Grid Resource Brokering and
Cost-based Scheduling
With Nimrod-G and Gridbus Case Studies
Rajkumar Buyya
Cloud Computing and Distributed Systems (CLOUDS) Lab.
The University of Melbourne
Melbourne, Australia
www.cloudbus.org
Agenda



Introduction to Grid Scheduling
Application Models and Deployment Approaches
Economy-based “Computational” Grid Scheduling



Nimrod-G -- Grid Resource Broker
Scheduling Algorithms and Experiments on World Wide
Grid testbed
Economy-based “Data Intensive” Grid Scheduling


Gridbus -- Grid Service Broker
Scheduling Algorithms and Experiments on Australian
Belle Data Grid testbed
Grid
Grid
Economy
2
Scheduling
Economics
Grid Scheduling: Introduction
Grid Resources and Scheduling
User Application
Grid Resource Broker
Local Resource Manager
Single CPU
(Time Shared Allocation)
4
Local Resource Manager
SMP
(Time Shared Allocation)
Grid Information Service
Local Resource Manager
2100
2100
2100
2100
2100
2100
2100
2100
Clusters
(Space Shared Allocation)
Grid Scheduling

Grid scheduling:




Grid schedulers are Global Schedulers



5
Resources distributed over multiple administrative
domains
Selecting 1 or more suitable resources (may involve
co-scheduling)
Assign tasks to selected resources and monitoring
execution.
They have no ownership or control over resources
Jobs are submitted to local resource managers (LRMs)
as user
LRMs take care of actual execution of jobs
Example Grid Schedulers

Nimrod-G - Monash University


Condor-G – University of Wisconsin


Computational Grid & System centric
Gridbus Broker – University of Melbourne

6
Computational Grid & System-centric
AppLeS–University of California@San
Diego


Computational Grid & Economic-based
Data Grid & Economic based
Key Steps in Grid Scheduling
Phase I-Resource Discovery
1. Authorization Filtering
Phase III- Job Execution
2. Application Definition
3. Min. Requirement Filtering
6. Advance Reservation
7. Job Submission
8. Preparation Tasks
Phase II - Resource Selection
9. Monitoring Progress
10 Job Completion
4. Information Gathering
11. Clean-up Tasks
5. System Selection
7
Source: J. Schopf, Ten Actions When SuperScheduling, OGF Document, 2003.
Movement of Jobs: Between the
Scheduler and a Resource



8
Push Model
 Manager pushes jobs from Queue to a resource.
 Used in Clusters, Grids
Pull Model
 P2P Agent request for a job for processing from job-pool
 Commonly used in P2P systems such as Alchemi and
SETI@Home
Hybrid Model (both push and pull)
 Broker deploys an agent on resources, which pulls jobs from
a resource.
 May use in Grid (e.g., Nimrod-G system).
 Broker also pulls data from user host or separate data host
(distributed datasets) (e.g., Gridbus Broker).
Example Systems
Job Push
Dispatch
Pull
Hybrid
Architecture
9
Centralised
PBS, SGE,
Condor,
Alchemi (when
in dedicated
mode)
Windmill from
CERN (used in
Physics ATLAS
experiment)
Condor (as it
supports nondedicated
owner specified
policies)
Decentralised
Nimrod-G,
AppLeS,
Condor-G,
Gridbus Broker
Alchemi,
SETI@Home,
UnitedDevice,
P2P Systems,
Aneka
Nimrod-G (push
Grid Agent,
which pulls
jobs)
Application Models and their
Deployment on Global Grids
Grid Applications and Parametric
Computing
Bioinformatics:
Drug Design / Protein
Modelling
Natural Language
Engineering
Sensitivity
experiments
on smog formation
Computer Graphics:
Ray Tracing
High Energy
Physics:
Searching for
Rare Events
Ecological Modelling:
Control Strategies
for Cattle Tick
Data Mining
Electronic CAD:
Field Programmable
Gate Arrays
VLSI Design:
Finance:
SPICE Simulations
Investment Risk Analysis
Civil Engineering:
Building Design
Automobile:
Crash Simulation
11
Network Simulation
Aerospace:
Wing Design
astrophysics
How to Construct and Deploy
Applications on Global Grids ?

