1. Utility-Oriented Cloud & Grid Computing: A Vision, Hype, and Reality
Dr. Rajkumar Buyya
Grid Computing and Distributed Systems (GRIDS) Lab Dept. of Computer Science and Software Engineering The University of Melbourne, Australia
Gridbus Sponsors

2. The GRIDS Lab @ Melbourne
R & D
Education
Youngest and one of the rapidly growing research labs in our School/University:
Founded in 2002
Houses 20+ researchers consisting of:
Research Fellows/PostDocs
Software Engineers
PhD candidates
Honours/Masters students
Funding
National and International organizations
Australian Research Council & DEST
Many industries (Sun, StorageTek, Microsoft, IBM, Microsoft)
University-wide collaboration:
Faculties of Science, Engineering, and Medicine
Many national and international collaborations.
Academics
Industries
Software:
Widely in academic and industrial users.
Publication:
My research team produces over 20% of our Dept’s research output.
+ Community Services: e.g., IEEE TC for Scalable Computing

3. Agenda Introduction Global Grids and Challenges
Utility Networks and Grid Computing
Application Drivers and Various Types of Grid Services
Global Grids and Challenges
Security, resource management, pricing models, …
Service-Oriented Grid Architecture and Gridbus Solutions
Market-based Management, GMD, Grid Bank, Aneka
Grid Service Broker
Architecture, Design and Implementation
Performance Evaluation: Experiments in Creation and Deployment of Applications on Global Grids
A Case Study in High Energy Physics
Summary and Conclusion

4. “Computer Utilities” Vision: Implications of the Internet
1969 – Leonard Kleinrock, ARPANET project
“As of now, computer networks are still in their infancy, but as they grow up and become sophisticated, we will probably see the spread of ‘computer utilities’, which, like present electric and telephone utilities, will service individual homes and offices across the country”
Computers Redefined
1984 – John Gage, Sun Microsystems
“The network is the computer”
2008 – David Patterson, U. C. Berkeley
“The data center is the computer. There are dramatic differences between of developing software for millions to use as a service versus distributing software for millions to run their PCs”
2008 – “Cloud is the computer” – Buyya!

5. Attributes/Capabilities
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 access
-Reliability
Scalability
Autonomic
Dynamic discovery
Composability
-QoS
-SLA
- …
Trillion $ business
Who will own it?
Paradigms
Attributes/Capabilities

6. * Since Grids have been around for sometime (early 2000), do we have a unified vision of what Grids can do? * And did we make sufficient advances to turn vision of “computer utilities” into a reality?
- Let us take a look at views of
“industrial” practitioners & “academics”

7. “Industrial” vision of Grid computing
IBM
On Demand Computing
Microsoft
.NET
Oracle
10g
Sun
N1 – Sun Grid Engine HP
Adaptive Enterprise
Amazon
Elastic Compute Cloud Services
Manjrasoft
Aneka for building enterprise Grids and Clouds.

8. Most academics view: Cyberinfrastructure for conducting collaborative (e-)Science

9. How do Grids look like? A Bird Eye View of a Global Grid
Grid Information Service
Grid Resource Broker
database
Application R2 R3 R4 R5 RN
Grid Resource Broker R6 R1
Resource Broker
Grid Information Service

10. How do Grids look like? A Bird Eye View of a Global Grid
Grid Information Service
Grid Resource Broker
database
Application R2 R3 R4 R5 RN
Grid Resource Broker R6 R1
Resource Broker
Grid Information Service

11. E-Business E-Science How Are Grids Used? Utility computing
High-performance computing
Collaborative design
Financial modeling
Collaborative data-sharing
High-energy physics
E-Business
Life sciences
Drug discovery
Data center automation
E-Science
Natural language processing
Business Intelligence
(Data Mining)

12. Agenda Introduction Global Grids and Challenges
Utility Networks and Grid Computing
Application Drivers and Various Types of Grid Services
Global Grids and Challenges
Security, resource management, pricing models, …
Service-Oriented Grid Architecture and Gridbus Solutions
Market-based Management, GMD, Grid Bank, Aneka
Grid Service Broker
Architecture, Design and Implementation
Performance Evaluation: Experiments in Creation and Deployment of Applications on Global Grids
A Case Study in High Energy Physics
Summary and Conclusion

13. Grid Challenges Application Construction Security
Uniform Access
Security
System Management
Computational Economy
Resource Discovery
Resource Allocation
& Scheduling
Data locality
Network Management
Application Construction

