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Zeng Zeng, Bharadwaj, IEEE TRASACTION ON COMPUTERS, VOL. 55, NO. 11, NOVEMBER 2006

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. Outline. Introduction

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Zeng Zeng, Bharadwaj, IEEE TRASACTION ON COMPUTERS, VOL. 55, NO. 11, NOVEMBER 2006

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  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 Ts ,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, Te. • 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 Ts :System loading is light.

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

  24. Performance Evaluation and Discussions (cont.) • Effect of Ts :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 Ts. • When the system loads are fairly high, QAP policy and QRAP policy have better performance than RAP policy.

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