Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Like this presentation? Why not share!

- Hill Climbing and Genetic Algorithm by Ahmad Yasir Ahmad 163 views
- Energy-aware Task Scheduling using ... by Linda J 1132 views
- final by Moustafa Najm 70 views
- 11. grid scheduling and resource ma... by Dr Sandeep Kumar ... 2047 views
- Optimization Heuristics by Kausal Malladi 5992 views
- Ant colony optimization by Meenakshi Devi 16416 views

2,314 views

Published on

No Downloads

Total views

2,314

On SlideShare

0

From Embeds

0

Number of Embeds

5

Shares

0

Downloads

78

Comments

0

Likes

1

No embeds

No notes for slide

- 1. Job Scheduling in Grid Environment using machine learning Algorithms GUIDE: T R SWAPNA JAYAKRISHNAN B CB.EN.P2CSE12007 1
- 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. 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. 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. 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. 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. Comparison of ACO with other scheduling algorithms. 7
- 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. 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. 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. 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

No public clipboards found for this slide

×
### Save the most important slides with Clipping

Clipping is a handy way to collect and organize the most important slides from a presentation. You can keep your great finds in clipboards organized around topics.

Be the first to comment