Three Options/Solutions:



Manual Scheduling - Use pure Globus commands
Application Level Scheduling - Build your own
Distributed App & Scheduler
Application Independent Scheduling – Grid Brokers


12
Decouple App Construction from Scheduling
Perform parameter sweep (bag of tasks)
(utilising distributed resources) within “T” hours
or early and cost not exceeding $M.
Using Pure Globus commands
Do all yourself! (manually)
Total Cost:$???
13
Build Distributed Application &
Application-Level Scheduler
Build App and scheduler
case by case basis
14
E.g., MPI Approach
Total Cost:$???
Compose and Deploy using Brokers –
Nimrod-G and Gridbus Approach
•Compose Apps and Submit
to the Broker
• Define QoS requirements
•Aggregate View
90
80
70
60
50
40
East
West
North
South
30
20
10
0
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
Compose, Submit & Play!
15
The Nimrod-G Grid Resource Broker
and Economy-based Grid Scheduling
[Buyya, Abramson, Giddy, 1999-2001]
Deadline and Budget Constrained
Algorithms for Scheduling
Applications on “Computational” Grids
Nimrod-G : A Grid Resource Broker



A resource broker (implemented using Python) for
managing, steering, and executing task farming
(parameter sweep) applications on global Grids.
It allows dynamic leasing of resources at runtime based
on their quality, cost, and availability, and users’ QoS
requirements (deadline, budget, etc.)
Key Features










