1. Design and Performance Evaluation of Queue-and-Rate-Adjustment Dynamic Load Balancing Policies for Distributed Networks Zeng Zeng, Bharadwaj, IEEE TRASACTION ON COMPUTERS, VOL. 55, NO. 11, NOVEMBER 2006 Presented by 張肇烜

2. Outline Introduction Classification of Dynamic Load Balancing Algorithms Comparative Study on the Algorithms Performance Evaluation and Discussions Extension to Large Scale Cluster Systems Conclusions

3. Introduction Centralized dynamic load balancing. Scheduler can handle most of the communication and computation overheads efficiently.

4. Introduction (cont.) Distributed dynamic load balancing. More advantages, such as scalability, flexibility, and reliability.

5. Introduction (cont.) A distributed computer system.

6. Introduction (cont.) In this paper, we classify the dynamic distributed load balancing algorithms for heterogenous distributed computer systems into three policies: Queue Adjustment Policy (QAP) Rate Adjustment Policy (RAP) Queue and Rate Adjustment Policy (QRAP)

7. Introduction (cont.) QAP: Estimated Load Information Scheduling Algorithm (ELISA). Perfect Information Algorithm (PIA). RAP: Rate-based Load Balancing via Virtual Routing (RLBVR).

8. Introduction (cont.) QRAP: Queue-based Load Balancing via Virtual Routing (QLBVR).

9. Classification of Dynamic Load Balancing Algorithms Queue Adjustment Policy:

10. Classification of Dynamic Load Balancing Algorithms (cont.) Rate Adjustment Policy:

11. Classification of Dynamic Load Balancing Algorithms (cont.) Queue and Rate Adjustment Policy:

12. Comparative Study on the Algorithms In distributed dynamic load balancing algorithms, the nodes in the system exchange their status information at a periodic interval of time T s,which is called the status exchange interval. The instant at which this information exchange takes place is called a status exchange epoch.

13. Comparative Study on the Algorithms (cont.) Each status exchange interval is further divided into equal subintervals denoted as estimation intervals, T e. The points of division are called estimation epochs.

14. Comparative Study on the Algorithms (cont.) Intervals of estimation and status exchange.

15. Comparative Study on the Algorithms (cont.) ELISA: Each node computes the average load on itself and its neighboring nodes. Nodes in the neighboring set whose estimated queue length is less than the estimated average queue length by more than a threshold θ form an active set.

16. Comparative Study on the Algorithms (cont.) ELISA: The node under consideration transfers jobs to the nodes in the active set until its queue length is not greater than θ and more than the estimated average queue length.

17. Comparative Study on the Algorithms (cont.) RLBVR:

18. Comparative Study on the Algorithms (cont.) QLBVR caries out coarse adjustment on job transferring and processing rates and fine adjustment on queue length. Coarse adjustment (on transfer and processing rates). Fine adjustment (on queue lengths).

19. Comparative Study on the Algorithms (cont.) QLBVR: When the job incoming rates change slightly, coarse adjustment can work well. When the system load is very high and job incoming rates change rapidly, fine adjustment can balance the queue lengths in a short time.

20. Performance Evaluation and Discussions Effect of system loading:

21. Performance Evaluation and Discussions (cont.) When the load of the system is light or moderate, RLBVR and QLBVR have a better performance than ELISA. When the rate of jobs becomes high, ELISA and QLBVR have a much better performance than RLBVR.

22. Performance Evaluation and Discussions (cont.) Effect of T s :System loading is light.

23. Performance Evaluation and Discussions (cont.) Effect of T s :System loading is moderate.

24. Performance Evaluation and Discussions (cont.) Effect of T s :System loading is moderate.

25. Extension to Large Scale Cluster Systems The mesh-connected cluster system.

26. Extension to Large Scale Cluster Systems (cont.) Mean response time of jobs for five different algorithms under different system utilization. System utilization is light or moderate. System utilization is high.

27. Extension to Large Scale Cluster Systems (cont.) System utilization is light or moderate.

28. Extension to Large Scale Cluster Systems (cont.) System utilization is high.

29. Extension to Large Scale Cluster Systems (cont.) Experiments when the arrival of loads is varying rapidly.

30. Extension to Large Scale Cluster Systems (cont.)

31. Conclusion With our rigorous experiments, we have shown that, when the system loads are light or moderate, algorithms of the Rap policy are preferable with longer T s. When the system loads are fairly high, QAP policy and QRAP policy have better performance than RAP policy.