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# JOB SCHEDULING USING ANT COLONY OPTIMIZATION ALGORITHM

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### JOB SCHEDULING USING ANT COLONY OPTIMIZATION ALGORITHM

1. 1. Job Scheduling in Grid Environment using machine learning Algorithms GUIDE: T R SWAPNA JAYAKRISHNAN B CB.EN.P2CSE12007 1
2. 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 2
3. 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 3
4. 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. 4
5. 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 5
6. 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. 6
7. 7. Comparison of ACO with other scheduling algorithms. 7
8. 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. 8
9. 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. 9
10. 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. 10
11. 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. 11