17
A declarative parameter programming language
A single window to manage & control experiment
Persistent and Programmable Task Farming Engine
Resource Discovery
Resource Trading
(User-Level) Scheduling & Predications
Generic Dispatcher & Grid Agents
Transportation of data & results
Steering & data management
Accounting
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
Globus, Legion, Condor, etc.
TS
GE
GIS
Grid Information Server(s)
RM & TS
$
RM & TS
G
$
RM & TS
C
L
G
Globus enabled node.
See HPCAsia 2000 paper!
18
L
Legion enabled
node.
RM: Local Resource Manager, TS: Trade Server
$
G
C L
Condor enabled node.
Nimrod/G Grid Broker Architecture
Legacy Applications
Customised Apps
(Active Sheet)
P-Tools (GUI/Scripting)
(parameter_modeling)
Monitoring and
Steering Portals
Farming Engine
Meta-Scheduler
Algorithm1
Programmable Entities Management
Resources
IP hourglass!
Jobs Tasks
Schedule
Advisor
Channels
Agents
JobServer
Database
Grid
Explorer
Dispatcher & Actuators
Globus-A
Legion-A
Condor-A
Legion
Condor
Globus
Computers
19
...
AlgorithmN
AgentScheduler
Local Schedulers
PC/WS/Clusters
Nimrod-G
Clients
...
Trading
Manager
P2P-A
P2P
...
Storage
Condor/LL/NQS
Nimrod-G
Broker
GTS
Networks
Database
...
GMD
...
G-Bank
Instruments
Radio Telescope
Middleware
Fabric
Deadline
A Nimrod/G
CostMonitor
66 Arlington
Alexandria
Legion hosts
She na nd o a h
Rive r
64
64
81
Ra p p a ha n no c k Po to m a c
Rive r
Rive r
Roanoke
Ja m e s
Rive r
Ap p o m a to x
Rive r
Richmond
Hampton
Norfolk
Virginia Beach
Portsmouth Chesapeake
Newport News
77
VIRGINIA
85
Globus Hosts
Bezek is in both
Globus and Legion Domains
20
User Requirements: Deadline/Budget
21
Nimrod/G Interactions
Nimrod-G Grid Broker
Grid
Scheduler
Grid Tools
And
Applications
Task
Farming
Engine
Grid
Dispatcher
Grid Info
Server
Grid Trade
Server
Process
Server
Local
Resource
Manager
Nimrod
Agent
User
Process
Do this in 30
min. for $10?
File
Server
User Node
22
File access
Grid Node
Compute Node
Adaptive Scheduling Steps
Discover Establish
Resources
Rates
Distribute Jobs
23
Compose &
Schedule
Discover
More
Resources
Evaluate &
Reschedule
Meet requirements ? Remaining
Jobs, Deadline, & Budget ?
Deadline and Budget Constrained
Scheduling Algorithms
24
Algorithm/
Strategy
Execution Time Execution Cost
(Deadline, D)
(Budget, B)
Cost Opt
Limited by D
Minimize
Cost-Time Opt
Minimize when
possible
Minimize
Time Opt
Minimize
Limited by B
Conservative-Time
Opt
Minimize
Limited by B, but all
unprocessed jobs
have guaranteed
minimum budget
Deadline and Budget-based
Cost Minimization Scheduling
1.
2.
3.
25
Sort resources by increasing cost.
For each resource in order, assign as
many jobs as possible to the resource,
without exceeding the deadline.
Repeat all steps until all jobs are
processed.
Scheduling Algorithms and
Experiments
World Wide Grid (WWG)
WW Grid
Australia
North America
ANL: SGI/Sun/SP2
USC-ISI: SGI
UVa: Linux Cluster
UD: Linux cluster
UTK: Linux cluster
UCSD: Linux PCs
BU: SGI IRIX
Melbourne U. : Cluster
Nimrod-G+Gridbus
VPAC: Alpha
Globus+Legion
GRACE_TS
Solaris WS
Globus/Legion
GRACE_TS
Europe
Asia
WW Grid
Internet
Tokyo I-Tech.: Ultra WS
AIST, Japan: Solaris
Cluster
Kasetsart, Thai: Cluster
NUS, Singapore: O2K
Globus +
GRACE_TS
Chile: Cluster
27
Globus +
GRACE_TS
South America
ZIB: T3E/Onyx
AEI: Onyx
Paderborn: HPCLine
Lecce: Compaq SC
CNR: Cluster
Calabria: Cluster
CERN: Cluster
CUNI/CZ: Onyx
Pozman: SGI/SP2
Vrije U: Cluster
Cardiff: Sun E6500
Portsmouth: Linux PC
Manchester: O3K
Globus +
GRACE_TS
Application Composition Using Nimrod
Parameter Specification Language
#Parameters Declaration
parameter X integer range from 1 to 165 step 1;
parameter Y integer default 5;
#Task Definition
task main
#Copy necessary executables depending on node type
copy calc.$OS node:calc
#Execute program with parameter values on remote node
node:execute ./calc $X $Y
#Copy results file to use home node with jobname as extension
copy node:output ./output.$jobname
endtask
28
calc 1 5  output.j1
calc 2 5  output.j2
calc 3 5  output.j3
…
calc 165 5  output.j165
Experiment Setup

Workload:




Deadline: 2 hrs. and budget: 396000 G$
Strategies: 1. Minimise cost 2. Minimise time
Execution:




