1. Introduction:A Golden Era in
Introduction:A Golden Era in
Computing
Computing
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2. Cloud Concepts, Enabling-
Cloud Concepts, Enabling-
technologies, and Models: The
technologies, and Models: The
Cloud Context
Cloud Context
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3. Evolution of Internet Computing
Evolution of Internet Computing
Publish
Inform
Interact
Integrate
Transact
Discover
(intelligence)
Automate
(discovery)
time
scale
Social
media
and
networking
Semantic
discovery
Data-intensive
HPC, cloud
web
deep web
Data
marketplace
and
analytics
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4. Top Ten Largest Databases
Top Ten Largest Databases
Ref: http://www.focus.com/fyi/operations/10-largest-databases-in-the-world/
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5. Challenges
Challenges
• Alignment with the needs of the business / user / non-
computer specialists / community and society
• Need to address the scalability issue: large scale data,
high performance computing, automation, response
time, rapid prototyping, and rapid time to production
• Need to effectively address (i) ever shortening cycle of
obsolescence, (ii) heterogeneity and (iii) rapid changes
in requirements
• Transform data from diverse sources into intelligence
and deliver intelligence to right people/user/systems
• What about providing all this in a cost-effective
manner?
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6. Enter the cloud
Enter the cloud
• Cloud computing is Internet-based computing,
whereby shared resources, software and
information are provided to computers and other
devices on-demand, like the electricity grid.
• The cloud computing is a culmination of numerous
attempts at large scale computing with seamless
access to virtually limitless resources.
o on-demand computing, utility computing, ubiquitous
computing, autonomic computing, platform computing,
edge computing, elastic computing, grid computing, …
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7. “
“Grid Technology
Grid Technology
• Emerging enabling technology.
• Natural evolution of distributed systems and the Internet.
• Middleware supporting network of systems to facilitate
sharing, standardization and openness.
• Infrastructure and application model dealing with sharing of
compute cycles, data, storage and other resources.
• Publicized by prominent industries as on-demand computing,
utility computing, etc.
• Move towards delivering “computing” to masses similar to
other utilities (electricity and voice communication).”
• Now,
Hmmm…sounds like the definition for cloud computing!!!!!
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8. It is a changed world now…
It is a changed world now…
• Explosive growth in applications: biomedical informatics, space
exploration, business analytics, web 2.0 social networking: YouTube,
Facebook
• Extreme scale content generation: e-science and e-business data
deluge
• Extraordinary rate of digital content consumption: digital gluttony:
Apple iPhone, iPad, Amazon Kindle
• Exponential growth in compute capabilities: multi-core, storage,
bandwidth, virtual machines (virtualization)
• Very short cycle of obsolescence in technologies: Windows Vista
Windows 7; Java versions; CC#; Phython
• Newer architectures: web services, persistence models, distributed file
systems/repositories (Google, Hadoop), multi-core, wireless and
mobile
• Diverse knowledge and skill levels of the workforce
• You simply cannot manage this complex situation with your
traditional IT infrastructure:
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9. Answer: The Cloud Computing?
Answer: The Cloud Computing?
• Typical requirements and models:
o platform (PaaS),
o software (SaaS),
o infrastructure (IaaS),
o Services-based application programming interface (API)
• A cloud computing environment can provide one
or more of these requirements for a cost
• Pay as you go model of business
• When using a public cloud the model is similar to
renting a property than owning one.
• An organization could also maintain a private cloud
and/or use both.
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11. Common Features of Cloud Providers
Common Features of Cloud Providers
Development
Environment:
IDE, SDK, Plugins
Production
Environment
Simple
storage
Table Store
<key,
value>
Drives
Accessible through
Web services
Management Console and Monitoring tools
& multi-level security
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12. Windows Azure
Windows Azure
• Enterprise-level on-demand capacity builder
• Fabric of cycles and storage available on-request
for a cost
• You have to use Azure API to work with the
infrastructure offered by Microsoft
• Significant features: web role, worker role , blob
storage, table and drive-storage
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13. Amazon EC2
Amazon EC2
• Amazon EC2 is one large complex web service.
• EC2 provided an API for instantiating computing
instances with any of the operating systems
supported.
• It can facilitate computations through Amazon
Machine Images (AMIs) for various other models.
• Signature features: S3, Cloud Management
Console, MapReduce Cloud, Amazon Machine
Image (AMI)
• Excellent distribution, load balancing, cloud
monitoring tools
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14. Google App Engine
Google App Engine
• This is more a web interface for a development
environment that offers a one stop facility for
design, development and deployment Java and
Python-based applications in Java, Go and Python.
