gridsim 2.0 adv. grid modelling & simulation toolkit
DESCRIPTION
GridSim 2.0 Adv. Grid Modelling & Simulation Toolkit. Rajkumar Buyya, Manzur Murshed (Monash), Anthony Sulistio , Chee Shin Yeo. Gri d Computing and D istributed S ystems (GRIDS) Lab, Dept. of Computer Science and Software Engineering The University of Melbourne www.gridbus.org. - PowerPoint PPT PresentationTRANSCRIPT
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GridSim 2.0Adv. Grid Modelling & Simulation
Toolkit
Rajkumar Buyya, Manzur Murshed (Monash),
Anthony Sulistio, Chee Shin Yeo
Grid Computing and Distributed Systems (GRIDS) Lab,
Dept. of Computer Science and Software Engineering
The University of Melbournewww.gridbus.org
Thanks to David Abramson
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Outline
• Motivation.• System Architecture.• GridSim Entities.• Visual Modeller.• Experiments.
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Performance Evaluation: With Large Scenarios
• Varying the number of Resources (1 to 100s..1000s..). Resource capability. Cost (Access Price). Users. Deadline and Budget. Workload. Different Time (Peak and Off-Peak).
• We need a repeatable and controllable environment.
• Can this be achieved on Real Grid testbed ?
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Grid Environment
• Dynamic: 1. Resource and User Properties vary with time.
Experiment cannot be repeated.
2. Resources are distributed and owned by different organisations. Heterogeneous users.
It is hard to create a controllable environment.
• Grid testbed size is limited.• Also, creating testbed infrastructure is time
consuming and expensive.• Hence, grid computing researchers turn to
modelling and simulation.
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GridSim Toolkit• GridSim 1.0 released in Dec. 2001
GridSim and GridBroker.
• GridSim 2.0 released in Nov. 2002 @ SC 2002. Improvements in GridSim and GridBroker. Add Visual Modeler.
• Few functionalities of GridSim: Allows modelling of heterogeneous of resources &
users. Supports simulation of both static & dynamic
schedulers. Simulates applications with different parallel models.
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System Architecture
Basic Discrete Event Simulation Infrastructure
Virtual Machine (Java, cJVM, RMI)
PCs ClustersWorkstations
. . .
SMPs Distributed Resources
GridSim Toolkit
Application Modeling
InformationServices
Resource Allocation
Grid Resource Brokers or Schedulers
Statistics
Resource Modeling and Simulation (with Time and Space shared schedulers)
Job Management
ClustersSingle CPU ReservationSMPs Load Pattern
Application Configuration
Resource Configuration
Visual Modeler
Grid Scenario
Network
SimJava Distributed SimJava
Resource Entities
Output
Application, User, Grid Scenario’s Input and Results
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GridSim Entities
JobsApplication
Scheduler
User #i Broker #i Output
Input
Output
Input
Resource #j
Job In Queue
Job Out QueueProcess Queue
Resource List
InformationService
Internet
Report
Writer #i
Statis
tics
Recor
der #i
Shutdown
Signal Manager #i
InputOutput
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EA
Output_EA
Input_EA
EB
Output_EB
Input_EB
body()
Send(output, data, EB)……
body()
body()
…
…body() …
body()
…
body()
Receive(input, data, EA)……
Timed Event Delivery
data, t2
(Deliver data @ t2)
GridSim Entities Communication Model
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Time Shared: Multitasking and Multiprocessing
PE1
PE2
G1
G2
G3
G1
G2G2
G2
G3G3
P1-G2P1-G1 P3-G2 P1-G3P2-G3
TimeG1
G1: Gridlet1 Arrives
G1FG3
G1F: Gridlet1 Finishes
G2 G2F G3F
Gridlet1 (10 MIs)
Gridlet2 (8.5 MIs)
Gridlet3 (9.5 MIs) P2-G2: Gridlet2 finishes at the 2nd prediction time.
P1-G2: Gridlet2 didn’t finish at the 1st prediction time.
Tasks onPEs/CPUs
2 6 9 12 16 19 2622
P2-G2
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Space Shared: Multicomputing
G1
G2
G3
G1 G3
G2 G3
P1-G1 P1-G2 P1-G3
TimeG1
G1: Gridlet1 Arrives
G1FG3
G1F: Gridlet1 Finishes
G2 G2F G3F
Gridlet1 (10 MIs)
Gridlet2 (8.5 MIs)
Gridlet3 (9.5 MIs)P1-G2: Gridlet2 finishes as per the 1st Predication
Tasks onPEs/CPUs
2 6 9 12 16 19 2622
PE1
PE2
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Visual Modeler
• Available in GridSim 2.0• Functionalities:
Create and delete many users and resources.
Able to save and load the model file (XML format).
Generate Java source code.
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Experiment 1
• Create 21 users and 25 resources.• Cost varies from 10 to 20 units per sec
(G$/sec).• Each user has 20 jobs with variation of ± 2.• Want to optimise cost.• Simulation Time approx. 7 hours.• Number of users grows -> Pr (one resource
per user) decreases.• This low Pr demands high D_Factor and
B_Factor in order to achieve very high job completion rate.