14. Some Grid Initiatives Worldwide
Australia
Nimrod-G
Gridbus
DISCWorld
GrangeNet.
APACGrid
ARC eResearch
Brazil
OurGrid, EasyGrid
LNCC-Grid + many others
China
ChinaGrid – Education
CNGrid - application
Europe
UK eScience
EU Grids..
and many more...
India
Garuda
Japan
NAREGI
Korea...
N*Grid
Singapore
NGP
USA
Globus
TeraGrid
Cyberinfrasture
AutoMate
and many more...
Industry Initiatives
IBM On Demand Computing
HP Adaptive Computing
Sun N1
Microsoft - .NET
Oracle 10g
Amzon – Elastic Compute Cloud
Infosys – Enterprise Grid
Satyam – Business Grid
Manjrasoft – enterprise Clouds and Grids
and many more
Public Forums
Open Grid Forum
Conferences:
CCGrid
Grid
HPDC
E-Science
1.3 billion – 3 yrs
27 million
2? billion
120million – 5 yrs
450million – 5 yrs
486million – 5 yrs
1.3 billion (Rs)
1 billion – 5 yrs

15. Open-Source Grid Middleware Projects
OurGrid
Slide by Hiro

16. Driving Theme: Community vs. Utility Grids
Type
Feature
Community Grids
Utility Grids (Now Clouds)
User QoS
Best effort
Contract/SLA
Service Pricing
Not considered /
free access
Usage, QoS level, Market supply and demand
Example Middleware
Globus, Condor, OMII, Unicore
Nimrod-G, Gridbus, & many inspired efforts (IBM Business Grid, Sun Grid Market)
.. Amazon EC2..
“So what’s difference between scheduling tasks on utility grids and community grids.” This table shows some differences in terms of availability, Quality of Service and pricing. In utility Grids, users can make a reservation with a service in advance to ensure the service availability. For the Quality of Service, users can negotiate with service providers on service level agreements for their required quality of service.
“Compared with utility services.” Services availability and quality of service in community grids may not be guaranteed. Community grids provide free access but in utility grids users need to pay for service access. In general, service pricing is based on the quality of service. Users don’t always prefer to use services with higher qos, since it charges more. So in a utility grid scheduler needs to allocate services to tasks based on users’ QoS requirements. For example, if a user want to process a task, there are multiple choices, one service can complete task in 4 min, the price is 100, second service can complete in 10 mins but only charge 20, a task also can be completed in just one min, but service provider charge 200. If user don’t need results immediately, see the deadline is 15 mins. Then the second service is best, it can complete task execution within the deadline but the cost is minimum.

17. Pushes Grid computing into mainstream computing
The Gridbus Melbourne: Enable Leasing of ICT Services on Demand
WWG
Gridbus
Pushes Grid computing into mainstream computing

19. Agenda Introduction Global Grids and Challenges
Utility Networks and Grid Computing
Application Drivers and Various Types of Grid Services
Global Grids and Challenges
Security, resource management, pricing models, …
Service-Oriented Grid Architecture and Gridbus Solutions
Market-based Management, GMD, Grid Bank, Aneka
Grid Service Broker
Architecture, Design and Implementation
Performance Evaluation: Experiments in Creation and Deployment of Applications on Global Grids
A Case Study in High Energy Physics
Summary and Conclusion

20. What do Grid players want & require?
Grid Service Consumers (GSCs): - minimize expenses, meet QoS
How do I express QoS requirements ?
How do I trade between timeframe & cost ?
How do I discover services and map jobs to meet my QoS needs?
How do I manage Grid dynamics and get my work done? …
Grid Service Providers (GSPs):– maximise ROI
How do I decide service pricing models ?
How do I specify them ?
How do I translate them into resource allocations ?
How do I enforce them ?
How do I advertise & attract consumers ?
How do I do accounting and handle payments?
They need mechanisms, tools and technologies that help them in value expression, value translation, and value enforcement.

21. Service-Oriented Grid Architecture
Data Catalogue
Grid Bank
Grid Market Services
Information Service
Sign-on
Health Monitor
Info ?
Grid Explorer
Grid Node N … …
Programming Environments
Secure
Job Control
Agent
Applications
Schedule Advisor
Grid Node1
QoS
Pricing Algorithms
Trade Server
Trading
Trade Manager
Accounting
Resource
Reservation
Misc. services …
Deployment Agent
JobExec
Resource Allocation
Grid Resource Broker
Storage R1 R2 … Rm
Core Middleware
Services
Grid Service Consumer
Grid Service Providers

