Economic Models for Management of Resources in Peer-to-Peer (P2P) and Grid Computing

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Economic Models for Management of Resources in
Peer-to-Peer (P2P) and Grid Computing

• R. Buyya, H.Stockinger, J.Giddy, D.Abramson

Melbourne, Australia
Switzerlandbuyya.com/ecogrid
ITCOM 2001, Denver, Aug 19-24, 2001
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Agenda

• A quick glance at todays Grid computing
• Resource Management challenges for next
  generation Grid computing
• A Glance at Approaches to Grid computing
• Grid Architecture for Computational Economy
• Economic Models for Resource Management
• Nimrod/G -- Grid Resource Broker
• Deadline and Budget Constrained (DBC) Scheduling
  Experiments on World Wide Grid testbed
• Conclusions

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Scalable HPC Breaking Administrative Barriers
new challenges
?
PERFORMANCE
Administrative Barriers

• Individual
• Group
• Department
• Campus
• State
• National
• Globe
• Inter Planet
• Universe

Desktop
SMPs or SuperComputers
Local Cluster
Global Cluster/Grid
Inter Planetary Grid!
Enterprise Cluster/Grid
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Why Grids? Large Scale Explorations need
themKiller Applications.

• Solving grand challenge applications using
  modeling, simulation and analysis

Aerospace
Internet Ecommerce
Life Sciences
CAD/CAM
Digital Biology
Military Applications
Military Applications
Military Applications
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What is Grid ?

• An infrastructure that logically couples
  distributed resources
• Computers PCs, workstations, clusters,
  supercomputers, laptops, notebooks,
• mobile devices, PDA, etc
• Software e.g., ASPs renting expensive special
  purpose applications on demand
• Catalogued data and databases e.g. transparent
  access to human genome database
• Special devices e.g., radio telescope
  SETI_at_Home searching for life in galaxy.
• People/collaborators.
• and presents them as an integrated global
  resource.
• It enables the creation of virtual enterprises
  (VEs) for resource sharing.

Widearea
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P2P/Grid Applications-Drivers

• Distributed HPC (Supercomputing)
• Computational science.
• High-Capacity/Throughput Computing
• Large scale simulation/chip design parameter
  studies.
• Content Sharing (free or paid)
• Sharing digital contents among peers (e.g.,
  Napster)
• Remote software access/renting services
• Application service provides (ASPs) Web
  services.
• Data-intensive computing
• Virtual Drug Design, Particle Physics, Stock
  Prediction...
• On-demand, realtime computing
• Medical instrumentation Mission Critical.
• Collaborative Computing
• Collaborative design, Data exploration,
  education.
• Service Oriented Computing (SOC)
• Computing as Utility New paradigm and new
  industries.

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Building and Using Grids require

• Services that make our systems Grid Ready!
• Security mechanisms that permit resources to be
  accessed only by authorized users.
• (New) programming tools that make our
  applications Grid Ready!.
• Tools that can translate the requirements of an
  application/user into the requirements of
  computers, networks, and storage.
• Tools that perform resource discovery, trading,
  selection/allocation, scheduling and distribution
  of jobs and collects results.

Globus
?
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Players in Grid Computing
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What users want ?Users in Grid Economy Strategy

• Grid Consumers
• Execute jobs for solving varying problem size and
  complexity
• Benefit by selecting and aggregating resources
  wisely
• Tradeoff timeframe and cost
• Strategy minimise expenses
• Grid Providers
• Contribute (idle) resources for executing
  consumer jobs
• Benefit by maximizing resource utilisation
• Tradeoff local requirements and market
  opportunity
• Strategy maximise return on investment

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Agenda

• A quick glance at todays Grid computing
• Resource Management challenges for next
  generation Grid computing
• A Glance at Approaches to Grid computing
• Grid Architecture for Computational Economy
• Economic Models for Resource Management
• Nimrod-G -- Grid Resource Broker
• Deadline and Budget Constrained (DBC) Scheduling
  Experiments on World Wide Grid testbed
• Conclusions

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Sources of Complexity in Resource Management for
World Wide Grid Computing

• Size (large number of nodes, providers,
  consumers)
• Heterogeneity of resources (PCs, Workstations,
  clusters, and supercomputers, instruments,
  databases, software)
• Heterogeneity of fabric management systems
  (single system image OS, queuing systems, etc.)
• Heterogeneity of fabric management polices
• Heterogeneity of application requirements (CPU,
  I/O, memory, and/or network intensive)
• Heterogeneity in resource demand patterns (peak,
  off-peak, ...)
• Applications need different QoS at different
  times (time critical results). The utility of
  experimental results varies from time to time.
• Geographical distribution of users located
  different time zones
• Differing goals (producers and consumers have
  different objectives and strategies)
• Unsecure and Unreliable environment

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Traditional approaches to resource management
scheduling are NOT useful for Grid ?

