1. Job Scheduling in Grid Environment using
machine learning Algorithms
GUIDE: T R SWAPNA
JAYAKRISHNAN B
CB.EN.P2CSE12007
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2. Motivation
• The resource scheduling in grid is a NP complete problem
• The choice of the best pairs of jobs and resources cannot be
determined accurately.
• Only way it can do this is by past experience.
• This gives high scope for machine learning algorithms which
makes system learn from previous experiences
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3. Objective
• Minimize makespan of Grid System
• Makespan is used to measure the throughput of the grid
system.
• Makespan is the total completion time of a particular task on
a machine
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4. Alternative solutions
• Grid scheduling algorithms such as
Opportunistic load balancing (OLB)
Maximum Standard deviation heuristic
• ANT COLONY OPTIMIZATION(ACO)
[1]
• ACO is a population based search optimization technique
developed in the year 1997
• This algorithm simulates a colony of artificial ants that behave
as cooperative agents where they are allowed to search and
reinforce pathways (solutions) in order to find the optimal
ones.
• This approach which is population based has been successfully
applied to many NP-hard optimization problems.
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5. Algorithm
Step 1: Construct the ETC matrix
Step 2:Repeat steps 3 to 10
Step 3:Set all initial values
pheromone evapouration value ƿ = 0.5.
pheromone trail T0 = 0.01 (initial deposit)
Free(0 to m-1) = 0
k = any number of ants.
Step 4:For each ant do step 5 to 7.
Step 5:Select the <task,machine> pair randomly.
Step 6:Repeat following steps until all tasks are finished
(i) calcutate the heuristic function nhj.(0<h<i)
(ii) Assign higher probabilty to tasks that have high
standard deviation among tasks
(iii) calculate the probability matrix P for all machines m
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6. Select the next <task,machine> pair according to the
probablitiy matix P.
Step 7: Find the Best Solution from the solutions of all ants.
Step 8:Update the pheromone trail.
Step 9:Compare the previous sloution with the current solution
and save the better solution.
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7. Comparison of ACO with other
scheduling algorithms.
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8. Proposed solution
Avoid Local optimum problem.
Solution is to implement multiple Ant colonies .
Update the pheromone value taking average of all colonies.
Extending job onto Online Environment.
• Each job is charecterized by a set of attributes.
• A job can be classified by following attributes.
• Number of reads.
• Number of writes.
• Classify the jobs according to the attibutes into particular classes.
• Train the scheduler with the training data.
• The scheduler will classify the job to the machine which the
classifier have mapped onto.
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9. Feasibility study
• The jobs are classified onto appropriate machine using
read and write operations per job,using machine learning tool
WEKA.
• Using the excel based tool SOLVER ,training data set is given
as input .
•
Constraint is given as ∑min(makespan).
•
New testing data classfied automatically.
• Neural networks can also be used for classification.
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10. Conclusion
• Grid scheduling can be implemented within Polynomial time
by adopting machine learning algorithms.
• ACO algorithm performs better than traditional scheduling
algorithms.
• The scheduling can be extended onto an online environment
by applying suitable classification algorithms.
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11. References
• [1].Ant Colony System: A Cooperative Learning Approach to the
Salesman Problem , Marco Dorigo,IEEE 1997
Traveling
• [2].An Improved Ant Algorithm for Grid Scheduling Problem,
Bagherzadeh, Mojtaba MadadyarAdeh,IEEE 2009
Jamshid
• [3].Task Scheduling with Load Balancing using Multiple Ant Colonies Optimization
in Grid Computing, Liang Bai, Yan-Li Hu, Song-Yang Lao, Wei-Ming Zhang,2010
IEEE
• [4].A Task scheduling for grid scheduling using Ant colony Optimization,Jun
Mao,IEEE 2011
• [5].Evaluating Scheduling Algorithms on Distributed
Ryan J. Wisnesky
Computational Grids,
• [6] Improved job grouping based PSO algorithm for task scheduling in grid
computing,Sudha sadhasivam,IJEST 2010
• [7] Wikipedia-Particle Swarm optimization.
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