29
165 jobs, each need 5 minute of CPU time
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 can now trade-off between Time Vs. Cost.
Resources Selected & Price/CPU-sec.
30
Resource &
Location
Grid services &
Fabric
Cost/CPU
sec.or unit
No. of Jobs Executed
Linux Cluster-Monash,
Melbourne, Australia
Globus, GTS, Condor
2
64
153
Linux-Prosecco-CNR,
Pisa, Italy
Globus, GTS, Fork
3
7
1
Linux-Barbera-CNR,
Pisa, Italy
Globus, GTS, Fork
4
6
1
Solaris/Ultas2
TITech, Tokyo, Japan
Globus, GTS, Fork
3
9
1
SGI-ISI, LA, US
Globus, GTS, Fork
8
37
5
Sun-ANL, Chicago,US
Globus, GTS, Fork
7
42
4
Time_Opt
Cost_Opt.
Total Experiment Cost (G$)
237000
115200
Time to Complete Exp. (Min.)
70
119
Deadline and Budget Constraint
(DBC) Time Minimization Scheduling
1.
2.
3.
4.
31
For each resource, calculate the next
completion time for an assigned job, taking into
account previously assigned jobs.
Sort resources by next completion time.
Assign one job to the first resource for which
the cost per job is less than the remaining
budget per job.
Repeat all steps until all jobs are processed.
(This is performed periodically or at each
scheduling-event.)
Resource Scheduling for DBC Time
Optimization
Condor-Monash
Linux-Prosecco-CNR
Linux-Barbera-CNR
Solaris/Ultas2-TITech
SGI-ISI
Sun-ANL
12
No. of Tasks in Execution
10
8
6
4
2
32
Time (in Minute)
68
72
60
64
52
56
44
48
36
40
28
32
20
24
16
8
12
4
0
0
Resource Scheduling for DBC Cost
Optimization
Condor-Monash
Linux-Prosecco-CNR
Linux-Barbera-CNR
Solaris/Ultas2-TITech
SGI-ISI
Sun-ANL
14
No. of Tasks in Execution
12
10
8
6
4
2
Time (in Minute)
33
10
2
10
8
11
4
96
90
84
78
72
66
60
54
48
42
36
30
24
18
6
12
0
0
Nimrod-G Summary



One of the “first” and most successful
Grid Resource Brokers world-wide!
Project continues to be active and being
used in many e-Science applications.
For recent developments, please see:

34
http://messagelab.monash.edu.au/Nimrod
Gridbus Broker
“Distributed” Data-Intensive
Application Scheduling
Gridbus Grid Service Broker (GSB)



A Java-based resource broker for Data Grids
(Nimrod-G focused on Computational 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








36
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
workload
Gridbus User Console/Portal/Application Interface
App, T, $, Optimization Preference
Gridbus Broker
Gridbus Farming Engine
Schedule Advisor
Trading Manager
Record
Keeper
Grid Dispatcher
Core Middleware
Grid Explorer
TM
$
TS
GE
GIS, NWS
Grid Info Server
RM & TS
$
G
$
U
Data
Node
Data
Catalog
C
G
Globus
enabled
37 node.
L
Amazon EC2/S3 Cloud.
A
Gridbus Broker: Separating “applications”
from “different” remote service access
enablers and schedulers
Application Development Interface
Home Node/Portal
Scheduling
Interfaces
Alogorithm1
AlogorithmN
Single-sign on security
batch()
Gridbus
Broker
fork()
-PBS
-Condor
-SGE
-Aneka
-XGrid
Data Catalog
Plugin Actuators
Aneka
Globus
Data Store
Job manager
Amazon EC2
Access Technology
fork()
batch()
-PBS
-Condor
-SGE
38
SRB
Gridbus
agent
Grid FTP
AMI
SSH
fork()
batch()
-PBS
-Condor
-SGE
-XGrid
Gridbus
agent
Gridbus Services for eScience
applications



39
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.
Click Here for Demo
Drug Design
Made Easy!
40
s
http://www.gridbus.org
AASample
SampleList
Listof
ofGridbus
GridbusBroker
BrokerUsers
Users
Molecular
Moleculardocking
dockingfor
fordrug
drugdesign
design
on
Australian
National
Grid
on Australian National Grid
High
HighEnergy
EnergyPhysics:
Physics:
Particle
Discovery
Particle Discovery
NeuroScience:
NeuroScience:Brain
Brain
Activity
Analysis
Activity Analysis
Melbourne University
EU
EUData
DataMining
MiningGrid
Grid
41
DaimlerChrysler,
Technion, U. Ljubljana,
U. Ulster
Kidney/Human
Kidney/Human
Physiome
PhysiomeModelling
Modelling
Melbourne Medical Faculty,
Université d'Evry, France
Finance
Finance/Investment
/InvestmentRisk
Risk
Studies:
Spanish
Stock
Studies: Spanish StockMarket
Market
Universidad
Complutense de
Madrid, Spain
Case Study:
High Energy Physics and Data Grid