• They use centralised policy that need
• complete state-information and
• common fabric management policy or decentralised
  consensus-based policy.
• Due to too many heterogenous parameters in the
  Grid it is impossible to define/get
• system-wide performance matrix and
• common fabric management policy that is
  acceptable to all.
• Economics paradigm proved to effective
  institution in managing decentralization and
  heterogeneity that is present in human economies!
  
• Fall of USSR Emergence of US as world
  superpower! (monopoly?)
• So, we propose/advocate the use of computational
  economics principles in management of resources
  and scheduling computations on world wide Grid.
• Think locally and act globally approach to grid
  computing!

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Benefits of Computational Economies

• It provides a nice paradigm for managing self
  interested and self-regulating entities (resource
  owners and consumers)
• Helps in regulating supply-and-demand for
  resources.
• Services can be priced in such a way that
  equilibrium is maintained.
• User-centric / Utility driven Value for money!
• Scalable
• No need of central coordinator (during
  negotiation)
• Resources(sellers) and also Users(buyers) can
  make their own decisions and try to maximize
  utility and profit.
• Adaptable
• It helps in offering different QoS (quality of
  services) to different applications depending the
  value users place on them.
• It improves the utilisation of resources
• It offers incentive for resource owners for being
  part of the grid!
• It offers incentive for resource consumers for
  being good citizens
• There is large body of proven Economic principles
  and techniques available, we can easily leverage
  it.

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New challenges of Computational Economy

• Resource Owners
• How do I decide service prices ? (economic
  models?)
• How do I specify them ?
• How do I enforce them ?
• How do I advertise attract consumers ?
• How do I do accounting and handle payments?
• ..
• Resource Consumers
• How do I decide expenses ?
• How do I express QoS requirements ?
• How I trade between timeframe cost ?
• .
• Any tools, traders brokers available to
  automate the process ?

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Agenda

• A quick glance at todays Grid computing
• Resource Management challenges for next
  generation Grid computing
• A Glance at Approaches to Grid computing
• Grid Architecture for Computational Economy
• Economic Models for Resource Management
• Nimrod-G -- Grid Resource Broker
• Deadline and Budget Constrained (DBC) Scheduling
  Experiments on World Wide Grid testbed
• Conclusions

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mix-and-match
Object-oriented
Internet/partial-P2P
Grid Computing Approaches
Network enabled Solvers
Market/Computational Economy
Nimrod-G
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Many Testbeds ? who pays ?, who regulates
supply and demand ?
GUSTO (decommissioned)
World Wide Grid
Legion Testbed
NASA IPG
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Agenda

• A quick glance at todays Grid computing
• Resource Management challenges for next
  generation Grid computing
• A Glance at Approaches to Grid computing
• Grid Architecture for Computational Economy
• Economic Models for Resource Management
• Nimrod-G -- Grid Resource Broker
• Deadline and Budget Constrained (DBC) Scheduling
  Experiments on World Wide Grid testbed
• Conclusions

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Building an Economy Grid(Next Generation Grid
Computing!)
To enable the creation and promotion of Grid
Marketplace (competitive) ASP Service Oriented
Computing . . . And let users focus on their own
work (science, engineering, or commerce)!
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GRACE A ReferenceGrid Architecture for
Computational Economy
Grid Bank
Information Server(s)
Grid Market Services
Sign-on
Health Monitor
Info ?
Grid Node N

Grid Explorer

Application
Secure
Job Control Agent
Grid Node1
Schedule Advisor
QoS
Pricing Algorithms
Trade Server
Trading
Trade Manager
Accounting
Resource Reservation
Misc. services

Deployment Agent
JobExec
Resource Allocation
Storage
Grid User
Grid Resource Broker

R1
R2
Rm
Grid Middleware Services
Grid Service Providers
See PDPTA 2000 paper!
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Grid Components
Applications and Portals
Grid Apps.

Prob. Solving Env.
Collaboration
Engineering
Web enabled Apps
Scientific
Grid Tools
Development Environments and Tools

Web tools
Languages
Libraries
Debuggers
Resource Brokers
Monitoring
Grid Middleware
Distributed Resources Coupling Services

QoS
Security
Information
Process
Resource Trading
Market Info
Local Resource Managers
TCP/IP UDP

Operating Systems
Queuing Systems
Libraries App Kernels
Grid Fabric
Networked Resources across Organisations

Clusters
Data Sources
Scientific Instruments
Storage Systems
Computers
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Economy Grid Globus GRACE
Applications
Grid Apps.

