This document discusses cloud computing paradigms. It outlines the history and growth of cloud computing, driven by large internet companies developing scalable infrastructure and data centers. It describes advantages for both service providers and end users, such as simplified installation and maintenance, centralized updates, and on-demand scaling. New applications like mobile and analytics are discussed. Cloud providers operate under a utility computing model of low commitment and pay-as-you-go services.

1. 2: Cloud Computing Paradigms
Zubair Nabi
zubair.nabi@itu.edu.pk
April 17, 2013
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2. Outline
1 Introduction
2 Cloud service providers
3 Utility Computing
4 Economics
5 Challenges
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3. Outline
1 Introduction
2 Cloud service providers
3 Utility Computing
4 Economics
5 Challenges
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4. Cloud computing
A realization of utility computing in which computation, storage, and
services are offered as a metered service
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5. Cloud computing
A realization of utility computing in which computation, storage, and
services are offered as a metered service
Encompasses applications delivered as services over the Internet and
hardware and software in the datacenters that enable those services
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6. Cloud computing
A realization of utility computing in which computation, storage, and
services are offered as a metered service
Encompasses applications delivered as services over the Internet and
hardware and software in the datacenters that enable those services
Software as a Service (SaaS)
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7. Cloud computing
A realization of utility computing in which computation, storage, and
services are offered as a metered service
Encompasses applications delivered as services over the Internet and
hardware and software in the datacenters that enable those services
Software as a Service (SaaS)
Public Cloud: If available to the public as a pay-as-you-go model, e.g.
Amazon Web Services, Google AppEngine, and Microsoft Azure
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8. Cloud computing
A realization of utility computing in which computation, storage, and
services are offered as a metered service
Encompasses applications delivered as services over the Internet and
hardware and software in the datacenters that enable those services
Software as a Service (SaaS)
Public Cloud: If available to the public as a pay-as-you-go model, e.g.
Amazon Web Services, Google AppEngine, and Microsoft Azure
Private Cloud: Internal datacenters of an organization that are not
publicly accessible
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9. Advantages
Advantages to both service providers and end users
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10. Advantages
Advantages to both service providers and end users
1 Service providers:
Simplified software installation and maintenance
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11. Advantages
Advantages to both service providers and end users
1 Service providers:
Simplified software installation and maintenance
Centralized control over versioning
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12. Advantages
Advantages to both service providers and end users
1 Service providers:
Simplified software installation and maintenance
Centralized control over versioning
No need to build, provision, and maintain a datacenter
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13. Advantages
Advantages to both service providers and end users
1 Service providers:
Simplified software installation and maintenance
Centralized control over versioning
No need to build, provision, and maintain a datacenter
On the fly scaling
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14. Advantages
Advantages to both service providers and end users
1 Service providers:
Simplified software installation and maintenance
Centralized control over versioning
No need to build, provision, and maintain a datacenter
On the fly scaling
2 End users:
“Anytime, anywhere” access
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15. Advantages
Advantages to both service providers and end users
1 Service providers:
Simplified software installation and maintenance
Centralized control over versioning
No need to build, provision, and maintain a datacenter
On the fly scaling
2 End users:
“Anytime, anywhere” access
Share data and collaborate easily
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16. Advantages
Advantages to both service providers and end users
1 Service providers:
Simplified software installation and maintenance
Centralized control over versioning
No need to build, provision, and maintain a datacenter
On the fly scaling
2 End users:
“Anytime, anywhere” access
Share data and collaborate easily
Safeguard data stored in the infrastructure (debatable)
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17. Outline
1 Introduction
2 Cloud service providers
3 Utility Computing
4 Economics
5 Challenges
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18. History
Phenomenal growth of Web services in late 90s and early 2000s
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19. History
Phenomenal growth of Web services in late 90s and early 2000s
Large Internet companies, including Amazon, eBay, Google, Microsoft,
Yahoo, etc., already had massive infrastructure
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20. History
Phenomenal growth of Web services in late 90s and early 2000s
Large Internet companies, including Amazon, eBay, Google, Microsoft,
Yahoo, etc., already had massive infrastructure
To keep up with demand, these companies also developed scalable
software infrastructure (think MapReduce, GFS, BigTable, Dynamo, etc.)
