task sch ppso
TRANSCRIPT
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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