Science
Engineering
Commerce
Portals
ActiveSheet
High-level Services and Tools
Grid Status

Grid Tools
DUROC
globusrun
MPI-G
Nimrod/G
CC
Core Services
Heartbeat Monitor
Nexus
GRACE-TS
GRAM
Grid Middleware
Grid Security Interface
GASS
DUROC
MDS
GARA
GBank
GMD
Grid Fabric
Local Services
GRD
QBank
JVM
Condor
TCP
UDP
eCash
LSF
PBS
Solaris
Irix
Linux
See IPDPS HWC 2001 paper!
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Agenda

• A quick glance at todays Grid computing
• Resource Management challenges for next
  generation Grid computing
• A Glance at Approaches to Grid computing
• Grid Architecture for Computational Economy
• Economic Models for Resource Management
• Nimrod-G -- Grid Resource Broker
• Deadline and Budget Constrained (DBC) Scheduling
  Experiments on World Wide Grid testbed
• Conclusions

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Economic Models

• Price-based Supply,demand,value, wealth of
  economic system
• Commodity Market Model
• Posted Price Model
• Bargaining Model
• Tendering (Contract Net) Model
• Auction Model
• English, first-price sealed-bid, second-price
  sealed-bid (Vickrey), and Dutch
  (consumerlow,high,rate producerhigh, low,
  rate)
• Proportional Resource Sharing Model
• Monopoly (one provider) and Oligopoly (few
  players)
• consumers may not have any influence on prices.
• Bartering
• Shareholder Model
• Partnership Model

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Grid Open Trading Protocols
Trade Manager
Trade Server
Get Connected
Pricing Rules
Reply to Bid (DT)
Negotiate Deal(DT)
.
API
Confirm Deal(DT, Y/N)
DT - Deal Template - resource requirements (BM)
- resource profile (BS) - price (any one can
set) - status - change the above values
- negotiation can continue -
accept/decline - validity period
Cancel Deal(DT)
Change Deal(DT)
Get Disconnected
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Various Criteria for Judging Effectiveness of
Economic Models

• Social Welfare
• global good of all
• Pareto Efficiency
• global perspective
• Individual Rationality
• better off by participating in negotiation
• Stability
• mechanisms that cannot be manipulated
• Computational Efficiency
• protocols should not consume too much of time
• Distribution and Communication Efficiency
• communication overhead to capture a desirable
  global solution

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A Commodity Market Model
Grid Info. Service
Grid Market Directory (GMD)
register me as GSP
Give me list of GSPs price?
service available?
Solve this in 5hrs for 20
Resource Broker
(Grid Service Provider)
(RB selects GSPs)
service available?
(GSP)
service available?
(GTS - Grid Trade Server)
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How to decide Price ?

• Fixed price model (like todays Internet)
• Dynamic/Demand and Supply (like tomorrows
  Internet)
• Usage Period
• Loyalty of Customers (like Airlines favoring
  frequent flyers!)
• Historical data
• Advance Agreement (high discount for
  corporations)
• Usage Timing (peak, off-peak, lunch time)
• Calendar based (holiday/vacation period)
• Bulk Purchase (register 100 .com domains at
  once!)
• Voting -- trade unions decide pricing structure
• Resource capability as benchmarked in the market!
• Academic RD/public-good application users can be
  offered at cheaper rate compared to commercial
  use.
• Customer Type Quality or price sensitive
  buyers.
• Can be Prescribed by Regulating (Govt.)
  authorities