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21. History
Phenomenal growth of Web services in late 90s and early 2000s
Large Internet companies, including Amazon, eBay, Google, Microsoft,
Yahoo, etc., already had massive infrastructure
To keep up with demand, these companies also developed scalable
software infrastructure (think MapReduce, GFS, BigTable, Dynamo, etc.)
They also acquired the operational expertise to deter potential physical
and electronic attacks
Zubair Nabi 2: Cloud Computing Paradigms April 17, 2013 7 / 22

22. History
Phenomenal growth of Web services in late 90s and early 2000s
Large Internet companies, including Amazon, eBay, Google, Microsoft,
Yahoo, etc., already had massive infrastructure
To keep up with demand, these companies also developed scalable
software infrastructure (think MapReduce, GFS, BigTable, Dynamo, etc.)
They also acquired the operational expertise to deter potential physical
and electronic attacks
Therefore, they had already created extremely large datacenters to
leverage statistical multiplexing and bulk purchasing of infrastructure
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23. Incentive for providers
Incentives include revenue, leveraging existing investment, defending a
franchise, attacking an incumbent, leveraging customer relationships, and
becoming a platform
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24. Incentive for providers
Incentives include revenue, leveraging existing investment, defending a
franchise, attacking an incumbent, leveraging customer relationships, and
becoming a platform
Data centers are being established in seemingly arbitrary locations
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25. Incentive for providers
Incentives include revenue, leveraging existing investment, defending a
franchise, attacking an incumbent, leveraging customer relationships, and
becoming a platform
Data centers are being established in seemingly arbitrary locations
Reasons for choosing a location include costs of electricity, cooling,
labour, property, and taxes
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26. Incentive for providers
Incentives include revenue, leveraging existing investment, defending a
franchise, attacking an incumbent, leveraging customer relationships, and
becoming a platform
Data centers are being established in seemingly arbitrary locations
Reasons for choosing a location include costs of electricity, cooling,
labour, property, and taxes
Cooling and electricity account for 1/3rd of all costs!
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27. Incentive for providers
Incentives include revenue, leveraging existing investment, defending a
franchise, attacking an incumbent, leveraging customer relationships, and
becoming a platform
Data centers are being established in seemingly arbitrary locations
Reasons for choosing a location include costs of electricity, cooling,
labour, property, and taxes
Cooling and electricity account for 1/3rd of all costs!
Cheaper to ship data over fiber optic cables than to ship electricity over
high-voltage transmission lines
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28. New technology trends and business models
“High-touch, high-margin, high-commitment” provisioning of service to
“low-touch, low-margin, low-commitment”
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29. New technology trends and business models
“High-touch, high-margin, high-commitment” provisioning of service to
“low-touch, low-margin, low-commitment”
For instance:
Payment model in Web 1.0: Contractual arrangement with a payment
processing service such as VeriSign or Authorize.net; making it hard for
small businesses to accept credit card payment online
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30. New technology trends and business models
“High-touch, high-margin, high-commitment” provisioning of service to
“low-touch, low-margin, low-commitment”
For instance:
Payment model in Web 1.0: Contractual arrangement with a payment
processing service such as VeriSign or Authorize.net; making it hard for
small businesses to accept credit card payment online
Web 2.0: With PayPal-like services anyone can sign up and accept credit
payments without a contract and a long-term commitment
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31. New technology trends and business models
“High-touch, high-margin, high-commitment” provisioning of service to
“low-touch, low-margin, low-commitment”
For instance:
Payment model in Web 1.0: Contractual arrangement with a payment
processing service such as VeriSign or Authorize.net; making it hard for
small businesses to accept credit card payment online
Web 2.0: With PayPal-like services anyone can sign up and accept credit
payments without a contract and a long-term commitment
Another example:
Ad revenue model in Web 1.0: Set up a relationship with an ad placement
company, such as DoubleClick
Zubair Nabi 2: Cloud Computing Paradigms April 17, 2013 9 / 22

32. New technology trends and business models
“High-touch, high-margin, high-commitment” provisioning of service to
“low-touch, low-margin, low-commitment”
For instance:
Payment model in Web 1.0: Contractual arrangement with a payment
processing service such as VeriSign or Authorize.net; making it hard for
small businesses to accept credit card payment online
Web 2.0: With PayPal-like services anyone can sign up and accept credit
