A Test-bed for Performance Evaluation of Load Balancing Strategies for Web Server
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A Test-bed for Performance Evaluation of Load Balancing Strategies for Web Server Systems

Many large web sites get more than 100 million hits everyday. They need a scalable web server system that can provide better performance to all the clients that may be in different geographical regions. Higher delays and losses are common on WAN links. To provide a better service to all the clients, it is natural to have fully replicated web server clusters in different geographical regions. In such an environment, one of the most important issue is that of server selection (and load balancing). The client's request should be directed to one of the servers in a way that the response can be quick. We assume that web servers are functionally homogeneous, i.e., any one of them can serve any client request. Another important point is that this system should not require modification of any client side component or existing standard protocol.

In this thesis, we have developed a test bed to emulate the world wide web environment and compare different schemes. A large number of systems have been proposed to do this load balancing. We also propose a new scheme which is based on estimating the round trip time between the client and various server clusters. The proposed scheme is shown (through emulation) to perform significantly better than many of the existing scheme.
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Introduction
1.1 Motivation

Number of users accessing the Internet is increasing quite rapidly and it is common to have more than 100 million hits a day for popular web sites. For example, netscape.com website receives more than 120 million hits a day. The number of users is expected to continue increasing at a fast rate and hence any website that is popular, faces the challenge of serving very large number of clients with good performance. Full mirroring of web servers or replication of web sites is one way to deal with increasing number of requests. Many techniques exist for selection of nearest web server from the client's point of view. Ideally, selection of best server should be done transparently without the intervention of the user.
Many of the existing schemes do only load-balancing. These schemes assume that the replicated site has all the web servers in one cluster. This is alright for medium sized sites, but beyond a certain amount of traffic, the connectivity to this one cluster becomes a bottleneck. So large web sites have multiple clusters, and it is best to have these clusters geographically distributed. This changes the problem to first select the nearest cluster and then do load balancing within the servers of that cluster. Of course, if all servers in a cluster are heavily loaded then another cluster should have been chosen. So the problem is more complex in such an environment. Designing such system involves making decisions about how best server is selected for a request such that user receives response of request in minimum time and how this request is directed to that server. In most strategies, a server is selected without taking into account any system state information, e.g. random, round robin etc. Some policies use weighted capacity algorithms to direct more percentage of requests to more capable servers. But few strategies select a server based on the server state and very few strategies take client state information into account. There is always a tradeoff between the overhead due to collection of system state information and performance gain by use of available state information. If too much state information (of server or clients) is collected, it may result in high overheads for collection of information and performance gain may not be comparable to overheads. So we must carefully collect only that state information that might improve performance of system as seen by clients but do not result in very high overheads.
In this thesis, we have proposed a new scheme based on collecting information about the load on each server as well as estimating round-trip time between clusters and those clients which make large number of requests.
To study the tradeoffs and impact of different parameters on a web server system, a framework is required. The framework should enable evaluation and comparison of performance of distributed web server systems. The framework should allow easy implementation of any scheme and analyze the performance of web server system with new policies.
In this thesis, we have designed and implemented a test bed to provide such a framework. We have also measured performance of few policies implemented in this test bed through emulation of world wide web scenario,
1.2 Steps in HTTP request service
Before we discuss further, it is important to understand how a HTTP request is serviced, so it is briefly discussed here, A client's request for desired object is fulfilled in following steps:
• Domain name to IP address mapping : The domain name present in URL must first be translated to an IP address. The client software requests its local
resolver for it, if this mapping is not in its cache. The resolver in turn returns the IP address for that domain name, that it may get from Intermediate name servers (which may have cached this mapping) or from directly from authorized DNS for that domain name either recursively or iterativelv. More details about DNS mechanism can be found in RFC 1034 [26] and RFC 1035 [27],
• Request for object to server with that IP address: Then client software sends request for object to server having that IP address. The server may return requested object directly or it may redirect it to other server using HTTP header options or fetch the object from other server and deliver to client or may transparently forward the request to other server which replies directly to client with address of forwarding server, etc.
Thus HTTP request service path allows us to distribute requests at two levels, first at DNS at the time of resolution of domain name to server IP address, and the other at server when request reaches at that server. Any system consisting of multiple servers and some request distribution mechanism is termed Distributed Web Server System (DWSS),
Time taken for service of any HTTP request submitted by client depends on two major factors namely network conditions and server load. Even if there is a capable server system present, but the connectivity of client in terms of delay, available bandwidth or packet loss is not good, it will sees large delays. If server system is saturated with requests, time taken for service is very large. So for keeping response time minimum, web server system should take into account both the factors,
1.3 Outline
In chapter 2, we first present a brief survey of existing approaches for request dis¬tribution mechanisms. In chapter 3, design goals for system, system model taken, approach used and algorithms for each server side component of proposed architec¬ture are discussed.
To evaluate the performance of proposed architecture and compare it with other existing proposals, a flexible test bed was designed to emulate real Internet like scenario in which various architectures for Distributed Web Server System can be emulated with minimal efforts. In chapter 4, design goals, overview and different components of this test bed are described. In chapter 5, different algorithms imple¬mented on the test bed and measured performance are briefly discussed and finally the performance results obtained for various schemes are compared. In chapter 6, we finally present conclusion and future extensions. In appendix, we give short description of softwares used by us.
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