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

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Contents

Introduction Load Distribution Strategies A QoS-Based Architecture Work in Progress Critique

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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

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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)

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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

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Load Distribution Strategies - Performance Comparison

Simulation scenario

Inter area network transfer delay

Intra area network transfer delay

Area 1

Area 4 Area 3

Area 2

Area 1Network

Delay

Area 4Network

Delay

Area 3Network

Delay

Area 2Network

Delay

Web Serverreplica 1

Web Serverreplica 2

Web Serverreplica 3

Web Serverreplica 4

Internet

Browsers

Access line to a Web Server

Simulation scenario

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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 factorsConsecutive 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)

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Load Distribution Strategies - Performance Comparison 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 Routers 0.98 Util.Other Areas Routers Max. 0.8 Util.

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Load Distribution Strategies - Performance Comparison

A heavily loaded area

– Results

Area 1 User-Query Generation 0.98 of Server CapacityOther Areas 0.8 of ServerCapacity

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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

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Load Distribution Strategies - Performance Comparison

A realistic scenario– Four distinct areas

• USA, Europe, Asia, Australia

– Daily different loads in different periods of time

– Results

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A QoS-Based Architecture

Do not require modification of any software Architecture

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A QoS-Based Architecture

Drawback– URT estimation : Single measure

– Polling overhead

DNS

ReplicatedServer 1

ReplicatedServer 2

ReplicatedServer N

...

Browser

DNS Request All Replica’sIP Address

Probe Request

DNS

ReplicatedServer 1

ReplicatedServer 2

ReplicatedServer N

...

Browser

DNS Request One Replica’sIP Address

Probe Reply

Broadcast Poll Request

Poll Reply

Poll Request

Poll Reply

(All Replica’s IP Address)

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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

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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

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