• Google offers the same reliability, availability and
scalability at par with Google’s own applications
• Interface is software programming based
• Comprehensive programming platform irrespective
of the size (small or large)
• Signature features: templates and appspot,
excellent monitoring and management console
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15. Demos
Demos
• Amazon AWS: EC2 & S3 (among the many
infrastructure services)
o Linux machine
o Windows machine
o A three-tier enterprise application
• Google app Engine
o Eclipse plug-in for GAE
o Development and deployment of an application
• Windows Azure
o Storage: blob store/container
o MS Visual Studio Azure development and production environment
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17. The Context: Big-data
The Context: Big-data
• Data mining huge amounts of data collected in a wide range of
domains from astronomy to healthcare has become essential for
planning and performance.
• We are in a knowledge economy.
o Data is an important asset to any organization
o Discovery of knowledge; Enabling discovery; annotation of
data
o Complex computational models
o No single environment is good enough: need elastic, on-
demand capacities
• We are looking at newer
o Programming models, and
o Supporting algorithms and data structures.
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18. Google File System
Google File System
• Internet introduced a new challenge in the form web
logs, web crawler’s data: large scale “peta scale”
• But observe that this type of data has an uniquely
different characteristic than your transactional or the
“customer order” data : “write once read many
(WORM)” ;
• Privacy protected healthcare and patient information;
• Historical financial data;
• Other historical data
• Google exploited this characteristics in its Google file
system (GFS)
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19. What is Hadoop?
What is Hadoop?
At Google MapReduce operation are run on a
special file system called Google File System (GFS)
that is highly optimized for this purpose.
GFS is not open source.
Doug Cutting and others at Yahoo! reverse
engineered the GFS and called it Hadoop Distributed
File System (HDFS).
The software framework that supports HDFS,
MapReduce and other related entities is called the
project Hadoop or simply Hadoop.
This is open source and distributed by Apache.
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20. Fault tolerance
Fault tolerance
• Failure is the norm rather than exception
• A HDFS instance may consist of thousands of server
machines, each storing part of the file system’s data.
• Since we have huge number of components and that
each component has non-trivial probability of failure
means that there is always some component that is
non-functional.
• Detection of faults and quick, automatic recovery from
them is a core architectural goal of HDFS.
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22. Hadoop Distributed File System
Hadoop Distributed File System
Application
Local file
system
Master node
Name Nodes
HDFS Client
HDFS Server
Block size: 2K
Block size: 128M
Replicated
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23. What is MapReduce?
What is MapReduce?
MapReduce is a programming model Google has used
successfully is processing its “big-data” sets (~ 20000 peta
bytes per day)
A map function extracts some intelligence from raw
data.
A reduce function aggregates according to some guides
the data output by the map.
Users specify the computation in terms of a map and a
reduce function,
Underlying runtime system automatically parallelizes the
computation across large-scale clusters of machines,
and
Underlying system also handles machine failures, efficient
communications, and performance issues.
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24. Classes of problems “mapreducable”
Classes of problems “mapreducable”
Benchmark for comparing: Jim Gray’s challenge on data-
intensive computing. Ex: “Sort”
Google uses it for wordcount, adwords, pagerank, indexing
data.
Simple algorithms such as grep, text-indexing, reverse
indexing
Bayesian classification: data mining domain
Facebook uses it for various operations: demographics
Financial services use it for analytics
Astronomy: Gaussian analysis for locating extra-terrestrial
objects.
Expected to play a critical role in semantic web and in
web 3.0
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26. MapReduce Engine
MapReduce Engine
• MapReduce requires a distributed file system and an
engine that can distribute, coordinate, monitor and
gather the results.
• Hadoop provides that engine through (the file system
we discussed earlier) and the JobTracker +
TaskTracker system.
• JobTracker is simply a scheduler.
• TaskTracker is assigned a Map or Reduce (or other
operations); Map or Reduce run on node and so is
the TaskTracker; each task is run on its own JVM on a
node.
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27. Demos
Demos
• Word count application: a simple foundation for
text-mining; with a small text corpus of inaugural
speeches by US presidents
• Graph analytics is the core of analytics involving
linked structures (about 110 nodes): shortest path
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28. Public Cloud vs. Private Cloud
Public Cloud vs. Private Cloud
Rationale for Private Cloud:
• Security and privacy of business data was a big
concern
• Potential for vendor lock-in
• SLA’s required for real-time performance and
reliability
• Cost savings of the shared model achieved
because of the multiple projects involving semantic
technologies that the company is actively
developing
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29. Cloud Computing for the Enterprise
Cloud Computing for the Enterprise
What should IT Do
What should IT Do
• Revise cost model to utility-based computing:
CPU/hour, GB/day etc.
• Include hidden costs for management, training
• Different cloud models for different applications -
evaluate
• Use for prototyping applications and learn
• Link it to current strategic plans for Services-Oriented
Architecture, Disaster Recovery, etc.
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31. Summary
Summary
• We illustrated cloud concepts and demonstrated the
cloud capabilities through simple applications
• We discussed the features of the Hadoop File System,
and mapreduce to handle big-data sets.
• We also explored some real business issues in
adoption of cloud.
• Cloud is indeed an impactful technology that is sure
to transform computing in business.
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