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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

25

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