Cloud Computing - The CLOUDS Lab

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Dec 4, 2013 (3 years and 9 months ago)

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Manjra
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Cloud Computing:

The Next Revolution in Information Technology


Manjra
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Cloud Computing:

The Next Revolution in Information Technology

Dr. Suraj Pandey and Dr. Christian Vecchiola

Clou
d Computing and
D
istributed
S
ystems (CLOUDS) Lab

Dept. of Computer Science and Software Engineering

The University of Melbourne, Australia

www.cloudbus.org

www.buyya.com

www.manjrasoft.com



Major Sponsors/Supporters

Manjra
soft
Dr Rajkumar Buyya
Chief Executive Officer
Manjrasoft Pty Ltd
Room 5.31, ICT Building, 111, Barry Street, Carlton,
Melbourne, VIC 3053, Australia
P: +61
-
3
-
8344 1344 | F : +61
-
3
-
9348 1184
E: raj@manjrasoft.com
http://www.manjrasoft.com
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Innovative Solutions for Cloud Computing
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The CLOUDS Lab @ Melbourne



Part of a World
-
class Melbourne Universit
y:


Rated as
#1 in Australia

(Times Higher
Education)


Often rated amongst the
top 20 or 30 in the
world
.


Youngest and one of the rapidly growing
research labs in the University:


Founded in 2002 (formerly, GRIDS Lab)


Houses 20+ researchers consisting of:


Research Fellows/PostDocs


Software Engineers


PhD candidates


Funding


Australian Research Council & DIISR


Many industries (Sun, IBM, Microsoft, CA)


Many national and international
collaborations.


Academics
-

Science, Engineering, Medicine


Industries


Software:


Used in over 40 countries by academic and
industrial users.


Publication:


Produces over 20% of our Dept’s output.

Education

R & D

+ Community Services: e.g., IEEE TC for Scalable Computing

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www.manjrasoft.com

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Books at Glance:

Co
-
authored/edited

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Cloud Compting

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Overall Agenda


Introduction to Recent Advances in Parallel and Distributed
Computing

Introduction to Cloud computing

Cloud Computing with Manjrasoft Aneka

Building Enterprise Cloud, Applications and Teaching
Cloud/Grid/Distributed Computing

Discussion and Q&A

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Drivers for New Advances in
Parallel/Distributed Computing:

Resource Hungry Applications


Solving grand challenge applications using
modeling
,
simulation

and
analysis

Life Sciences

CAD/CAM

Aerospace

Military Applications

Digital Biology

Military Applications

Military Applications

Internet &

Ecommerce

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How to Run Applications Faster ?


There are 3 ways to improve performance:


Work Harder


Work Smarter


Get Help


Computer Analogy


Build faster hardware


Develop Optimized algorithms and techniques used to
solve computational tasks


Utilise multiple computers to solve a particular task

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No. of Processors

C.P.I

1 2 . . . .

Computational Power Improvement

(Horizontal integration)

Multiprocessor

Uniprocessor

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2100

2100

2100

2100

2100

2100

2100

2100

2100

Personal Device

SMPs or
SuperComputers

Local

Cluster

Global

Grid/InterClo
ud

S

E

R

V


I

C

E

S

+

P
E
R
F
O
R
M
A
N
C
E

Inter Planet

Grid


Individual


Group


Department


Campus


State


National


Globe


Inter Planet


Universe

Administrative Barriers

Compute

Cloud

Computing is Scaling:


Towards Inter
-
Planetary Level

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New IT Trends


Ownership
-
> Leasing Model


Infrastructure, platform, applications


PC
-
> Internet Data Center


Building applications for a single user


large
number of concurrent consumers


Web 2.0 & Virtualisation


Building 3
rd

party IT services by composing multiple
independent services


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Cloud Computing:

The Next Revolution in Information Technology


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The Next Revolution in IT

The Big Switch in IT


Classical Computing


Buy & Own


Hardware, System
Software, Applications
often to meet peak needs.


Install, Configure,
Test, Verify, Evaluate


Manage


..


Finally, use it


$$$$....$(High CapEx)


Cloud Computing


Subscribe


Use







$
-

pay for what you
use, based on QoS

Every 18 months?

