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CS 423


Operating Systems Design

Lecture 33
Cloud Computing


Cristina Abad

Fall 2010

Takeaway


The new
i
nformation
t
echnologies are
disruptive


Utility computing is changing industry


Efficient information sharing provides a
new model for business



Overview


What is meant by


Cloud Computing


Utility Computing


{Infrastructure, Platform, Software} as a
Service


Why do corporations need to pay attention


General
principles


Cloud
Computing
Testbed

(CCT
)


Research


Tremendous Buzz

“No less influential than e
-
business”

(Gartner, 2008)

“Cloud computing achieves a
quicker return on
investment


(Lindsay Armstrong of
salesforce.com, Dec 2008)

“ Economic downturn, the
appeal of that cost advantage
will be greatly magnified"


(IDC, 2008)

“Revolution, the biggest upheaval
since the invention of
the PC

in the 1970s […] IT departments will have little left to
do once the bulk of business computing shifts […] into the
cloud”

(Nicholas Carr, 2008)


Not only is it
faster and more
flexible
, it is
cheaper
. […] the
emergence of cloud models
radically
alters the cost benefit decision


(FT Mar 6, 2009)

The economics are
compelling
, with business
applications made three to five times cheaper and
consumer applications five to 10 times
cheaper

(Merrill Lynch, May, 2008)

Gartner Hype Cycle*

Cloud
Computing

* From
http://en.wikipedia.org/wiki/Hype_cycle


Cloud Computing

Computing paradigm where the boundaries
of computing will be determined by
economic rationale rather than technical
limits

Professor
Ramnath

Chellappa

Emory University


It is not just Grid, Utility, or Autonomic
computing
.

Cloud
Characteristics


On
-
demand self
-
service


Ubiquitous network access


Location independent resource
pooling


Rapid elasticity


Pay per use



7

Delivery
Models


Software
as a Service (
SaaS
)


Use provider’s applications over a network


SalesForce.com


Platform
as a Service (
PaaS
)


Deploy
customer
-
created applications to a cloud


AppEng


Infrastructure
as a Service (
IaaS
)


Rent processing, storage, network capacity, and other
fundamental computing
resources


EC2, S3

8

Software Stack

Mobile (Android), Thin client (
Zonbu
)
Thick client (Google Chrome)

Identity, Integration Payments, Mapping,
Search, Video Games, Chat

Peer
-
to
-
peer (
Bittorrent
), Web app
(twitter),
SaaS

(Google Apps, SAP)

Java Google Web Toolkit,
Django
, Ruby on
Rails, .NET

S3,
Nirvanix
,
Rackspace

Cloud Files,
Savvis
,

Full virtualization (
GoGrid
), Management
(
RightScale
), Compute (EC2), Platform
(Force.com)


Services

Application

Pl atform

Storage

Infrastructure

Success?


Salesforce.com


Customer Relations Management


1999
Benioff

took over company


Killed off Siebel Systems (Big Switch [1])


Made profit through recession (AMR
Research):


FY2009 4Q growth of 44% over 2008,


Yearly revenue >$1B

Recent Trends[2]

Google App Engine

(April 2008)

Microsoft Azure

(Oct 2008)

Facebook

Platform

(May 2007)

Amazon EC2

(August 2006)

Amazon S3

(March 2006)

Salesforce

AppExchange

(March 2006)

Utility Computing

“Computing may someday be organized as a
public utility, just as the telephone system
is organized as a public utility”

John McCarthy, 1961

Perils of Corporate Computing


Own information systems



However


Capital investment



Heavy fixed costs



Redundant expenditures



High energy cost, low CPU utilization



Dealing with unreliable hardware



High
-
levels of overcapacity (Technology and Labor)



NOT SUSTAINABLE

CPU Utilization [2]


Activity profile of a sample of 5,000 Google Servers over a period of 6 months

Energy Overhead [2]


Subsystem Power Usage[2]


Subsystem power usage in an x86 server as the compute load varies from
idle to full usage.

Service Disruptions[2]


Machine Restarts[2]


Distributions of machine restarts over 6 months at Google

Machine Downtime[2]


Distribution of machine downtime, observed at Google over 6 months. The
average annualized restart rate across all machines is 4.2, corresponding to a
mean time between restarts of just less than 3 months.

Utility Computing


Let economy of scale prevail


Outsource all the trouble to someone else


The utility provider will share the
overhead costs among many customers,
amortizing the costs


You only pay for:



the amortized overhead



Your real CPU / Storage / Bandwidth usage

Why Utility Computing Now


Large data stores


Fiber networks


Commodity computing


Multicore

machines

+


Huge data sets


Utilization/Energy


Shared people

Utility Computin
g

Data Intensive Computing


Data collection too large to transmit economically
over Internet
---

Petabyte

data collections


Computation produces small data output
containing a high density of information


Implemented in Clouds


Easy to write programs, fast turn around.


MapReduce
.


Map(k1, v1)
-
> list (k2, v2)


Reduce(k2,list(v2))
-
> list(v3)


Hadoop
, PIG, HDFS,
Hbase



Sawzall
, Google File System,
BigTable


References

1.
The Big Switch rewiring the world from
Edison to Google, Nicholas G Carr,
Norton & Company, Ltd, 2008

2.
The Datacenter as a Computer
An
Introduction to the Design of Warehouse
-
Scale Machines,
Luiz André Barroso and
Urs Hölzle,
Morgan & Claypool, 2009

http://www.morganclaypool.com/doi/pdf
/10.2200/S00193ED1V01Y200905CAC0
06