Web server load balancing: A queueing analysis

Over the last few years, the Web-based services, more specifically different types of E-Commerce applications, have become quite popular, resulting in exponential growth in the Web traffic. In many situations, this has led to unacceptable response times and unavailability of services, thereby drivin...

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Bibliographic Details
Published inEuropean journal of operational research Vol. 186; no. 2; pp. 681 - 693
Main Authors Zhang, Zhongju, Fan, Weiguo
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 16.04.2008
Elsevier
Elsevier Sequoia S.A
SeriesEuropean Journal of Operational Research
Subjects
Applied sciences
Electronic commerce
Exact sciences and technology
Load balancing
Operational research and scientific management
Operational research. Management science
Parallel queues
Queueing theory
Queuing theory
Queuing theory. Traffic theory
Routing
Servers
Studies
Routing
Load balancing
Queueing theory
Parallel queues
Distributed load
Performance characteristic
Optimal policy
Web service
Modeling
Rejection
Response time
User interface
Time average
World wide web
Parallelism
Electronic trade
Internet
Two server queue
Queue
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Summary:Over the last few years, the Web-based services, more specifically different types of E-Commerce applications, have become quite popular, resulting in exponential growth in the Web traffic. In many situations, this has led to unacceptable response times and unavailability of services, thereby driving away customers. Many companies are trying to address this problem using multiple Web servers with a front-end load balancer. Load balancing has been found to provide an effective and scalable way of managing the ever-increasing Web traffic. However, there has been little attempt to analyze the performance characteristics of a system that uses a load balancer. This paper presents a queuing model for analyzing load balancing with two Web servers. We first analyze the centralized load balancing model, derive the average response time and the rejection rate, and compare three different routing policies at the load balancer. We then extend our analysis to the distributed load balancing and find the optimal routing policy that minimizes the average response time.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2007.02.011