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Posted Price Model
Grid Info. Service
Grid Market Directory (GMD)
T3E, 9/hr, Sunday
Any SP2/T3E? offers Free or lt 2/hr
clustersmatlab
Solve this by next day for 5
Resource Broker
(Grid Service Provider)
5MB free
2hrs SP2, 5
(GSP)
Free for Genome
10 discount today
(GTS - Grid Trade Server)
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Bargaining Model
Grid Info. Service
Grid Market Directory (GMD)
register me as GSP
Give me list of GSPs
access price ?, 2, 3 ?
Solve this in 5hrs for 20
Resource Broker
(Grid Service Provider)
(RB negotiates for the best price)
access price ?
(GSP)
access price ?
(GTS - Grid Trade Server)
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Tender/Contract-Net Model
Grid Info. Service
Grid Market Directory (GMD)
Any Ads for service tenders
Post call for tenders
gsp1 bid
Solve this in 15hrs for 10
Resource Broker
(Grid Service Provider)
gsp2 bid?
(GSPs bid)
gsp3 bid?
(GSP)
gspN bid?
Closed Reverse Auction ? Buyers name their price
and supplies compete to bid the lowest price. Eg
GotFrom.com
(GTS - Grid Trade Server)
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Auction Model
Grid Info. Service
Grid Market Auctioneer (GMA)
Solve this in 20 hrs for 5
Post auction T3E service
2, gsp1
Resource Broker
(Grid Service Provider)
(RBs bid)
4, gsp1 2, gsp2
.
Solve this in 1 hrs for 35
Resource Broker
SP2 time, 9pm-8am
(GSP)
(GTS - Grid Trade Server)
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Auction Types

• English Auction - first-price open-cry
• each bidder free to raise his bid and highest
  bidder wins at his bid price.
• First-price sealed bid
• each bidder submits one bid and highest bidder
  wins at his bid price.
• Vickrey Auction- Second-price sealed bid
• each bidder submits one bid and highest bidder
  wins at the price of second highest bid.
• Dutch (descending) Auction
• seller continuously lowers the price until one of
  the bidders bids.

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Proportional Resource Sharing Model
Grid Info. Service
Grid Market Directory (GMD)
Solve this in 20 hrs for 5
Post auction T3E service
Give me list of GSPs
Resource Broker
RB1 2
(Grid Service Provider)
(RBs bid)
RBn 4
.
Solve this in 1 hrs for 50
Resource Broker
SP2 time, 9pm-8am
(GSP)
Resource Share ? Bid/(sum of all bids). E.g., RB1
share 1/3 RB n share - 2/3
(GTS - Grid Trade Server)
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Bartering Partnership/Shareholder

• A group of organizations pool in resources
  (money) together or govt. funded.
• APAC (Australian Partnership for Advance
  Computing)
• VPAC (Victorian PAC) - VIC unis and govt.
• NPACI in US
• Allocation proportional to contribution.
• When in demand follow proportional resource
  sharing strategy QoS.
• A group of individuals pool in resources (idle
  cycles) like in Condor pool.
• Contribute for public good/fame (SETI_at_Home or
  distributed.net)

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Agenda

• A quick glance at todays Grid computing
• Resource Management challenges for next
  generation Grid computing
• A Glance at Approaches to Grid computing
• Grid Architecture for Computational Economy
• Economic Models for Resource Management
• Nimrod-G -- Grid Resource Broker
• Deadline and Budget Constrained (DBC) Scheduling
  Experiments on World Wide Grid testbed
• Conclusions

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Nimrod/G A Grid Resource Broker

• A resource broker for managing, steering, and
  executing task farming (parametric sweep/SPMD
  model) applications on Grid based on deadline and
  computational economy.
• Based on users QoS requirements, our Broker
  dynamically leases services at runtime depending
  on their quality, cost, and availability.
• Key Features
• A single window to manage control experiment
• Persistent and Programmable Task Farming Engine
• Resource Discovery
• Resource Trading
• Scheduling Predications
• Generic Dispatcher Grid Agents
• Transportation of data results
• Steering data management
• Accounting

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Parametric Processing
Parameters
Magic Engine
Multiple Runs Same Program Multiple Data
Killer Application for the Grid!
See IPDPS 2000 paper!
Courtesy Anand Natrajan, University of Virginia
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Parameter Processing on the Grid
Aggregate Job Submission Aggregate View
Submit Play!
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A Glance at Nimrod-G Broker
Nimrod/G Client
Nimrod/G Client
Nimrod/G Client
Nimrod/G Engine
Schedule Advisor
Trading Manager
Grid Store
Grid Dispatcher
Grid Explorer
Grid Middleware
TM TS
Globus, Legion, Condor, etc.
GE GIS
Grid Information Server(s)
RM TS
RM TS
RM TS
G
C
L
G
Legion enabled node.
Globus enabled node.
L
G
C
L
RM Local Resource Manager, TS Trade Server
Condor enabled node.
See HPCAsia 2000 paper!
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A Nimrod/G Monitor
Deadline
Legion hosts
Globus Hosts
Bezek is in both Globus and Legion Domains
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User Requirements Deadline/Budget
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Adaptive Scheduling Algorithms
See HPDC AMS 2000 paper!
Discover More Resources
Discover Resources
Establish Rates
Compose Schedule
Evaluate Reschedule
Meet requirements ? Remaining Jobs, Deadline,
Budget ?
Distribute Jobs
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Agenda

