1. Name : Mamatha J M Seminar guide: Mr. Kemparaju

2. GRID COMPUTING

3. Abstract Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. A grid can be thought as a distributed system with non-interactive workloads that involve a large number of files. BACO algorithm stimulates the behavior of ants, used for job scheduling in grids.

4. Pheromone When the ants move, they leave a trace of pheromone on the path. The ants following the path can easily find their way on that path due to the pheromone trace. The more ants move on the same path, the stronger will be the pheromone trace.

5. ACO - ant colony optimization A good schedule would adjust its scheduling strategy according to the changing status of the entire environment and the types of jobs. Therefore, a dynamic algorithm in job scheduling such as ant colony optimization is appropriate for grids. ACO initiates the behavior of real ants in nature to find food and connect each other by pheromone laid paths. We assume each job is an ant and the algorithm sends the ants to search for resources.

6. Ant algorithms ACS (ant colony system) MMAS(max-min ant system) RAS (rank-based ant system) FANT (fast ant system) EAS (elitist ant system)

7. Job scheduling methods FPLTF (fastest processor to largest task) WQR (work queue with replication) Min-min RR (round robin) Priority scheduling algorithm FCFS (first come first serve)

8. BACO – balanced ant colony optimization system architecture

9. BACO algorithm An ant in the ant system is a job in the grid system. Pheromone value on a path in the ant system is a weight for a resource in the grid system. A resource with larger weight value means that the resource has a better computing power. The pheromone of each resource is stored in the scheduler and the scheduler uses it as the parameters for BACO algorithm.

10. The resource with the higher weight(pheromone) in assigned the job.

11. yes no Flow chart

12. Pheromone indicator PI= transmission time + execution time. PI ij : pheromone indicator for job j assigned to resource i. M j : size of the given job j. Bandwidth i : bandwidth available between scheduler and the resource. T j : CPU time needed to job j. CPU_speed i : CPU speed. Load i : current load.

13. m resources and n jobs. We select the largest entry from the matrix. Local pheromone update. Global pheromone update.

14. Example: Assume there are three jobs j1, j2, j3 and three resources r1, r2, r3 in the grid. The initial status of each resource is shown in the table below.

15. Global update

16. The layer of Grid Simulation architecture

17. An event diagram for interaction between the jobs and the resources entities

18. TRAVELLING SALESMEN PROBLEM

19. Graph (N,E): where N = cities/nodes, E = edges = the tour cost from city i to city j (edge weight) Ant move from one city i to the next j with some transition probability Ant Systems for TSP

22. Exploration: each of the edges in proportion to its value Exploitation: the best edge is chosen Next city is chosen between the not visited cities according to a probabilistic rule

23. Implementation environment CPU speed of each resource

24. Implementation client portal scheduler

25. Experimental results for matrix with same size average execution time per job for matrix multiplication average execution time per job for linear programming

26. = standard deviation. = no. of resources. = load of resource i. = average load of all resources. standard deviation of load for matrix multiplication standard deviation of load for linear programming

27. Experimental results for matrix with mixed sizes makespan of each method with mixed sizes for matrix multiplication makespan of each method with mixed sizes for linear programming

28. standard deviation of load of each method with mixed size for matrix multiplication Standard deviation of load of each method with mixed sizes for linear programming

29. Conclusion The local and global update functions offer the job scheduler with the newest information of all resources for the next job assignment. The experimental results shows that BACO is capable of balancing the entire system load.