1. Load Distribution among Replicated Web Servers: A QoS-based Approach Marco Conti, Enrico Gregori, Fabio Panzieri WISP99 2000.9.14 KAIST EECSD CALab Hwang In-Chul

2. 2/15 Contents n Introduction n Load Distribution Strategies n A QoS-Based Architecture n Work in Progress n Critique

3. 3/15 Introduction(1/2) n A practical approach to the provision of web services –Replicate Web servers(WSs) at distinct sites –Each client select the “most convenient” WS replica n The success of this approach –To bind dynamically a client to the most convenient replica –To maintain data consistency among the WS replicas

4. 4/15 Introduction(2/2) n In this paper –Load distribution strategy Mirror-based strategy DNS-based strategy QoS-based strategy –To minimize the URT(User Response Time)

5. 5/15 Load Distribution Strategies n Mirror-based strategy –The user manually selects a replica n DNS-based strategy –“Ideal” round-robin assignment of clients to WS replicas n QoS-based strategy –DNS : all addresses of replica WSs –Browser selects a replica with satisfactory URT by sending probe

6. 6/15 Load Distribution Strategies - Performance Comparison n Simulation scenario Inter area network transfer delay Intra area network transfer delay Area 1 Area 4Area 3 Area 2 Area 1 Network Delay Area 4 Network Delay Area 3 Network Delay Area 2 Network Delay Web Server replica 1 Web Server replica 2 Web Server replica 3 Web Server replica 4 Internet Browsers Access line to a Web Server Simulation scenario

7. 7/15 Load Distribution Strategies - Performance Comparison n Simulation environment –Network delay model Intra-area delays –The minimum area round trip time –The queuing delays in the area router –The packet transmission time Inter-area delays –Random variables –Other factors Consecutive queryIndependent and exponential distributed Each queryAccess a geometrically distributed number of pages Web page sizeAvg. 3000 bytes Dummy req.1000 bytes Server capacity200 request per second(FIFO queue)

8. 8/15 Load Distribution Strategies - Performance Comparison n Impact of intra-area network congestion –Results Utilization of each replica –QoS-based strategy : (0.58, 0.91, 0.92, 0.92) –Other strategies : uniformly 0.80 Area 1 Routers0.98 Util. Other Areas RoutersMax. 0.8 Util.

9. 9/15 Load Distribution Strategies - Performance Comparison n A heavily loaded area –Results Area 1 User-Query Generation0.98 of Server Capacity Other Areas0.8 of ServerCapacity

10. 10/15 Load Distribution Strategies - Performance Comparison n Symmetric case –All Areas The most congested router : 0.80 utilization The user-query generation rate : 0.80 of server capacity –Results

11. 11/15 Load Distribution Strategies - Performance Comparison n A realistic scenario –Four distinct areas USA, Europe, Asia, Australia –Daily different loads in different periods of time –Results

12. 12/15 A QoS-Based Architecture n Do not require modification of any software n Architecture

13. 13/15 A QoS-Based Architecture n Drawback –URT estimation : Single measure –Polling overhead DNS Replicated Server 1 Replicated Server 2 Replicated Server N... Browser DNS Request All Replica’s IP Address Probe Request DNS Replicated Server 1 Replicated Server 2 Replicated Server N... Browser DNS Request One Replica’s IP Address Probe Reply Broadcast Poll Request Poll Reply Poll Request Poll Reply (All Replica’s IP Address)

14. 14/15 Work in Progress n Load Distribution(LD) service –To overcome the main limitations –Responsible for distributing the browsers’ requests –Maintain for each WS replica the WS response time –Continuous monitoring of the response time

15. 15/15 Critiques n Contribution in this paper –QoS-based approach: Minimize URT –Load distribution considering network delay n Simulation with realistic workload n Not Scalable n More research on LD –How to evaluate the accurate WS response time

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