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Outline


“Computer Utilities”


Vision and Promising IT Paradigms/Platforms


Cloud Computing and Related Paradigms


Trends, Definition, Cloud Benefits and Challenges


Market
-
Oriented Cloud Architecture


SLA
-
oriented
Resource Allocation


Global Cloud Exchange


Emerging Cloud Platforms


Cloudbus: Melbourne Cloud Computing
Initiative


Summary and Thoughts for Future

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4 Essential Utilities and

Delivery Networks

(1) Water

Water

Distribution

Network

(4) Telephone

Telecom

Networks

(3) Gas

(2) Electricity

Power Grid

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Computing Paradigms and
Attributes:
Realizing
the ‘Computer
Utilities’ Vision


Web


Data Centres


Utility Computing


Service Computing


Grid Computing


P2P Computing


Market
-
Oriented
Computing


Cloud Computing




-
Ubiquitous

-
Reliable

-
Scalable

-
Autonomic

-
Dynamic

discovery

-

Composable

-
QoS

-
SLA

-



+

Paradigms

Attributes/Capabilities

-
Trillion $ business

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Outline


“Computer Utilities”


Vision and Promising IT Paradigms/Platforms


Cloud Computing and Related Paradigms


Trends, Definition, Cloud Benefits and Challenges


Market
-
Oriented Cloud Architecture


SLA
-
oriented
Resource Allocation


Global Cloud Exchange


Emerging Cloud Platforms


Cloudbus: Melbourne Cloud Computing
Initiative


Summary and Thoughts for Future

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Defining Clouds: There are many
views for what is cloud computing?


Over 20 definitions:


http://cloudcomputing.sys
-
con.com/read/612375_p.htm


Renting “remote storage”


backup


Renting “remote server”


hosting Web server


Renting “remote more servers”


to manage large workload


Buyya’s Scientific definition of Cloud Computing



“Cloud is a
market
-
oriented

distributed computing system
consisting of a collection of inter
-
connected and
virtualised

computers that are
dynamically provisioned

and presented
as one or more unified computing resources based on
service
-
level agreements (SLA)

established through
negotiation

between the service provider and consumers.”


SLA = {negotiated and agreed QoS parameters +
rewards + penalties for violation of agreement....}

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Subscription

Oriented Cloud Services:


X{compute, apps, data, ..}

as a Service (..aaS)


Clients

Other

Cloud Services

Govt.

Cloud Services

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Cloud Services


Infrastructure as a Service
(IaaS)


CPU, Storage: Amazon.com,
Nirvanix, GoGrid….


Platform as a Service (PaaS)


Google App Engine, Microsoft
Azure, Manjrasoft Aneka..


Software as a Service (SaaS)


SalesForce.Com


Infrastructure as a Service (IaaS)

Software as a Service (SaaS)

Platform as a Service (PaaS)

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....

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Cloud Deployment Models

Private/Enterprise
Clouds

Cloud computing

model run

within a company’s

own Data Center /

infrastructure for

internal and/or


partners use.


Public/Internet
Clouds

3rd party,

multi
-
tenant Cloud

infrastructure

& services:



* available on

subscription basis

(pay as you go)

Hybrid/Mixed Clouds

Mixed usage of

private and public

Clouds:

Leasing public

cloud services

when private cloud

capacity is

insufficient

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Cloud Applications


Scientific/Tech Applications


Business Applications


Consumer/Social Applications

Science and Technical Applications

Business Applications

Consumer/Social Applications

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(Promised) Benefits of (Public)
Clouds


No upfront infrastructure investment


No procuring hardware, setup, hosting, power, etc..


On demand access


Lease what you need and when you need..


Efficient Resource Allocation


Globally shared infrastructure, can always be kept busy by serving
users from different time zones/regions...


Nice Pricing


Based on Usage, QoS, Supply and Demand, Loyalty, …


Application Acceleration


Parallelism for large
-
scale data analysis, what
-
if scenarios studies…


Highly Availability, Scalable, and Energy Efficient


Supports Creation of 3
rd

Party Services & Seamless offering


Builds on infrastructure and follows similar Business model as Cloud


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Outline


“Computer Utilities”


Vision and Promising IT Paradigms/Platforms


Cloud Computing and Related Paradigms


Trends, Definition, Cloud Benefits and Challenges


Market
-
Oriented Cloud Architecture


SLA
-
oriented
Resource Allocation


Global Cloud Exchange


Emerging Cloud Platforms


Cloudbus: Melbourne Cloud Computing
Initiative


Summary and Thoughts for Future

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Cloud to expand/shrink leasing of
resources with QoS/demand
variation

Dispatcher
VM
Monitor
Service Request
Monitor
Pricing
Accounting
Service Request Examiner and
Admission Control
-