22. Market-Oriented Grid Software: A union of Gridbus and other technologies…
Grid
Applications
Science
Commerce
Engineering
Collaboratories
Grid Portals …
APIs/Tools:
MPI
ExcellGrid
Workflow APIs
Task, Parametric, and Components Programming
User-Level Middleware
Grid Workflow Engine
Grid Scheduling:
Gridbus Resource Broker
Globus
Unicore …
Grid Storage Economy
Grid Exchange & Federation
Grid Market Directory
Core Grid
Middleware
Aneka Cloud (WS-based access + SLA
Grid Bank
NorduGrid
XGrid
Grid Economy
JVM
Condor
PBS
SGE
Libra
Tomcat
Grid
Fabric
Software
.NET
Windows
Solaris
Linux
AIX
IRIX
OSF1
Mac
CDB
Grid
Fabric
Hardware
PDB
Worldwide Grid

23. On Demand Assembly of Services in Market-Oriented Grid Environments
Visual Application Composer
Application Code
Explore data 1
Results+ Cost Info 10
Grid Resource Broker 2
Bill 12
Data Catalogue 5
ASP Catalogue
Grid Info Service
Grid Market Directory 4 6 3
Job 8
Results 9 7
GSP
(e.g., Microsoft) PE
(e.g., Amazon)
(e.g., IBM)
CPU
or PE
Grid Service (GS)
(Globus)
Aneka
EC2
GTS
Resource Allocation
GSP
(Accounting Service)
Gridbus GridBank 11

24. On Demand Assembly of Services in Market-Oriented Grid Environments

25. Enterprise/Private Clouds Public/Internet Clouds
Cloud Services
Infrastructure as a Services (IaaS)
CPU, Storage: Amazon.com et. al
Platform as a Services (PaaS)
Google App Engine, Microsoft Azure, and Manjrasoft Aneka
Software as a Service (SaaS)
SalesForce.Com
Enterprise/Private Clouds
Clouds
Public/Internet Clouds

26. Layered view of services within a Cloud stack
Software as a Service (SaaS)
e.g., ..SalesForce.com
Platform as a Service (PaaS)
e.g., ..Aneka
Infrastructure as a Service (IaaS)
e.g., Amazon, Nirvanix

27. Aneka
A Software Platform for Building and Managing “Enterprise” Grids and Clouds

28. Aneka: A 3rd Gen enterprise Grid Technology  Cloud model
Generation
Properties
Technologies
First
(-2001)
Application-specific platforms
distributed.net,
Second
( )
Single programming model, rigid architecture, no QoS
UD, XtremeWeb, Alchemi, Digipede, DataSynapse, BOINC
Third
( ?)
SOA, extensible architecture, multiple programming models, multi-tenancy, enterprise QoS, SLAs, market-based resource allocation
Aneka

29. ANEKA – Product Overview (Alpha)
.NET based service-oriented platform for grid / cloud computing
Development and Run Time Environment
Includes Development and Management Tools
Suitable for
Development of Enterprise Grid / Cloud Applications
Grid / Cloud enabling legacy applications
Ideal for Corporate Developers, Software, SaaS, Hosting Vendors and Application / System Integrators
ANEKA Product Architecture

30. Aneka Deployment Models
Enterprise/Private
Harness LAN connected resources
Application Development, Testing, Execution
Teaching and Learning
Sensitive applications
Public
Hosted by a 3rd party service provider owning a large Data Center (1000s of servers)
Offers subscription-based services to their shared infrastructure on “pay-as go” model.to many users from different organisations.
Amazon.com, Microsoft Azure
Aneka SDK + Execution Manger
Enterprise/Private Clouds
Aneka
Public Clouds

31. Programming / Deployment Model
FIRST PRODUCT
Aneka: components
public DumbTask: ITask { …
public void Execute() …… }
Programming / Deployment Model
Aneka enterprise Cloud
for(int i=0; i<n; i++) { …
DumbTask task = new DumbTask();
app.SubmitExecution(task); }
Executor
Executor
work units
Client Agent
Executor
Scheduler
internet
work units
Executor
Aneka Worker
Service
Aneka Manager
Client Agent
internet
Aneka Users

32. How does it solve the problem?
Divide the problem in to multiple small tasks and distribute them run in parallel on multiple computers within a Cloud.
An Illustratioin
Executor
Application
Manager
Manager / Executor
GThreads/Tasks

33. User scenario: GoFront (unit of China Southern Railway Group)
Application: Locomotive design CAD rendering
Raw Locomotive Design Files
(Using AutoDesk Maya)
Using Maya Graphical Mode Directly
Case 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)
Case 2: Aneka Enterprise Cloud
Aneka utilizes idle desktops (30) to decrease task time from days to hours
Time (in hrs)
Single Server
Aneka Cloud

34. Aneka: How can get it? Available to Download: Teaching material
Software:
Manual: Setting up Cloud 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..
Price – highly affordable 
= Fee you charge to 1 student (each year) and all students/teachers in entire college/university can use it!
Applications
Other Departments (Physics, Chemistry, Biology, Finance, Engineering) can use it for their applications.