The Belle Experiment



42

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
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.
43
Australian Belle Data Grid Testbed
Certificate
Authority
Analysis Request
Analysis Results
Virtual
Organization
Replica
Catalog
NWS
NameServer
Grid
Service
Broker
Globus
Gatekeeper
GRIS
Globus
Gatekeeper
GRIS
NWS
Sensor
NWS
Sensor
GridFTP
Dual Intel Xeon 2.8 Ghz,
2 GB RAM
GridFTP
Dept. of Physics,
University of Sydney
Dual Intel Xeon 2.8 Ghz,
2 GB RAM
AARNET
GRIDS Lab,
University of Melbourne
Globus
Gatekeeper
GRIS
Globus
Gatekeeper
GRIS
NWS
Sensor
GridFTP
Globus
Gatekeeper
GRIS
NWS
Sensor
GridFTP
NWS
Sensor
GridFTP
Dual Intel Xeon 2.8 Ghz,
2 GB RAM
ANU, Canberra
VPAC
Melbourne
44
Intel Pentium 2.0 Ghz,
512 MB RAM
Dept. of Physics,
University of Melbourne
Dual Intel Xeon 2.8 Ghz,
2 GB RAM
Dept. of Computer Science,
University of Adelaide
Belle Data Grid (GSP CPU Service Price: G$/sec)
Certificate
Authority
Analysis Request
Analysis Results
Virtual
Organization
Replica
Catalog
NWS
NameServer
Grid
Service
Broker
Globus
Gatekeeper
GRIS
Globus
Gatekeeper
GRIS
NWS
Sensor
NWS
Sensor
GridFTP
G$4
Dual Intel Xeon 2.8 Ghz,
2 GB RAM
GridFTP
Dept. of Physics,
University of Sydney
NA
Dual Intel Xeon 2.8 Ghz,
2 GB RAM
Globus
Gatekeeper
AARNET
GRIDS Lab,
University of Melbourne
GRIS
Globus
Gatekeeper
GRIS
NWS
Sensor
Globus
Gatekeeper
GridFTP
GRIS
NWS
Sensor
GridFTP
NWS
Sensor
Dual Intel Xeon 2.8 Ghz,
2 GB RAM
GridFTP
ANU, Canberra
VPAC
Melbourne
45
G$6
Intel Pentium 2.0 Ghz,
512 MB RAM
Dept. of Physics,
University of Melbourne
G$2
Dual Intel Xeon 2.8 Ghz,
2 GB RAM
Data
node
Dept. of Computer Science,
University of Adelaide
G$4
Belle Data Grid (Bandwidth Price: G$/MB)
Certificate
Authority
Analysis Request
Analysis Results
Virtual
Organization
NWS
NameServer
Replica
Catalog
Grid
Service
Broker
Globus
Gatekeeper
Globus
Gatekeeper
GRIS
32
33
36
NWS
Sensor
GRIS
31
GridFTP
NA
30
31
GRIDS Lab,
University of Melbourne
Dept. of Physics,
University of Sydney
NWS
Sensor
Globus
Gatekeeper
GridFTP
GRIS
GRIS
NWS
Sensor
GridFTP
NWS
Sensor
Dual Intel Xeon 2.8 Ghz,
2 GB RAM
GridFTP
ANU, Canberra
VPAC
Melbourne
46
G$6
Intel Pentium 2.0 Ghz,
512 MB RAM
Dept. of Physics,
University of Melbourne
G$4
Dual Intel Xeon 2.8 Ghz,
2 GB RAM
Globus
Gatekeeper
AARNET
GRIS
GridFTP
34
38
Dual Intel Xeon 2.8 Ghz,
2 GB RAM
Globus
Gatekeeper
NWS
Sensor
G$2
Dual Intel Xeon 2.8 Ghz,
2 GB RAM
Data
node
Dept. of Computer Science,
University of Adelaide
G$4
Deploying Application Scenario




