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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 Contents Introduction Load Distribution Strategies A QoS-Based Architecture Work in Progress Critique 2/15 Introduction(1/2) A practical approach to the provision of web services – Replicate Web servers(WSs) at distinct sites – Each client select the “most convenient” WS replica The success of this approach – To bind dynamically a client to the most convenient replica – To maintain data consistency among the WS replicas 3/15 Introduction(2/2) In this paper – Load distribution strategy • Mirror-based strategy • DNS-based strategy • QoS-based strategy – To minimize the URT(User Response Time) 4/15 Load Distribution Strategies Mirror-based strategy – The user manually selects a replica DNS-based strategy – “Ideal” round-robin assignment of clients to WS replicas QoS-based strategy – DNS : all addresses of replica WSs – Browser selects a replica with satisfactory URT by sending probe 5/15 Load Distribution Strategies - Performance Comparison Simulation scenario Area 1 Area 2 Web Server replica 1 Web Server replica 2 Area 1 Network Delay Area 2 Network Delay Area 4 Network Delay Area 3 Network Delay Web Server replica 4 Web Server replica 3 Browsers Area 4 Area 3 Internet Inter area network transfer delay Intra area network transfer delay Access line to a Web Server Simulation scenario 6/15 Load Distribution Strategies - Performance Comparison 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 query Independent and exponential distributed Each query Access a geometrically distributed number of pages Web page size Avg. 3000 bytes Dummy req. 1000 bytes Server capacity 200 request per second(FIFO queue) 7/15 Load Distribution Strategies - Performance Comparison Impact of intra-area network congestion Area 1 Routers Other Areas Routers 0.98 Util. Max. 0.8 Util. – Results • Utilization of each replica – QoS-based strategy : (0.58, 0.91, 0.92, 0.92) – Other strategies : uniformly 0.80 8/15 Load Distribution Strategies - Performance Comparison A heavily loaded area Area 1 User-Query Generation 0.98 of Server Capacity Other Areas 0.8 of ServerCapacity – Results 9/15 Load Distribution Strategies - Performance Comparison Symmetric case – All Areas • The most congested router : 0.80 utilization • The user-query generation rate : 0.80 of server capacity – Results 10/15 Load Distribution Strategies - Performance Comparison A realistic scenario – Four distinct areas • USA, Europe, Asia, Australia – Daily different loads in different periods of time – Results 11/15 A QoS-Based Architecture Do not require modification of any software Architecture 12/15 A QoS-Based Architecture DNS DNS DNS Request DNS Request All Replica’s IP Address Replicated Server 1 One Replica’s IP Address Replicated Server 1 Replicated Server 2 Probe Reply Replicated Server N Browser Replicated Server 2 ... Probe Request ... Browser Broadcast Poll Request Poll Reply Replicated Server N Drawback – URT estimation : Single measure – Polling overhead 13/15 Work in Progress 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 14/15 Critiques Contribution in this paper – QoS-based approach: Minimize URT – Load distribution considering network delay Simulation with realistic workload Not Scalable More research on LD – How to evaluate the accurate WS response time 15/15 16/15