payments without a contract and a long-term commitment
Another example:
Ad revenue model in Web 1.0: Set up a relationship with an ad placement
company, such as DoubleClick
Web 2.0: Use Google AdSense
Zubair Nabi 2: Cloud Computing Paradigms April 17, 2013 9 / 22

33. New technology trends and business models
“High-touch, high-margin, high-commitment” provisioning of service to
“low-touch, low-margin, low-commitment”
For instance:
Payment model in Web 1.0: Contractual arrangement with a payment
processing service such as VeriSign or Authorize.net; making it hard for
small businesses to accept credit card payment online
Web 2.0: With PayPal-like services anyone can sign up and accept credit
payments without a contract and a long-term commitment
Another example:
Ad revenue model in Web 1.0: Set up a relationship with an ad placement
company, such as DoubleClick
Web 2.0: Use Google AdSense
This same model was used by Amazon Web Services in 2006:
pay-as-you-go computing with no contract, with the only requirement
being a credit card
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34. New applications
Mobile applications: Require high availability and rely on large data sets
that are most conveniently hosted in large datacenters
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35. New applications
Mobile applications: Require high availability and rely on large data sets
that are most conveniently hosted in large datacenters
Parallel batch processing: Analytics jobs that analyze terabytes of data
and can take hours to finish can leverage the “cost associativity” of the
cloud
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36. New applications
Mobile applications: Require high availability and rely on large data sets
that are most conveniently hosted in large datacenters
Parallel batch processing: Analytics jobs that analyze terabytes of data
and can take hours to finish can leverage the “cost associativity” of the
cloud
Business analytics: Understanding customers, supply chains, buying
habits, ranking, and so on
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37. New applications
Mobile applications: Require high availability and rely on large data sets
that are most conveniently hosted in large datacenters
Parallel batch processing: Analytics jobs that analyze terabytes of data
and can take hours to finish can leverage the “cost associativity” of the
cloud
Business analytics: Understanding customers, supply chains, buying
habits, ranking, and so on
Computation offloading: Compute-intensive tasks are offloaded to the
cloud. For instance, Matlab, Mathematica, image rendering, 3D
animations, etc.
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38. Outline
1 Introduction
2 Cloud service providers
3 Utility Computing
4 Economics
5 Challenges
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39. Classes of utility computing
Every application needs computation, storage, and quite possibly
communication
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40. Classes of utility computing
Every application needs computation, storage, and quite possibly
communication
These resources need to be virtualized to achieve elasticity and the
illusion of infinite capacity
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41. Classes of utility computing
Every application needs computation, storage, and quite possibly
communication
These resources need to be virtualized to achieve elasticity and the
illusion of infinite capacity
The details of statistical multiplexing and sharing is abstracted away from
the programmer
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42. Classes of utility computing
Every application needs computation, storage, and quite possibly
communication
These resources need to be virtualized to achieve elasticity and the
illusion of infinite capacity
The details of statistical multiplexing and sharing is abstracted away from
the programmer
Different utility computing offerings can be distinguished on the basis of
the abstraction presented to the programmer
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43. Bare metal hardware abstraction
An instance looks like physical hardware
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44. Bare metal hardware abstraction
An instance looks like physical hardware
Programmers control the entire software stack from the kernel upwards
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45. Bare metal hardware abstraction
An instance looks like physical hardware
Programmers control the entire software stack from the kernel upwards
Employed by Amazon EC2
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46. Bare metal hardware abstraction
An instance looks like physical hardware
Programmers control the entire software stack from the kernel upwards
Employed by Amazon EC2
A very thin API is exposed to request and configure virtualized hardware
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47. Bare metal hardware abstraction
An instance looks like physical hardware
Programmers control the entire software stack from the kernel upwards
Employed by Amazon EC2
A very thin API is exposed to request and configure virtualized hardware
No bar on the kinds of applications that can be hosted
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48. Bare metal hardware abstraction
An instance looks like physical hardware