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D-Factor
MINMAX
MIN
MIN
MAX
Job_TimeJob_Time
Job_TimeDeadlineFactorD
Job_Time
Job_Time
_
priority highest theresourcefastest thegiving
parallel,in tasks, theall process toTime
resourceslowest theusing
serially, tasks, theall process toTime
• Any job with D_Factor < 0 would never be completed.
• As long as some resources are available throughout the deadline, any job with D_Factor 1 would always be completed.
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B-Factor
MINMAX
MIN
MIN
MAX
Job_CostJob_CostJob_CostBudget
FactorB
deadline
Job_Cost
deadline
Job_Cost
_
priorityhighest theresource
cheapest thegiving , within
parallelin tasks, theall process Cost to
priorityhighest theresource
costliest thegiving , within
parallelin tasks, theall process Cost to
• Any job with B_Factor < 0 would never be completed.
• As long as some resources are available throughout the deadline, any job with B_Factor 1 would always be completed.
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Main Window of Visual Modeler
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View User Property Dialog
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View Resource Property Dialog
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Job Completion & Cost Optimise
50 90
130
170
210 50
1700
20
40
60
80
100
Job completion
rate (%)
B-factor (%)
D-factor (%)
Users=21, Resources=25, Optimization: COST
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Time Utilisation & Cost Optimise
50 90
130
170
210 50
1500
20
40
60
80
100
Time Utilization (%)
B-factor (%)
D-factor (%)
Users=21, Resources=25, Optimization: COST
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Budget Utilisation & Cost Optimise
50 90
130
170
210
50
1700
5
10
15
20
Budget Utilization (%)
B-factor (%)
D-factor (%)
Users=21, Resources=25, Optimization: COST
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Experiment 2• Workload Synthesis:
200 jobs, each job processing requirement = 10K MI or SPEC with random variation from 0-10%.
• Exploration of many scenarios: Deadline: 100 to 3600 simulation time, step =
500. Budget: 500 to 22000 G$, step = 1000.
• Deadline and Budget Constraint (DBC) Strategies: Cost Optimisation for a single user.
• Resources: Simulated WWG resources.
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Simulated WWG ResourcesResource Name in
Simulation
Simulated Resource Characteristics
Vendor, Resource Type, Node OS, No of PEs
Equivalent Resource in Worldwide Grid
(Hostname, Location)
A PE SPEC/ MIPS Rating
Resource Manager
Type
Price
(G$/PE time unit)
MIPS per G$
R0 Compaq, AlphaServer,
CPU, OSF1, 4
grendel.vpac.org,
VPAC, Melb, Australia
515 Time-shared 8 64.37
R1 Sun, Ultra, Solaris, 4 hpc420.hpcc.jp,
AIST, Tokyo, Japan 377 Time-shared 4 94.25
R2 Sun, Ultra, Solaris, 4 hpc420-1.hpcc.jp,
AIST, Tokyo, Japan 377 Time-shared 3 125.66
R3 Sun, Ultra, Solaris, 2 hpc420-2.hpcc.jp,
AIST, Tokyo, Japan 377 Time-shared 3 125.66
R4 Intel, Pentium/VC820,
Linux, 2 barbera.cnuce.cnr.it,
CNR, Pisa, Italy 380 Time-shared 2 190.0
R5 SGI, Origin 3200, IRIX, 6 onyx1.zib.de,
ZIB, Berlin, Germany 410 Time-shared 5 82.0
R6 SGI, Origin 3200, IRIX,
16 Onyx3.zib.de,
ZIB, Berlin, Germany 410 Time-shared 5 82.0
R7 SGI, Origin 3200, IRIX,
16
mat.ruk.cuni.cz, Charles U., Prague,
Czech Republic
410 Space-shared 4 102.5
R8 Intel, Pentium/VC820,
Linux, 2 marge.csm.port.ac.uk,
Portsmouth, UK 380 Time-shared 1 380.0
R9 SGI, Origin 3200, IRIX, 4
(accessible) green.cfs.ac.uk, Manchester, UK
410 Time-shared 6 68.33
R10 Sun, Ultra, Solaris, 8, pitcairn.mcs.anl.gov, ANL, Chicago, USA
377 Time-shared 3 125.66
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Gridlets vs Budget
5000
9000
13000
17000
21000100
600
1100
1600
2100
2600
3100
3600
0
20
40
60
80
100
120
140
160
180
200
Gridlets
Budget
Deadline
100
600
1100
1600
2100
2600
3100
3600
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Impact of budget for deadline values
5000
9000
13000
17000
21000
100
600
1100
1600
2100
2600
3100
3600
0
500
1000
1500
2000
2500
3000
3500
4000
Deadline Time Utilised
Budget
Deadline
100
600
1100
1600
2100
2600
3100
3600
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Budget spent with deadline values
5000
7000
9000
11000
13000
15000
17000
19000
21000
100
1100
2100
3100
0
5000
10000
15000
20000
25000
Budget Spent
Budget
Deadline
100
600
1100
1600
2100
2600
3100
3600
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Selected GridSim Users
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Conclusion
• GridSim toolkit is suitable for application scheduling simulations in Grid and P2P computing environment.
• GridSim 2.0 is available to download:www.gridbus.org
Extending to support Data Grid modelling