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Adaptive Load Sharing for Clustered Digital Library Servers Song, yong-joo System Software Lab, EECS, KAIST 9. 13, 2000.

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Presentation on theme: "Adaptive Load Sharing for Clustered Digital Library Servers Song, yong-joo System Software Lab, EECS, KAIST 9. 13, 2000."— Presentation transcript:

1 Adaptive Load Sharing for Clustered Digital Library Servers Song, yong-joo System Software Lab, EECS, KAIST 9. 13, 2000

2 Contents Introduction Previous Work Clustered ADL server design Load sharing strategy Policy for node selection & load collection Experiment Conclusion & Critique

3 3 Introduction Alexandria digital library Geographically-referenced materials Map, satellite image, digitized aerial photograph Large data Require extensive processing Goal : minimize response time

4 4 Introduction(cont ’ d) Basic ADL Clustered ADL

5 5

6 6 Previous Work Clustered Web server design DNS round-robin rotation Problem : load imbalance SWEB Redirecting request by dynamic system load prediction of response time

7 7 SWEB DNS round-robin rotation Redirect request to minimum expected response time server CPU, I/O load monitoring prediction of DB searching time load information update overhead

8 8 Load Sharing strategy Class of request Text & image ( CPU & I/O usage small ) DB search ( CPU & I/O usage maybe large ) Image processing ( predictable ) Relative Server-site Response Cost Availability relative to the unloaded base node redirection threshold value

9 9 Policy for load collection Broadcast – redundant info, load explosion Random poll – synchronization overhead Proposed Random multicast A hybrid approach Random multicast + Random poll

10 10 Experiment Server 6 of 147Mhz 2 of dual 167Mhz Multi-node performance Comparison of load collection policies Effectiveness of RSRC func.

11 11 Conclusion Study adaptive load balancing strategy Consider both I/O & CPU load factor On non-uniform cluster Hybrid node selection & collection policy

12 12 Critique SWEB does not consider non-uniform cluster? RSRC correctness Parameter, threshold value decision Multicast overhead How about Broadcast & selective catch


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