Programmers control the entire software stack from the kernel upwards
Employed by Amazon EC2
A very thin API is exposed to request and configure virtualized hardware
No bar on the kinds of applications that can be hosted
Low level virtualization, block-device storage, and IP-level connectivity
allow developers to design any application
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49. Bare metal hardware abstraction
An instance looks like physical hardware
Programmers control the entire software stack from the kernel upwards
Employed by Amazon EC2
A very thin API is exposed to request and configure virtualized hardware
No bar on the kinds of applications that can be hosted
Low level virtualization, block-device storage, and IP-level connectivity
allow developers to design any application
On the downside, scalability and failover are application-dependent
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50. Domain-specific platform
Target traditional web applications
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51. Domain-specific platform
Target traditional web applications
Enforce an application structure of clean separation between a stateless
computation tier and a stateful storage tier
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52. Domain-specific platform
Target traditional web applications
Enforce an application structure of clean separation between a stateless
computation tier and a stateful storage tier
Employed by Google AppEngine
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53. Domain-specific platform
Target traditional web applications
Enforce an application structure of clean separation between a stateless
computation tier and a stateful storage tier
Employed by Google AppEngine
Applications are expected to be request-reply based
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54. Domain-specific platform
Target traditional web applications
Enforce an application structure of clean separation between a stateless
computation tier and a stateful storage tier
Employed by Google AppEngine
Applications are expected to be request-reply based
In contrast to the bare metal hardware abstraction, enable automatic
scaling and high-availability mechanisms
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55. Domain-specific platform
Target traditional web applications
Enforce an application structure of clean separation between a stateless
computation tier and a stateful storage tier
Employed by Google AppEngine
Applications are expected to be request-reply based
In contrast to the bare metal hardware abstraction, enable automatic
scaling and high-availability mechanisms
Not suitable for general-purpose computing
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56. Hybrid
Offer a sweet spot between flexibility and programmer convenience
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57. Hybrid
Offer a sweet spot between flexibility and programmer convenience
Offered by Microsoft’s Azure
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58. Hybrid
Offer a sweet spot between flexibility and programmer convenience
Offered by Microsoft’s Azure
Applications are written using .NET libraries and compiled to the Common
Language Runtime (A language-independent management environment)
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59. Hybrid
Offer a sweet spot between flexibility and programmer convenience
Offered by Microsoft’s Azure
Applications are written using .NET libraries and compiled to the Common
Language Runtime (A language-independent management environment)
Supports general purpose computing
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60. Hybrid
Offer a sweet spot between flexibility and programmer convenience
Offered by Microsoft’s Azure
Applications are written using .NET libraries and compiled to the Common
Language Runtime (A language-independent management environment)
Supports general purpose computing
Users have control over the choice of language but not the underlying OS
or runtime
Zubair Nabi 2: Cloud Computing Paradigms April 17, 2013 15 / 22

61. Hybrid
Offer a sweet spot between flexibility and programmer convenience
Offered by Microsoft’s Azure
Applications are written using .NET libraries and compiled to the Common
Language Runtime (A language-independent management environment)
Supports general purpose computing
Users have control over the choice of language but not the underlying OS
or runtime
Provide some degree of automatic failover and scalability but require
some help from the developer in the form of declaration of some
application properties
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62. Outline
1 Introduction
2 Cloud service providers
3 Utility Computing
4 Economics
5 Challenges
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63. Elasticity
Pay-as-you-go model: Only pay for what you use
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64. Elasticity
Pay-as-you-go model: Only pay for what you use
Add or remove resources at a fine grain (such as one server at a time)
with minimal lead time
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65. Elasticity
Pay-as-you-go model: Only pay for what you use
Add or remove resources at a fine grain (such as one server at a time)
with minimal lead time
Useful for traffic spikes such as “Black Friday”
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66. Elasticity
