Task Sch PPSO

8/8/2019 Task Sch PPSO

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Parallel Job Submission In Grid Environment

UsingParallel Particle Swarm Optimization

Dr. G. Sudha Sadhasivam

Asst. Professor

Dept. of CSE.PSG College Of Technology.

D. Komagal Meenakshi (07MW05)

PSG College Of Technology.


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Outline Scheduling in Grid.

Problem Statement Need For Job Grouping in Scheduling

PreviousWork Done in Job Grouping

Proposed System

Trust Based Filtering of jobs

Particle Swarm Optimization Parallel PSO

Model for PPSO

Dynamic jobs

Results

Conclusion and Future work Bibliography


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Scheduling in Grid.

Grid computing is a high performance computing

environment to solve large scale computational demands.

Task scheduling is a fundamental issue in achieving

high performance in grid computing systems.

Reason: Large numbers of tasks are computed on the

geographically distributed resources, a reasonable

scheduling algorithm must be adopted order to minimizejob completion time with uniform load distribution.


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Need

An unorganized deployment of grid applications with a

large amount of fine-grain jobs

Leads to

communication overhead dominate the overall processingtime

Low computation-communication ratio.

Results


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Need For Job Grouping in Scheduling

Efficient job grouping-based scheduling system is required.

A Grid Scheduler should

Reduce the total transmission of user jobs to/from the

resources.

Reduce the overhead processing time of the jobs at the

resources.


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Job Grouping

Dynamically

assemble

Transmit

Grid resources

job groups [ coarse grained ]

Jobs of an application [ fine grained ]


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PreviousWork Done in Job Grouping

Comparison of Scheduling algorithms with and without jobgrouping.

In the context of DAG scheduling, grouping of jobs into clusters

to reduce inter-job communication.

Job Grouping strategy, adaptive to run time environment

Job Grouping with PSO.


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Proposed System A noveljob grouping method using Parallel PSO

To reduce the communication overhead.

Enhance the speed of completion of processes.

Improve resource utilization.

Improve parallel efficiency.

Uses PPSO to select the resources to minimize the make span.

Trust level and dynamism of jobs is considered

Tool Used - Gridsim-4.2-beta.


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The Project aims at

Job Grouping based on trust Using PPSO

Parallel Job Submission

Enhancing Computation-communication Ratio

Reducing The Overall Processing Time Of Jobs Using

Parallelization

Improving Resource Utilization In The Grid Environment.

Trust based job filtering

Dynamic job submission


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Dynamically assemble

Using PPSO

Transmit to

Grid resources

job groups [ coarse grained ]

Filtered Jobs of an application [ fine grain] based on Trust

Grid resources Grid resources

In Parallel

1. Job Grouping

J b G i


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Total number of jobs

Average MI rate of job

MI deviation Percentage

Overhead processing time

Granularity time

Grid Resource

Grid resource 0

Grid resource 1

Grid resource N

Grid Resource File

User Input

GridletsGrid resources characteristics

Gridlet MI Resource MIPS Granularity time

Total MIPS

Grid resource 0

Gridlet group 0

Grid resource 1

Gridlet group 1

Grid resource 2

Gridlet group 2

Gridlet groups Resource IDs

..

Gridlet Scheduler

(1)

(3)

(4)

(5)

(6)

(7)(2)

Trust level

In parallel

Filter jobs

based ontrust

Job Grouping


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2. Trust Based Filtering of jobs

The Grid Information Service GIS gives the information

about all the trust level of the resources .

The user submits the jobs with different trust values.

From this, the jobs that have trust values greater thanthe resource's trust value are filtered out.

Trust aware resource management and scheduling offerQuality of Service at application layer in grid

environment.


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3. Particle Swarm Optimization If large numbers of tasks are computed on the

geographically distributed resources, a reasonable

scheduling approach must be adopted in order to

get the minimum completion time.

Task scheduling is a NP-Complete problem

Heuristic optimization algorithm can be used tosolve NP-complete problems.


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Particle Swarm Optimization (PSO) is an evolutionaryoptimization technique inspired by nature.

It simulates the process of a swarm of birds preying.

Its global searching ability can be used for neuralnetwork training, control system analysis and design,

structural optimization.

It also has fewer algorithm parameters than geneticalgorithm.

PSO algorithm works well on most global optimalproblems.


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PSO Concept

A swarm intelligence based algorithm finds a solution toan optimization problem in a search space.

Proposed solution exists in the form of a fitness function.

The swarm is typically modeled by particles inmultidimensional space that have a position and avelocity.

A Particle is a candidate solution in the population andrepresents a task.


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Particles fly through hyperspace .

An iterative process to improve candidate solutions is set in motion.The particles iteratively evaluate the fitness of the candidatesolutions.

Particles posses two essential reasoning capabilities Memory of their own best position and

knowledge of the global best of the swarm.

As the swarm iterates, the fitness of the global best solutionimproves.

All particles being influenced by the global best eventually approachthe global best. This phenomenon is called 'convergence'.


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PSO Algorithm Initialize parameters

Initialize population randomly

Initialize each particle position vector and velocity vector

Do {

Update each particles velocity and position;

Find a permutation according to the updated each particles position; Evaluate each particle and update the personal best and the global best;

Apply the local search;

} While (!Stop criterion)


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Parallel PSO

Recent advances in computer and network technologies led to parallel optimizationalgorithms.

Parallel PSO (parallel implementation of stochastic optimization alg)


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Parallel PSO design

Intialize

f(x) f(x) f(x)

Check Convergence

Update

# of particles

#

ofiteration

s


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Model for PPSO

Master

Slave Slave Slave

SEND GLOBAL VALUERECEIVE INDIVIDUAL

VALUE


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Gridlet Grouping

Scheduler

Trust based

filtered Gridletlist

Resource list

Call PPSO to assign Gridlet To Resources

Create new grouped Gridlet

With length= Total length

Assign to resources


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4. Dynamic jobs

Dynamic submission of jobs is considered. User can submit jobs when other jobs are being

processed.

The unused MIPS rating of the resources can beutilized in a efficient way such that grouping isdone by considering the unused MIPS as totalMIPS and the jobs are processed.

Then Parallel Submission of grouped Gridlets toresources is done


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Simulation Time for Job Grouping

using PSO vs. Parallel PSO

90

100

110

120

130

140

150

20 40 60 80

No of Gridlets

SimulationTim

PPSO

PSO


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Total number of processed gridlets for

different granularity time and resources

0

20

40

60

80

100

R 1 R 1-R 2 R 1-R 3 R 1-R 4 R 1-R 5

Resources

NoofGridletscom

pletedingran

time

10

20

30

40

50

d d b


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Load at resources during job grouping

with PPSO

0

100

200

300400

500

600

700

800

900

1000

R1 R2 R3 R4 R5

Resources

loa

50 gridlets

60 gridlets

70 gridlets

80 gridlets90 gridlets

Diff i b i i i f idl


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Difference in submission time of gridlets

with PSO and PPSO


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Add load balancing feature graph here


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Future Work

Future work would involve developing a more

comprehensive job grouping-based scheduling system that

takes into account QoS (Quality of Service) requirements of

each user job before performing the grouping method.

Resource utilization can be done according to the capacity

of the resource.


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THANK YOU

Documents

Task Sch PPSO