Customer
-
driven Service Management
-

Computational Risk Management
-

Autonomic Resource Management
Users
/
Brokers
SLA
Resource
Allocator
Virtual
Machines
(
VMs
)
Physical
Machines
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Cloud to expand (lease more
resources) with increase in demand

Dispatcher
VM
Monitor
Service Request
Monitor
Pricing
Accounting
Service Request Examiner and
Admission Control
-

Customer
-
driven Service Management
-

Computational Risk Management
-

Autonomic Resource Management
Users
/
Brokers
SLA
Resource
Allocator
Virtual
Machines
(
VMs
)
Physical
Machines
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Cloud to shrink (unlease resources)
with decrease in demand

Dispatcher
VM
Monitor
Service Request
Monitor
Pricing
Accounting
Service Request Examiner and
Admission Control
-

Customer
-
driven Service Management
-

Computational Risk Management
-

Autonomic Resource Management
Users
/
Brokers
SLA
Resource
Allocator
Virtual
Machines
(
VMs
)
Physical
Machines
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A (Layered) Cloud Architecture

Cloud resources




Virtual Machine (VM), VM Management and Deployment

QoS Negotiation, Admission Control,
Pricing,
SLA Management,
Monitoring, Execution Management, Metering, Accounting, Billing

Cloud programming: environments and tools

Web 2.0 Interfaces, Mashups, Concurrent and Distributed
Programming, Workflows, Libraries, Scripting

Cloud applications

Social computing, Enterprise, ISV, Scientific, CDNs, ...

Adaptive Management

Core

Middleware

User
-
Level

Middleware

System level

User level

Autonomic / Cloud Economy

Apps Hosting Platforms

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Outline


21
st

Century Vision of Computing


Promising Computing Paradigms


Cloud Computing and Related Paradigms


Trends, Definition, Characteristics, Architecture


Market
-
Oriented Cloud Architecture


SLA
-
oriented
Resource Allocation


Global Cloud Exchange


Emerging Cloud Platforms


Cloudbus: Melbourne Cloud Computing
Initiative


Summary and Thoughts for Future

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Some Commercial
-
Oriented Cloud
platforms/technologies


System

Property

Amazon

EC2 & S3

Google

App Engine

Microsoft

Azure

Manjrasoft

Aneka

Focus

IaaS

IaaS/PaaS

IaaS/PaaS

PaaS

Service Type

Compute (EC2),

Storage (S3)

Web apps

Web and non
-
web
apps

Compute/Data

Virtualisation

OS Level: Xen

Apps container

OS level/Hyper
-
V

Resource Manager
and Scheduler

Dynamic
Negotiation of
QoS

None

None

None

SLA
-
oriented/

Resource
Reservation

User Access
Interface

EC2 Command
-
line
Tools

Web
-
based
Administration
Console

Windows Azure
portal

Workbench, Tools

Web APIs

Yes

Yes

Yes

Yes

Value
-
added
Service Providers

Yes

No

Yes

No

Programming
Framework

Amazon Machine
Image (AMI)

Python

.NET framework

Multiple App models
in.NET languages

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Outline


“Computer Utilities”


Vision and Promising IT Paradigms/Platforms


Cloud Computing and Related Paradigms


Trends, Definition, Cloud Benefits and Challenges


Market
-
Oriented Cloud Architecture


SLA
-
oriented
Resource Allocation


Global Cloud Exchange


Emerging Cloud Platforms


Cloudbus: Melbourne Cloud Computing
Initiative


Summary and Thoughts for Future

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Cloudbus@CLOUDS Lab:

Melbourne Cloud Computing Initiative


Market
-
Oriented Clouds


SLA
-
based Resource Management


Global Cloud Exchange Elements: Brokers


Aneka


.NET
-
based Cloud Computing


PaaS for Enterprise and Public Clouds


Scaling Across Clouds (Meta Brokering)


Harnessing Compute
resources


Federation of clouds for application scaling across distributed
resources


3
rd

Party Cloud Services (e.g., MetaCDN)


