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GLOA:A New Job Scheduling Algorithm for Grid Computing
The document presents GLOA, a new job scheduling algorithm for grid computing aimed at optimizing resource allocation by minimizing computation time and makespans. It discusses the problem space, simulation results, and the algorithm's approach, which involves leveraging social dynamics within groups to escape local minima. The conclusion emphasizes GLOA's potential real-world applications and reduced overhead on resources.
GLOA:A New Job Scheduling Algorithm for Grid Computing
- 1. OSLab GLOA: A NewJob Scheduling Algorithm for Grid Computing Julie Kim kjulee114@gmail.com 2013-1st AI System Design
- 2. Table of Contents • • • • • • OSLab Gridcomputing The significant problem How to solve it? The new approach, GLOA Result of simulation Conclusion 2
- 3. Grid Computing OSLab Federation ofcomputer resources from multiple locations to reach a common goal http://www.gridcafe.org/nav/WhatIsGrid.jpg 3
- 4. Grid Computing OSLab Variety Typeof Applications Multiple Resources http://www.digipede.net/images/digipede-overview.gif 4
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- 9. GLOA Problem Space OSLab Effect of SocialLeaders Problem Space 9 http://thecollaboratory.wdfiles.com/local--files/2012-sociology/Social%20groups(1).jpg
- 10. Group Leader OSLab • Thebest member • Members try to become similar • Find solution space • Randomly interchanged some variables between groups – Come out of local minima 10 http://www.empiremarketing.ca/reportuploads/1316465877-Leading_by_example_SEO.png
- 11. Steps OSLab • Initial PopulationProduction – P members * n groups • Calculating Fitness Values of All group Members – Fitness(member_i)=1/makespan(member_i) • Determine Leader – The most fitness member 11
- 12. GLOA OSLab Solution space N groups Pmembers 12
- 13. Steps OSLab • Mutation Operator –new =r1 *old+r2 *leader +r3 *random – If it is better, replace old • One-way Crossover Operator – Some parameters values are replaced with another values of another group – To escape local minima • Repetition 13
- 14. Simulation OSLab • JRE • 2.66GHz cpu, 4GB memory 14
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- 16. Conclusion OSLab • Purpose ofthe Grid Computing – Make Common Resources available to a central computer – computational power, bandwidth, and databases • GLOA – – – – scheduling tasks/jobs in a computational grid wasting less computation time produce shortest makespans could be applied in the real world • Less overhead on resources 16
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- 19. References OSLab • Pooranian, Z.,M. Shojafar, J. H. Abawajy, and M. Singhal. GLOA: A New Job Scheduling Algorithm for Grid Computing. IJIMAI(2013.03), p.59-64 • http://casd.csie.ncku.edu.tw/meeting/n1/20121214/CloudDLS%20Dynamic%20trusted%20scheduling%20for%20Cloud %20computing.pdf • http://en.wikipedia.org/wiki/Deterministic_algorithm • http://en.wikipedia.org/wiki/Simulated_annealing • http://en.wikipedia.org/wiki/Particle_swarm_optimizationhttp ://www.inf.ucv.cl/~bcrawford/Cuesta_Olivares/NuevasMetahe uristicas/1-s2.0-S0020025509001200-main.pdf • http://www.ise.ncsu.edu/fangroup/ie789.dir/IE789F_tabu.pdf 19