• A quick glance at todays Grid computing
• Resource Management challenges for next
  generation Grid computing
• A Glance at Approaches to Grid computing
• Grid Architecture for Computational Economy
• Economic Models for Resource Management
• Nimrod-G -- Grid Resource Broker
• Deadline and Budget Constrained (DBC) Scheduling
  Experiments on World Wide Grid testbed
• Conclusions

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Experiment Setup

• Workload (Hypothetical Application)
• 165 jobs, each need 5 minute of CPU time
• Deadline 2 hrs. and budget 396000 units (G)
• Strategy minimise time / cost
• Execution Cost with cost optimisation
• Optimise Cost 115200 (G) (finished in 2hrs.)
• Optimise Time 237000 (G) (finished in 1.25 hr.)
• In this experiment Time-optimised scheduling run
  costs double that of Cost-optimised.
• Users are able to trade-off between Time Vs. Cost
  depending on QoS requirements.

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World Wide Grid (WWG)
Australia
North America
ANL SGI/Sun/SP2 USC-ISI SGI UVa Linux
Cluster UD Linux cluster UTK Linux cluster
Monash Uni.
Nimrod/G
Linux cluster
GlobusLegion GRACE_TS
Globus/Legion GRACE_TS
Solaris WS
Internet
Asia/Japan
Europe
Tokyo I-Tech. ETL, Tuskuba
ZIB/FUB T3E/Mosix Cardiff Sun E6500 Paderborn
HPCLine Lecce Compaq SC CNR Cluster Calabria
Cluster CERN Cluster Pozman SGI/SP2
Linux cluster
Globus GRACE_TS
Chile Cluster
Globus GRACE_TS
Globus GRACE_TS
South America
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Resources Selected Price/CPU-sec.
No. of Jobs Executed
Cost/CPU sec.in G
Grid services Fabric
Resource Location
Cost_Opt.
Time_Opt
153
64
2
Globus, GTS, Condor
Linux Cluster-Monash, Melbourne, Australia
1
7
3
Globus, GTS, Fork
Linux-Prosecco-CNR, Pisa, Italy
1
6
4
Globus, GTS, Fork
Linux-Barbera-CNR, Pisa, Italy
1
9
3
Globus, GTS, Fork
Solaris/Ultas2 TITech, Tokyo, Japan
5
37
8
Globus, GTS, Fork
SGI-ISI, LA, US
4
42
7
Globus, GTS, Fork
Sun-ANL, Chicago,US
Total Experiment Cost (G) 237000 115200
Time to Complete Exp. (Min.) 70 119
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DBC Scheduling for Time Optimization
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DBC Scheduling for Cost Optimization
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Agenda

• A quick glance at todays Grid computing
• Resource Management challenges for next
  generation Grid computing
• A Glance at Approaches to Grid computing
• Grid Architecture for Computational Economy
• Economic Models for Resource Management
• Nimrod-G -- Grid Resource Broker
• Deadline and Budget Constrained (DBC) Scheduling
  Experiments on World Wide Grid testbed
• Conclusions Pointers

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Summary and Conclusions

• P2P and Grid Computing is emerging as a next
  generation computing platform for solving large
  scale problems through sharing of geographically
  distributed resources.
• Resource management is a complex undertaking as
  systems need to be adaptive, scalable,
  competitive,, and driven by QoS.
• We proposed a framework based on computational
  economies and discussed several economic models
  for resource allocation and for regulating
  supply-and-demand for resources.
• Scheduling experiments on World Wide Grid
  demonstrate our Nimrod-G broker ability to
  dynamically lease or rent services at runtime
  based on their quality, cost, and availability
  depending on consumers QoS requirements.
• Economics paradigm for QoS driven resource
  management is essential to push P2P/Grids into
  mainstream computing!

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Download Software Information

• Nimrod Parameteric Computing
• http//www.csse.monash.edu.au/davida/nimrod/
• Economy Grid Nimrod/G
• http//www.buyya.com/ecogrid/
• Virtual Laboratory/Virtual Drug Design
• http//www.buyya.com/vlab/
• Grid Simulation (GridSim) Toolkit (Java based)
• http//www.buyya.com/gridsim/
• World Wide Grid (WWG) testbed
• http//www.buyya.com/ecogrid/wwg/
• Looking for new volunteers to grow ?
• Please contact me to barter your our machines!
• Want to build on our work/collaborate
• Talk to me now or email rajkumar_at_csse.monash.edu.
  au