1. A Case for Economy Grid Architecture for Service Oriented Grid Computing
Authors:
Rajkumar Buyya, David Abramson & Jonathan Giddy
Presenter: Diego Lopez
Agnostic: Djuradj Babic

2. Outline Introduction Grid Economy and Resource Management Issues
Economy Models and Related Work
GRACE
Resource Trading and Scheduling Experimentation
Conclusion and Future Work
June 12, 2006

3. Source: Buyya, R http://www.buyya.com
1. Introduction
“ We expect that an economy driven approach to resource management and scheduling will make a great impact on the eventual success and widespread adoption of the Grid in day-to-day computational activities.”
Researcher Giddy
Dr. Buyya
Prof. Abramson
June 12, 2006
Source: Buyya, R

4. 1. Introduction Grid environment is complex ($$$)
Different access cost models
Dynamically varying loads and availability conditions
Use of economic models in the Grid to encourage participation and wide-scale adoption
Proposal of computational economy framework that leverage existing Grid sites
June 12, 2006

5. View of Economic Grid June 12, 2006
Source: Buyya, R A Case for Economy Grid Architecture for Service Oriented Grid Computing (Pg. 2)

6. 2. Grid Economy and Resource Management Issues
Establish policies that promote Grid resource sharing
2 key players in Grid economy
Resource providers (GSP)
Resource consumers (GRB)
Consumers interact with brokers to express their budget and deadline requirements from the Grid
June 12, 2006

7. Proposal of GRACE Grid Architecture for Computational Economy
Leverage of existing infrastructures:
Globus/Legion
Condor/G
Provide an infrastructure that allows for:
Info/Market directory for publicizing entities
Model for determining value of resources
Resource pricing schemes
Accounting, Billing and Payment mechanisms
June 12, 2006

8. 3. Economy Models and Related Work
Possible economic models for resource trading and pricing strategies
Commodity Market
Posted Price
Bargaining
Tendering/Contract-Net
Auction
Bid-based Proportional Resource Sharing
Community/Coalition/Bartering
June 12, 2006

9. Examples of Computational Economy Systems
SYS_NAME
ECO_MODEL
PLATFORM
Mariposa (’96)
Bidding
Dist.Dbase
Mungi (’02)
Commodity
Storage servers
Popcorn (’98)
Auction
Web browser
Mojo Nation
Credit-based or bartering
Network storage
Java Market
QoS based computational
Web browser (applets)
June 12, 2006

10. 4. GRACE Use of well-adopted Grid technologies, Globus/Condor
Development of middleware services for resource trading using different economic models
Development of advanced user-centric Grid resource brokers
June 12, 2006

11. 4.1 Grid Resource Broker (GRB)
Mediator between user and grid resources
*Nimrod – parametric modeling language
Use of Nimrod/G broker (superscheduler)
Job Control Agent
Schedule Advisor
Grid Explorer
Trade Manager
Deployment Agent
June 12, 2006

12. Globus, Legion, Condor, etc. Grid Information Server(s)
Nimrod/G Broker
RM: Local Resource Manager, TS: Trade Server
Nimrod/G Client
Nimrod/G Client
Nimrod/G Client
Nimrod/G Engine
Schedule Advisor
Grid
Store
Trading Manager
Grid Dispatcher
Grid Explorer
Grid Middleware
Globus, Legion, Condor, etc.
TM TS
GE GIS
Grid Information Server(s)
RM & TS
RM & TS
RM & TS G C L G
Condor enabled node.
Legion enabled node.
Globus enabled node. L
June 12, 2006
Src:

13. 4.2 Economy Grid Middleware in Globus Context
Trade Server (TS) – maximize the resource utility and profit for its owner
Pricing Policies – define prices for resources based on economic models previously mentioned
Resource Accounting and charging – tracking resource usage for billing and auditing purposes
June 12, 2006

14. 4.3 Grid Open Trading Protocols and Deal Template
Establish rules and format for exchanging commands between a GRACE client (Trade Manager) and Trade Server
Deal Template (DT) contains
CPU time
Storage requirements
Initial offer
This trading overhead can be reduced if prices are announced via GIS
June 12, 2006

15. 4.4 Pricing, Accounting, and Payment Mechanisms
N-ways to determine resource pricing
Fixed price model (no QoS like today’s www)
Usage timing (peak, off-peak)
Bulk purchase
Demand and supply
Loyalty of customers (i.e. frequent flyer miles)
Calendar based
June 12, 2006

16. 4.4 Pricing, Accounting, and Payment Mechanisms
Service items to be charged
CPU time
Memory
Storage used
Software and Libraries accessed (ASP)
Access to these services can be charged
Individually
Combination (costing matrix)
June 12, 2006

17. 4.4 Pricing, Accounting, and Payment Mechanisms
Prepaid – purchase credits from GSP or Grid Bank
Use and pay later (like electricity)
Pay as you go (wireless calling cards)
Grants based
*Billing services handled by 3rd party:
NetCheque
Paypal
June 12, 2006
*not incorporated into GRACE described in this paper

18. 4.5 System Prototype & Demo Experiences
Prototype of the Nimrod/G resource brokering demo held during HPDC 2000
Parameter study experiment performed over Grid resources located in both Australia and the US
Ability to change deadline and budget to trade-off cost vs. timeframe to illustrate Grid marketplace dynamics
June 12, 2006

19. 5. Resource Trading and Scheduling Experimentation
Experiment to test operation of Grid Trade Server across 5 systems (165 jobs)
Use of Posted Price Market Model for the Nimrod/G brokering
Runs during peak time vs. off-peak time
Access price expressed in Grid units per CPU second (G$)
Resource/service price provided by GRACE framework
June 12, 2006

20. Economy Grid Results Cost-Optimization algorithm successfully
Minimized artificial access cost per resource
Completed within one-hour deadline
Initial calibration phase ensures completion within budget/time constraints
Scheduler excluded usage of resources during peak time
Scheduler predictions met deadline using least-expensive resources available
June 12, 2006

21. 6. Conclusion and Future Work
GRACE leverages existing middleware systems (Condor/Legion/Globus)
Nimrod/G can discover best resource providers based on user’s requirements
Nimrod/G does not support dynamic prices once initial scheduling is proposed
Nimrod/G Portal available …
June 12, 2006