A data grid scenario with 100 jobs and
each accessing remote data of ~30MB
Deadline: 3hrs.
Budget: G$ 60K
Scheduling Optimisation Scenario:



47
Minimise Time
Minimise Cost
Results:
SUMMARY OF EVALUATION RESULTS
Scheduling strategy
Total Time
Compute
Data
Taken
Cost
Cost
(mins.)
(G$)
(G$)
Total Cost
(G$)
Cost Minimization
Time Minimization
34425
58390
71.07
48.5
26865
50938
7560
7452
Time Minimization in Data Grids
fleagle.ph.unimelb.edu.au
belle.anu.edu.au
belle.physics.usyd.edu.au
brecca-2.vpac.org
80
70
Number of jobs completed
60
50
40
30
20
10
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
Time (in mins.)
48
Results : Cost Minimization in Data
Grids
fleagle.ph.unimelb.edu.au belle.anu.edu.au belle.physics.usyd.edu.au brecca-2.vpac.org
100
90
80
Number of jobs completed
70
60
50
40
30
20
10
0
1
49
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63
Time(in mins.)
Observation
Organization
50
SUMMARY OF EVALUATION RESULTS
Scheduling strategy
Total Time
Compute
Data
Taken
Cost
Cost
(mins.)
(G$)
(G$)
Total Cost
(G$)
Cost Minimization
Time Minimization
34425
58390
Node details
71.07
48.5
26865
50938
Cost (in G$/CPUsec)
7560
7452
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
N.A. (Not used as a
compute resource)
--
--
ANU, Canberra
belle.anu.edu.au
4 CPU, 2GB RAM, 40 GB HD,
Linux
4
2
2
Dept of Physics,
USyd
belle.physics.usyd.edu.au
4 CPU (only 1 available), 2GB
RAM, 40 GB HD, Linux
4
72
2
VPAC,
Melbourne
brecca-2.vpac.org
180 node cluster (only head node
used), Linux
6
23
2
Summary and Conclusion
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51
Application scheduling on global Grids is a complex
undertaking as systems need to be adaptive, scalable,
competitive,…, and driven by QoS.
Nimrod-G is one of the popular Grid Resource Broker for
scheduling parameter sweep applications on Global Grids
Scheduling experiments on the World Wide Grid
demonstrate Nimrod-G broker ability to dynamically lease
services at runtime based on their quality, cost, and
availability depending on consumers QoS requirements.
Easy to use tools for creating Grid applications are essential
for success of Grid Computing.
References
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52
Rajkumar Buyya, David Abramson, Jonathan Giddy, Nimrod/G: An
Architecture for a Resource Management and Scheduling System
in a Global Computational Grid, Proceedings of the 4th
International Conference on High Performance Computing in AsiaPacific Region (HPC Asia 2000), Beijing, China. IEEE Computer
Society Press, USA, 2000.
David Abramson, Rajkumar Buyya, and Jonathan Giddy, A
Computational Economy for Grid Computing and its Implementation
in the Nimrod-G Resource Broker, Future Generation Computer
Systems (FGCS) Journal, Volume 18, Issue 8, Pages: 1061-1074,
Elsevier Science, The Netherlands, October 2002.
Jennifer Schopf, Ten Actions When SuperScheduling, Global Grid
Forum Document GFD.04, 2003.
Srikumar Venugopal, Rajkumar Buyya and Lyle Winton, A Grid
Service Broker for Scheduling e-Science Applications on Global
Data Grids, Concurrency and Computation: Practice and
Experience, Volume 18, Issue 6, Pages: 685-699, Wiley Press, New
York, USA, May 2006.