Harnessing Storage
resources


Building Content Delivery Networks using different “vendors” Storage
Clouds


Green Clouds / Data Centers


Energy Efficiency and QoS Oriented Resource Allocation


CloudSim: Toolkit for Simulation of Clouds


Design and evaluation for resource management policies & algorithms

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PEX
OSC
IBM Blue Cloud
multimedia
education
science
engineering
business
….
bag of tasks
parameter sweep
workflow
message passing
map
-
reduce
….
Aneka
Task Model
Thread Model
PSM
MapReduce
Accounting
Metering
Pricing
Billing
QoS and SLA
Res. Provisioning & Reservation
Workflow
Engine
Broker
Cloudbus Middleware
Other Middleware
Market Maker
Meta
-
broker
Web 2.0 Interfaces: Ajax, Web Services, Service Portals, REST
Desktop Computers
Applications
Models
Technology
InterCloud
Public Cloud Infrastructure
Amazon EC2 / S3
Microsoft Azure
Google AppEngine
Core Technologies
Infrastructure
PaaS Vendor 1
PaaS Vendor 3
PaaS Vendor 2
physics
biology
medicine
optimization
modeling
CAD
teaching
risk analysis
banking
finance
3D rendering
imaging
Scalable Execution Application Manager
Cluster Datacenter
social
networking
blogging
photos
Innovative Solutions for

Simplifying and Accelerating Applications

in .NET
-
based Cloud Computing Environments

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Infrastructure as a Service (IaaS)
Software as a Service (SaaS)
Platform as a Service (PaaS)
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Aneka and Hour Glass Model

(many in one, one in many)

TCP/IP

Internet Architecture

Aneka Architecture

multiple networks

multiple applications

Multi
-
core

Cluster

Grid

Cloud

Thread

Task

...

MapReduce

2100

2100

2100

2100

2100

2100

2100

2100

Aneka

multiple applications

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Aneka as Cloud Platform

Datacenters

Clusters

Desktop PCs

Public Clouds

Platform

Infrastructure

Applications

& Services

Application Development

Cloud Middleware

Aneka

Scheduling
& Execution

Monitoring

Billing

SLA & QoS

User

Management

Application

Management

Dynamic

Provisioning

Bag of Tasks

Dist. Threads

MapReduce



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Aneka: components

public

DumbTask:
ITask


{





public void

Execute()


{


……


}

}

for
(
int

i=0; i<n; i++)

{





DumbTask

task =
new

DumbTask
();


app.SubmitExecution(task);

}

Executor

Scheduler

Executor

Executor

Executor

Client

Agent

work units

internet

internet

Aneka enterprise Cloud

Client

Agent

work units

Aneka User Agent

Aneka Worker

Service

Aneka Manager

Programming / Deployment Model

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Aneka & Virtual Resource Pools
Integration


XenServer Pool


Provisioning over
private Cloud
managed by Xen
Server


VMWare Pool


Provisioning over
private Cloud
managed by VMWare


Amazon EC2 Pool


Provisioning over
public Cloud provider:
Amazon EC2


Executors/Schedulers

Executor

Client Libraries

Executors

Scheduler

Public Cloud

publicly available resources

(physical and virtual)

Scheduler

internet

Private
Cloud

private enterprise

network

VPN

(virtual resources)

Executor

Aneka Case Studies

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(GoFront: A unit of China
Southern Railway Group)

Aneka utilizes idle desktops
(
30
) to decrease task time
from days to
hours

Time

(in hrs)

Single
Server

Aneka
Cloud

Raw Locomotive Design Files

(Using AutoDesk Maya)

Using Maya
Graphical Mode
Directly

Setup 1: Single Server

4 cores
server

Aneka Maya Renderer

Use private
Aneka Cloud

GoFront Private Aneka
Cloud

LAN network


(Running Maya Batch Mode on demand)

Setup 2: Aneka
Enterprise Cloud

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Application: Locomotive design CAD rendering

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A scalable architecture for TitanStrike on
-
line Gaming Portal

TitanStrike Private Aneka
Cloud

LAN network


(Running Game plugins on Demand)

Case 2: Aneka Enterprise Cloud =
Scalability

Aneka
-
based
GameController

The
local scheduler

interacts with
Aneka

and distributes the load
in the cloud.

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Distributed

log parsing

logs

logs

Case 1: Single Server =

Huge Overload

Single scheduler

controlling the
execution of all the
matches.

Game Servers

Gamers profiles

Players statistics

Team playing

Multiple games



Titan Strike On Line
Gaming Portal

Centralized

log parsing

logs

logs

Single
GameController

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Demo Time

Using Aneka in

Teaching and Research

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Teaching Tools


Theory:


Use an introductory parallel/distributed computing
book to teach concepts..


Labs/Hands on Experience


on Aneka Platform


Use Aneka to setup on enterprise cloud/grid using
Aneka


Teach parallel/distributed application programming


Give assignment to students to write
parallel/distributed programs using Aneka


Encourage students to conduct speedup analysis

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Large Projects / Research
Exploration


Encourage students to write applications


Parallel data mining


Parallel searching


Parallel sorting


Mass document searching


Image processing..