35. On Demand Assembly of Services in Market-Oriented Grid Environments

36. Agenda Introduction Global Grids and Challenges
Utility Networks and Grid Computing
Application Drivers and Various Types of Grid Services
Global Grids and Challenges
Security, resource management, pricing models, …
Service-Oriented Grid Architecture and Gridbus Solutions
Market-based Management, GMD, Grid Bank, Aneka
Grid Service Broker
Architecture, Design and Implementation
Performance Evaluation: Experiments in Creation and Deployment of Applications on Global Grids
A Case Study in High Energy Physics
Summary and Conclusion

37. Grid Service Broker (GSB)
A resource broker for scheduling task farming data Grid applications with static or dynamic parameter sweeps on global Grids.
It uses computational economy paradigm for optimal selection of computational and data services depending on their quality, cost, and availability, and users’ QoS requirements (deadline, budget, & T/C optimisation)
Key Features
A single window to manage & control experiment
Programmable Task Farming Engine
Resource Discovery and Resource Trading
Optimal Data Source Discovery
Scheduling & Predications
Generic Dispatcher & Grid Agents
Transportation of data & sharing of results
Accounting

38. App, T, $, Optimization Preference
workload
Gridbus User Console/Portal/Application Interface
App, T, $, Optimization Preference
Gridbus Broker
Gridbus Farming Engine
Schedule Advisor
Record Keeper
Trading Manager
Grid Dispatcher
Grid Explorer
TM TS $
GE GIS, NWS
Core Middleware
Grid Info Server
RM & TS G $
Data
Catalog
Data
Node C $ U G
Globus enabled node. L A
Amazon EC2/S3 Cloud.

39. Gridbus Broker: Separating “applications” from “different” remote service access enablers and schedulers
Application Development Interface
Home Node/Portal
Gridbus
Broker
fork()
batch()
-PBS
-Condor
-SGE
-Aneka
-XGrid
Single-sign on security
Alogorithm1
Scheduling Interfaces
AlogorithmN
Data Catalog
Plugin Actuators
Aneka
-PBS
-Condor
-SGE
Globus
Job manager
fork()
batch()
Gridbus
agent
Data Store
AMI
Amazon EC2
-PBS
-Condor
-SGE
-XGrid
SSH
fork()
batch()
Gridbus
agent
Access Technology
SRB
Grid FTP

40. Gridbus Services for eScience applications
Application Development Environment:
XML-based language for composition of task farming (legacy) applications as parameter sweep applications.
Task Farming APIs for new applications.
Web APIs (e.g., Portlets) for Grid portal development.
Threads-based Programming Interface
Workflow interface and Gridbus-enabled workflow engine.
… Grid Superscalar – in cooperation with BSC/UPC
Resource Allocation and Scheduling
Dynamic discovery of optional computational and data nodes that meet user QoS requirements.
Hide Low-Level Grid Middleware interfaces
Globus (v2, v4), SRB, Aneka, Unicore, and ssh-based access to local/remote resources managed by XGrid, PBS, Condor, SGE.

41. Click Here for Demo
Drug Design
Made Easy!

42. s

43. Agenda Introduction Global Grids and Challenges
Utility Networks and Grid Computing
Application Drivers and Various Types of Grid Services
Global Grids and Challenges
Security, resource management, pricing models, …
Service-Oriented Grid Architecture and Gridbus Solutions
Market-based Management, GMD, Grid Bank, Aneka
Grid Service Broker
Architecture, Design and Implementation
Performance Evaluation: Experiments in Creation and Deployment of Applications on Global Grids
A Case Study in High Energy Physics
Summary and Conclusion

44. Case Study: High Energy Physics and Data Grid
The Belle Experiment
KEK B-Factory, Japan
Investigating fundamental violation of symmetry in nature (Charge Parity) which may help explain “why do we have more antimatter in the universe OR imbalance of matter and antimatter in the universe?”.
Collaboration 1000 people, 50 institutes
100’s TB data currently
High Energy Physics (HEP) is the study of the fundamental constituents of matter and the forces between these constituents. It is called High Energy Physics as using high energies enables us to probe smaller distances and structures within matter, and also allows us to study matter as it was in the early universe, the history of matter. It is also called Particle Physics as we deal with quanta of matter and forces and the properties associated with these.
The study of HEP is broken into two main disciplines, theoretical and experimental. Theoretical HEP propose theories and models to describe matter, forces, their properties, actions, and interactions. Experimental HEP construct experiments or detectors and accelerators to investigate matter interactions and behaviour under high energy conditions.
Experimental HEP can be roughly broken into 3 separate activities. The boundaries of these activities, in time and responsibility, are often indistinct. The activities are the construction of detectors which typically takes many years, the measurement or collection of data, and the analysis of this data. We will focus on the using data grids for the analysis of data within HEP.