Pay-as-you-go model: Only pay for what you use
Add or remove resources at a fine grain (such as one server at a time)
with minimal lead time
Useful for traffic spikes such as “Black Friday”
Over time, hardware costs come down and vendors acquire updated
hardware. Thus, benefiting the tenant
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67. Reasons for companies to migrate to the cloud
Pay separately per resource: Pay proportional to resource requirements
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68. Reasons for companies to migrate to the cloud
Pay separately per resource: Pay proportional to resource requirements
Power, cooling, and physical plant costs: Cost of electricity and
cooling already factored in
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69. Reasons for companies to migrate to the cloud
Pay separately per resource: Pay proportional to resource requirements
Power, cooling, and physical plant costs: Cost of electricity and
cooling already factored in
Man-power costs: No need to employ sysadmins
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70. Reasons for companies to migrate to the cloud
Pay separately per resource: Pay proportional to resource requirements
Power, cooling, and physical plant costs: Cost of electricity and
cooling already factored in
Man-power costs: No need to employ sysadmins
Operational costs: Low-level upgrades and software patches
responsibility of the provider
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71. Outline
1 Introduction
2 Cloud service providers
3 Utility Computing
4 Economics
5 Challenges
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72. Obstacles
1 Service availability: Possibility of cloud outage
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73. Obstacles
1 Service availability: Possibility of cloud outage
2 Data lock-in: Reliance on cloud specific APIs
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74. Obstacles
1 Service availability: Possibility of cloud outage
2 Data lock-in: Reliance on cloud specific APIs
3 Security: Requires strong encrypted storage, VLANs, and network
middleboxes (firewalls, etc.)
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75. Obstacles
1 Service availability: Possibility of cloud outage
2 Data lock-in: Reliance on cloud specific APIs
3 Security: Requires strong encrypted storage, VLANs, and network
middleboxes (firewalls, etc.)
4 Data transfer bottlenecks: Moving large amounts of data in and out is
expensive
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76. Obstacles
1 Service availability: Possibility of cloud outage
2 Data lock-in: Reliance on cloud specific APIs
3 Security: Requires strong encrypted storage, VLANs, and network
middleboxes (firewalls, etc.)
4 Data transfer bottlenecks: Moving large amounts of data in and out is
expensive
5 Performance unpredictability: Resource sharing between applications
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77. Obstacles (2)
6 Scalable storage: No standard model to arbitrarily scale storage up and
down on-demand while ensuring data durability and high availability
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78. Obstacles (2)
6 Scalable storage: No standard model to arbitrarily scale storage up and
down on-demand while ensuring data durability and high availability
7 Bugs in large-scale distributed systems: Hard to debug large-scale
applications in full deployment
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79. Obstacles (2)
6 Scalable storage: No standard model to arbitrarily scale storage up and
down on-demand while ensuring data durability and high availability
7 Bugs in large-scale distributed systems: Hard to debug large-scale
applications in full deployment
8 Scaling quickly: Automatically scaling while conserving resources and
money is an open ended problem
Zubair Nabi 2: Cloud Computing Paradigms April 17, 2013 21 / 22

80. Obstacles (2)
6 Scalable storage: No standard model to arbitrarily scale storage up and
down on-demand while ensuring data durability and high availability
7 Bugs in large-scale distributed systems: Hard to debug large-scale
applications in full deployment
8 Scaling quickly: Automatically scaling while conserving resources and
money is an open ended problem
9 Reputation fate sharing: Bad behaviour by one tenant can reflect badly
on the rest
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81. Obstacles (2)
6 Scalable storage: No standard model to arbitrarily scale storage up and
down on-demand while ensuring data durability and high availability
7 Bugs in large-scale distributed systems: Hard to debug large-scale
applications in full deployment
8 Scaling quickly: Automatically scaling while conserving resources and
money is an open ended problem
9 Reputation fate sharing: Bad behaviour by one tenant can reflect badly
on the rest
10 Software licensing: Gap between pay-as-you-go model and software
licensing
Zubair Nabi 2: Cloud Computing Paradigms April 17, 2013 21 / 22

82. References
1 Above the Clouds: A Berkeley View of Cloud Computing" by Michael
Armbrust, Armando Fox, Rean Griffith, Anthony D. Joseph, Randy Katz,
Andy Konwinski, Gunho Lee, David Patterson, Ariel Rabkin, Ion Stoica,
and Matei Zaharia. Technical Report EECS-2009-28, EECS Department,
University of California, Berkeley.
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