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

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

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

4/15 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)

5/15 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

6/15 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

7/15 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 bytes Dummy req.1000 bytes Server capacity200 request per second(FIFO queue)

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.

9/15 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

10/15 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

11/15 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

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

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)

14/15 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 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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