45. Case Study: Event Simulation and Analysis
B0->D*+D*-Ks
Simulation and Analysis Package - Belle Analysis Software Framework (BASF)
Experiment in 2 parts – Generation of Simulated Data and Analysis of the distributed data
Analyzed 100 data files (30MB each) that were distributed among the five nodes within Australian Belle DataGrid platform.

46. Australian Belle Data Grid Testbed
VPAC Melbourne

47. Belle Data Grid (GSP CPU Service Price: G$/sec)NA
G$4
G$6
VPAC Melbourne
G$2
Data node

48. Belle Data Grid (Bandwidth Price: G$/MB)32 33 36
G$4 31 30 34 NA 38 31
G$4
G$6
VPAC Melbourne
G$2
Data node

49. Deploying Application Scenario
A data grid scenario with 100 jobs and each accessing remote data of ~30MB
Deadline: 3hrs.
Budget: G$ 60K
Scheduling Optimisation Scenario:
Minimise Time
Minimise Cost
Results:

50. Time Minimization in Data Grids10 20 30 40 50 60 70 80 1 2 3 4 5 6 7 8 9 11 12 13 14 15 16 17 18 19 21 22 23 24 25 26 27 28 29 31 32 33 34 35 36 37 38 39 41 42
Time (in mins.)
Number of jobs completed
fleagle.ph.unimelb.edu.au
belle.anu.edu.au
belle.physics.usyd.edu.au
brecca-2.vpac.org

51. Results : Cost Minimization in Data Grids10 20 30 40 50 60 70 80 90
100 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63
Time(in mins.)
Number of jobs completed
fleagle.ph.unimelb.edu.au
belle.anu.edu.au
belle.physics.usyd.edu.au
brecca-2.vpac.org

52. Observation Organization Node details Cost (in G$/CPU-sec)
Node details
Cost (in G$/CPU-sec)
Total Jobs Executed
Time
Cost
CS,UniMelb
belle.cs.mu.oz.au 4 CPU, 2GB RAM, 40 GB HD, Linux
N.A. (Not used as a compute resource) --
Physics, UniMelb
fleagle.ph.unimelb.edu.au 1 CPU, 512 MB RAM, 40 GB HD, Linux 2 3 94
CS, University of Adelaide
belle.cs.adelaide.edu.au
4 CPU (only 1 available) , 2GB RAM, 40 GB HD, Linux
ANU, Canberra
belle.anu.edu.au 4 CPU, 2GB RAM, 40 GB HD, Linux 4
Dept of Physics, USyd
belle.physics.usyd.edu.au 4 CPU (only 1 available), 2GB RAM, 40 GB HD, Linux 72
VPAC, Melbourne
brecca-2.vpac.org
180 node cluster (only head node used), Linux 6 23

53. Agenda Introduction Global Grids and Challenges
Utility Networks and Grid Computing
Application Drivers and Various Types of Grid Services
Global Grids and Challenges
Security, resource management, pricing models, …
Service-Oriented Grid Architecture and Gridbus Solutions
Market-based Management, GMD, Grid Bank, Aneka
Grid Service Broker
Architecture, Design and Implementation
Performance Evaluation: Experiments in Creation and Deployment of Applications on Global Grids
A Case Study in High Energy Physics
Summary and Conclusion

54. Summary and Conclusion
Grids exploit synergies that result from cooperation of autonomous entities:
Resource sharing, dynamic provisioning, and aggregation at global level Great Science and Great Business!
Grids have emerged as enabler for Cyberinfrastructure that powers e-Science and e-Business applications.
SOA + Market-based Grid Management = Utility Grids
Grids allow users to dynamically lease Grid services at runtime based on their quality, cost, availability, and users QoS requirements.
Delivering ICT services as computing utilities.
Clouds are rapidly emerging, but more work is required
Federation of Clouds, Cloud Exchange, and Application Scaling

55. 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 …
Trillion $ business
Who will own it?
Paradigms
Attributes/Capabilities

56. Thanks for your attention!
Are there any
Questions?
Comments/ Suggestions
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