as a Service (DBaaS), Data as a
Service (DaaS), Ethernet as a Service
(EaaS), Network as a Service (NaaS),
Identity and Policy Management as a
Service (IPMaaS), and Sensing as a service
(SaaS). In general, all these models are
called XaaS, which means ‘X’ can be
virtually anything. In this paper we
discuss only, Sensing as a service (SaaS)
model

19. Employees'
generating data
User’s
generating data
Machines
generating data
In good old days, the data was brought to the processors to process
the data.
But today , as the data has grown scalable , processors are brought to
the data to process.

20. • We constantly produce allot of data.
• For example via social media , public transport and GPS.
• But it goes way beyond that.
• Daily we upload …….
55 Million pictures 340 Million Tweets 1 Billion Documents
Total of 2.5 Quintillion of Data per day

21. 3 V’s
Volume VelocityVariety
Others
Value Veracity

22. Big Data
Powerful
Computer
Processed by

23. Big Data Powerful
Computer
Only so much data
can be processed
Processing Limit

25. Big Data
Is broken into pieces
Hadoop’s approach

26. Big Data
Computation
Computation
Computation
Computation
Computation
Combined result
Hadoop’s approach

27. Hadoop Framework of tools
Is a

28. Hadoop
Running applications
On Big Data
Supports

29. • Hadoop is a open source implementation based on GFS and Map-reduce from
Google.
• Sanjay Ghemawat, Howard Gobioff and Shun- Tak Leung ( 2003 ): The Google File
System.
• Jeffrey Dean and Sanjay Ghemawat ( 2004 ) : Map-Reduce-Simplified Data
Processing on Large Clusters.
• Created by Doug Cutting and Michael Cafarella ( Yahoo ) in 2005.
• Yahoo donated the project to Apache’s in 2006.

30. MapReduce
HDFS
Projects
Provides additional
functionality

31. • A Distributed File System
• Basically storage layer
• Designed to reliably store data using commodity
hardware
• Designed to overcome hardware failures
• Intended for large files
• Designed for batch inserts

32. • Files are stored as a collection of blocks.
• Blocks are 64 MB chunks of a file [configurable].
• Blocks are replicated on 3 nodes .
• The NameNode (NN) manages metadata about
files and blocks.
• The SecondaryNameNode (SNN) holds a backup
of NN data.
• DataNodes (DN) store and serve blocks.

33. MapReduce
[Distributed programming model or
Computation framework ]
HDFS
Hadoop Distributed File System
HBase Pig Hive CascadingOozie
Scoop Mahout
fig. 17

34. Linux Linux LinuxLinux
Low cost computers
Works on Linux
based machines

35. Data Node
Task
Tracker
Data Node
Task
Tracker
Data Node
Task
Tracker
Data Node
Task
Tracker
Data Node
Task
Tracker
Task
Tracker
Job
Tracker
Data
Node
Name
Node
Master
Slaves
ApplicationQUEUE
Batch
Processing

36. Data Node
Task
Tracker
Data Node
Task
Tracker
Data Node
Task
Tracker
Data Node
Task
Tracker
Data Node
Task
Tracker
Task
Tracker
Job
Tracker
Data
Node
Name
Node
Master
Slaves
Tables are backed
up

37. Programmers
Where the file is located
How to manage failures
How to break computation
into pieces
How to program for
scaling
Do not have to
worry about
Writing scale free programs
Could focus on

38. Processing speed
Number of computers
Linear

39. 1. Lowering the cost of innovation
2. Procuring large scale resources quickly
3. Handling Batch Workloads Efficiently
4. Handling Variable Resource Requirements
5. Running Closer to the Data
6. Simplifying Hadoop Operations

40. • Amazon Elastic Compute Cloud (EC2) is a central part of Amazon.com's
cloud computing platform, Amazon Web Services (AWS).
• EC2 allows users to rent virtual computers on which to run their own
computer applications.
• EC2 allows scalable deployment of applications by providing a Web service
through which a user can boot an Amazon Machine Image to create a
virtual machine, which Amazon calls an "instance", containing any software
desired.
• A user can create, launch, and terminate server instances as needed, paying
by the hour for active servers, hence the term "elastic".

41. As of December 2012, the following instance types were offered:
On-demand : Pay by hour without commitment
Reserved : Rent instances with one-time payment receiving discounts on the hourly
charge
Reserved Instances can be purchased in three different ways: All Upfront, Partial Upfront
and No Upfront.
Spot : Bid-based service (runs the jobs only if the spot price is below the bid specified by
bidder—the spot price is claimed to be supply-demand based, however, research refutes
this claim )
For more info : http://en.wikipedia.org/wiki/Amazon_Elastic_Compute_Cloud
http://aws.amazon.com/ec2/

42. • Operating systems
• Persistent storage
• Elastic IP addresses
• Automated scaling
• Reliability
• Multiple Locations
• Amazon Virtual Private Cloud
• Elastic Load Balancing
• High Performance Computing (HPC) Clusters
• High I/O Instances
• High Storage Instances
• VM Import/Export

43. • Elastic Web-Scale Computing
• Completely Controlled
• Flexible Cloud Hosting Services
• Designed for use with other Amazon Web Services
• Reliable
• Secure [For more information on Amazon EC2 security refer to our Amazon Web
Services: Overview of Security Process document.]
• Inexpensive
• On-Demand Instances

44. To use Amazon EC2, you simply:
• Select a pre-configured, template Amazon Machine Image (AMI) to get up and running
immediately. Or create an AMI containing your applications, libraries, data, and associated
configuration settings.
• Configure security and network access on your Amazon EC2 instance.
• Choose which instance type(s) you want, then start, terminate, and monitor as many
instances of your AMI as needed, using the web service APIs or the variety of management
tools provided.
• Determine whether you want to run in multiple locations, utilize static IP endpoints, or
attach persistent block storage to your instances.
• Pay only for the resources that you actually consume, like instance-hours or data transfer.

45. • Detection of malfunction can be handled.
• Door sensors can help to detect the status of refrigerator door.
• Sensors data can be used by third party vendors for analytics
purpose.
• In large systems like Hotels, were many refrigerators are used, the
detection of malfunction of any refrigerator can be easily
identified.
• User will get the service at efficient level.

47. • In this presentation, we introduced a concept of Sensing as a Service (SaaS) in
refrigerators, and identified unique challenges of developing SaaS cloud, which
include: 1) support for various sensing applications; 2) Hadoop in cloud 3) detection
of malfunction; 4) Using sensor data for analytics purpose by 3rd party vendors.
• The sensor data can be used at different levels to detect malfunction in any system.
• The traditional approach of Data analysis is changing because of data usage at
community level generated by sensor.