Science, Engineering, Medical applicatiions


Aneka can be use for:


Parallel programming models research


Scheduling algorithms research


SLA
-
oriented resource allocation research


Reconfigurable software systems research


…………….


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Development of Your Own Model


Extensibility


Implement your own model!


Defines the user abstractions


Provide an implementation for..


Execution Service (middleware)


Scheduling Service (middleware)


Application Manager (client component)


Where to start:


Aneka.Entity.*, Aneka.Data.Entity*;


Aneka.Execution.*;


Aneka.Runtime.Scheduling.*;

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Early Customers/Users


China Southern Railways, China


Engineering applications


Titan Gaming Inc, USA


Scalable gaming applications


C
-
DAC (Hyderabad), India


Research and Education Activities


Vel Tech University


A autonomous institute in Chennai, India


M S Ramaiah Institute of Technology


A autonomous institute in Bangalore, India


MAM College of Engineering


Affiliated with Anna University, Tirchi, India

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Manjrasoft’s Solutions
Procurement & Usage


Evaluation version available for Download:


Software:
www.manjrasoft.com


Manual: Setting up Clouds using your LAN
-
network computers


Teaching material


parallel and distributed computing and programming.


List of possible assignments for students.


Possible Projects for Final year students.


Full software can be procured from Manjrasoft


Comes with documentation (+some teaching material) + and
support with guidance on setting up a lab (charged at 1/3
rd

of
enterprise license price).


Applications


Other Departments (Physics, Chemistry, Biology, Finance,
Engineering) can use it for their applications.


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Outline


“Computer Utilities”


Vision and Promising IT Paradigms/Platforms


Cloud Computing and Related Paradigms


Trends, Definition, Cloud Benefits and Challenges


Market
-
Oriented Cloud Architecture


SLA
-
oriented
Resource Allocation


Global Cloud Exchange


Emerging Cloud Platforms


Cloudbus: Melbourne Cloud Computing Project


Summary and Thoughts for Future

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Summary…


Recent advances have made, Parallel and Distributed
Computing gain first class status in IT field.


All computers including laptops and desktops will have
multiple CPUs.


Parallel processing become a RULE (no more optional)


Network
-
based Parallel & Distributed Computing
technologies such as
Aneka

offer low
-
cost solution for
teaching and learning this field.

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Summary


Several Computing Paradigms have promised to
deliver “Computing Utilities” vision:


Cloud Computing is the most recent kid in the block
promising to turn vision into reality


Many exciting business and consumer applications
enabled.


Aneka is rapidly emerging as a platform for:


Building Clouds (especially “private”) and their apps.


Accelerating Applications and Managing their
execution.


Already used in engineering, life science, gaming,
and business intelligence applications.

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Convergence of Competing
Paradigms/Communities Needed


Web


Data Centres


Utility Computing


Service Computing


Grid Computing


P2P Computing


Cloud Computing


Market
-
Oriented
Computing





Ubiquitous


access


Reliability


Scalability


Autonomic


Dynamic


discovery


Composability


QoS


SLA




+

Paradigms

Attributes/Capabilities

-
Trillion $ business

-

Who will own it?

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References


Blueprint Paper!


R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg,
I. Brandic, “
Cloud Computing and Emerging
IT Platforms: Vision, Hype, and Reality for
Delivering Computing as the 5
th

Utility

,
Future Generation Computer Systems

(FGCS)
Journal, June 2009.


R. Buyya, S. Pandey, C. Vecchiola,
Cloudbus
Toolkit for Market
-
Oriented Cloud Computing
,
Proceedings of the 1st International
Conference on Cloud Computing (CloudCom
2009), Beijing, China, Dec. 1
-
4, 2009.


Aneka:


C. Vecchiola, X. Chu, and R. Buyya,
Aneka: A
Software Platform for .NET
-
based Cloud
Computing
,
High Speed and Large Scale
Scientific Computing
, 267
-
295pp, IOS Press,
Amsterdam, Netherlands, 2009.

Wiley Press, New York, USA,

March 2011

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Thanks for your attention!


Are there any


Questions?


Comments/Suggestions

We welcome you to:

Study/Research with Us | Do Business with us!


http:/www.cloudbus.org

|
www.Manjrasoft.com

rbuyya@unimelb.edu.au | raj@manjrasoft.com

Manjra
soft