1. Grid Load Balancing Scheduling Algorithm Based on Statistics Thinking The 9th International Conference for Young Computer Scientists Bin Lu, Hongbin Zhang Present by Chen, Ting-Wei

2. 2 Table of content Introduction Hierarchical Grid system model Grid resource allocation and task scheduling Simulative experiments Conclusions

3. 3 Introduction Propose a new task scheduling and resource allocation algorithm –Load tracking module –Job distributing module –Load monitoring module Simulate the algorithm

4. 4 Introduction (cont.) Based on the hierarchical Grid structure model –Distribution of Grid resources –The strategy of task scheduling –Following statistical thinking

5. 5 Hierarchical Grid system model

6. 6 Grid resource allocation and task scheduling Load (L x ) Length of CPU Ready queue CPU Utilization Light Load L x L x < L low ShortLow Heavy Load L x L x >L high LongHigh Moderate Load L x L low < L x < L high Normal Definition of the Load

7. 7 Grid resource allocation and task scheduling (cont.) Algorithm analysis and implementation –Three components Load tracing module Job distributing module Load monitor module

8. 8 Grid resource allocation and task scheduling (cont.) –Three tables are installed on the database server in GRM and DRM The first: Every sub-DRM load information The second: The number of tasks being operated The third: The throughput and the average delayed response

9. 9 Grid resource allocation and task scheduling (cont.) Load tracing module –Get the load information of DRM –Monitor periodically the implementation state of operations –Report it to the upper level for the overall management and scheduling

10. 10 Grid resource allocation and task scheduling (cont.) Job distributing module –According to load balancing –GRM chooses a suitable DRM to carry out job distributing –Consider CPU utilization Memory usage –The weight of load indicators can be determined statically or dynamically

11. 11 Grid resource allocation and task scheduling (cont.) –Load computing formula of the CN –The average of the sub-DRM’s load or CN’s load –Variance of load

12. 12 Grid resource allocation and task scheduling (cont.) –Job algorithm Initialize; For (;;) {do If (There was information form the DRMs or CNs) { Updating the load information of DRM or CNs according to the formula (1)-(3); Calculating the rate of throughput; Calculating the average response delay; If (There was information of completed assignment) { Deducting one assignment of corresponding DRM; }

13. 13 Grid resource allocation and task scheduling (cont.) If (Portal gives the assignment to GRM) { If (The node having the load is not unique) { ; Making the node whose variance is as the scheduling nodes, if still not unique, selecting the first node; } Else { Making the node whose load value is be the scheduling node; } Giving it the assignment for scheduling, and updating the number of assignment of DRM; }

14. 14 Grid resource allocation and task scheduling (cont.) Load monitor module –Surveillance The current load of all the DRMs

15. 15 Grid resource allocation and task scheduling (cont.) For (;;) { If (sum < ) { The opening of stand-by resources manager; } Reading the load vector of domain resources manager; Reading the upper and lower threshold provided in profile; If (Every load exceeds the threshold ) { Opening the stand-by resources manager; } If (Every load is below the threshold ) { Removing stand-by resources manager; }

16. 16 Grid resource allocation and task scheduling (cont.) Else { Get the last updated time of the node If (The current time – last updated time) > (timeout) Means that the main resource manager failed and it takes proper measures; }

17. 17 Simulative experiments Use Gridsim to simulate Compare performance of the load balancing scheduling method –Min-Min algorithm –Ant algorithm –This algorithm (This paper proposed)

18. 18 Simulative experiments (cont.) Configuration of experiment environment

19. 19 Simulative experiments (cont.) Comparison of experiment results

20. 20 Conclusions Statistical load balancing scheduling scheme Future work –Granular computing idea to improve this algorithm –More the flexibility and efficiency of this algorithm

21. Thank you for your attention