Dynamic Web Workload Distribution Test from 0 Rps to 1000 Rps on

Cluster-based Web Server System with Locality-based

Least Connection Algorithm

Nongki Angsar, Maria D. Badjowawo and Marthen Dangu Elu Beily

Electrical Engineering Department, State Polytechnic of Kupang, Kupang, Indonesia

Keywords:

Distribution Test, Web Server, Cluster.

Abstract: The growth of web traffic and network bandwidth which is quicker than the growth of microprocessor these

days cause single server platform no longer be adequate to fulfill the requirement of web server system

scalability. Plural server platform is the answer. One of solutions which have been recognized is cluster-based

web server system. This research did some dynamic web workload distribution tests on a cluster-based web

server system by generating HTTP workloads dynamicly, with continuous changing HTTP request rate from

0 request per second (rps) to 1000 rps, from client to web server system pool. In this research, result of

dynamicly testing with continuous changing HTTP request rate from 0 rps to 1000 rps shows that HTTP

requests were well-distributed to web server system pool by Locality-Based Least Connection Algorithm.

HTTP reply rate, TCP connection rate, and throughput tend to increase linearly with the increase in HTTP

request rate. While response time and error almost equal to zero with the increase of HTTP request rate.

Correlation between linearity and the zero of error is, at the point 0 rps to 1000 rps, almost all of HTTP

requests were replied by the pool of servers.

1 INTRODUCTION

Along with the complexity of web service and

application in so many areas, hence web service

request from user become progressively high.

Example of popular web services and applications are

business service and application (e-business),

education (e-learning), news (e-news), and others.

Also with the growth of network infrastructure

and computer communication become progressively

good in recent years. Application of optical fibre on

cables (Roger, 1998), Gigabit Ethernet on LAN

(William, 2000), broadband- ISDN on WAN

(William, 2000), xDSL digital transmission on

telephone line (William, 2000), and cable modem

make network bandwidth become bigger. Even a

prediction which is

made by George Gilder in 1995

said that the growth of network bandwidth will be

multiply thrice every year (Gray, 2000). This

prediction still go into effect, special for the optical

fibre, refers to article made in 2008 (Gilder, 2008).

On the other side, computer growth (sum of

transistors in a microprocessor chip), according to the

prediction of Intel founder, Gordon Moore in 1960

will only be multiply twice every 18 months (Intel,

2003). This

prediction have been proven through years untill now,

and usually referred as Moore’s Law.

According to these two predictions, the network

bandwidth growth will be multiply twice than

computer growth, and the possible bottle-neck will

lay in server side.

2 LITERATURE REVIEW

According to Cardellini et al (Valeria, 2001), there

are two efforts which can be done: (1) scale-up effort

(single

platform server and (2) scale-out effort

(plural

platform server). First effort is good enough,

however having some weakness. First, requiring big

expense

to keep pace with recent technology. Second,

can not

eliminate the fact that single point of failure

(SPOF) is on server itself. Third, availability and

continuity will be disturbed at the time of server

scalability improvement. Fourth, replacement to new

hardware

cause old hardware tends to be useless in

system.

While second effort, on the contrary, cheaper

Angsar, N., Badjowawo, M. and Beily, M.

Dynamic Web Workload Distribution Test from 0 Rps to 1000 Rps on Cluster-based Web Server System with Locality-based Least Connection Algorithm.

DOI: 10.5220/0010958700003260

In Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2021), pages 1049-1053

ISBN: 978-989-758-615-6; ISSN: 2975-8246

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

1049

and do not own SPOF. One of the popular plural web

server system is cluster-based web server system.

3 BASIC THEORY

A cluster-based web server system is a set of

heterogeneous web server that work under

coordination of load balancer to serve HTTP request

from client. Web server cluster is visible from client

as one single system with one domain name and IP

address.

This system consist of (Valeria, 2001):

Load Balancer, is a digital device which

intentionally be placed at 7

or 4

layer of

ISO/OSI to share workload among servers.

Server Pool, is a cluster of real-servers which

doing real service, such as: web, ftp, e-mail.

Back-end Server, is backside system which save

service data and content from server, such as:

database and NFS.

Figure 1: Cluster-based Web Server System

Architecture.

There are two main function of load balancer in

cluster-based web server system, those are: routing

function (which realized in routing mechanism) and

delivery function (which realized in dispatching

algorithm.

Routing Mechanism

Routing mechanism functioning to package and

direct client request to a real-server. Routing

mechanism which is used in this paper is Network

Address Translation (NAT).

Dispatching Algorithm

Dispatching algorithm functioning to choose a

real-

server to reply client request (Shivaratri, 1992).

Dispatching

algorithm which is used in this paper

is Locality- Based Least Connection Algorithm.

Weight Determination

Weight determination influenced by web content

type provided by web server. If web content type is

static hence the weight will only be influenced by

storage media speed factor, P

. If web content type is

dynamic hence the weight will only be influenced by

processor speed factor, P

. If web content type is a

mix between static and dynamic, hence its formula

will become

𝑤=𝛼𝑃





1−𝛼



𝑃



(1)

𝛼

is a ratio which determine contribution of

𝑃



and

𝑃



to the weight

𝑤

𝛼=

















(2)

with 𝑁



and 𝑁



are number of dynamic and static

web content access statistic.

4 RESEARCH METHODS

Methodology which is used in this paper covers

tools and materials, the way of research, system

design, and analysis.

A. Tools and Materials

Tools specification which are used in this paper are:

1. Load Balancer: Intel

Celeron

Dual-Core

N3060 1,6 GHz x 2, DDR3 SDRAM 2 GB, HD

Toshiba

SATA 500 GB x 1, NIC Realtek PCI

Fast Ethernet, Linux 4.8.6-300

2. Real-server 1: AMD

A4-1200 APU with

Radeon

HD Graphics 1GHz x 2, DDR3

SDRAM 2 GB, HD Seagate

Barracuda

ATA

500 GB x 1, NIC Realtek PCI Fast Ethernet,

Windows 8 Pro, Apache 2.2.25.

3. Real-server 2: AMD

Dual Core Processor C-50

1 GHz x 2, DDR3 SDRAM 2GB, HD Hitachi

ATA 320GB x 1, NIC Atheros Family PCI,

Windows 7 Ultimate, Apache 2.2.25.

4. Client: Intel

Celeron

M CPU 430 1,73 GHz,

DDR2 SDRAM Visipro

512 MB, HD Seagate

Barracuda

60 GB 5400 rpm x 1, NIC Broadcom

440x 10/100 Mbps, Linux 2.6.25-14

5. Switch: SMC

5-port 10/100Mbps Auto-MDIX

Switch - SMC-EZ6505TX (store-and-forward

transmission)

6. UTP cable (Cat 5) 15 meters.

iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science

1050

Materials which will be researched is the average

HTTP reply rate of cluster-based web server system

if HTTP request rate from client are dynamic.

The Way of Research

1. Hardware configuration.

In this research, there were only two real- servers

that being used, because it was hard to

find real-

servers with different specification in

laboratorium.

Real-servers with different

specification was more

suitable with real world

condition.

Figure 2: Hardware configuration.

2. Software configuration.

Load Balancer (LB)

● Network interface configuration and masking

(NAT)

● Load Balancer software configuration

● Define dispatching algorithm

● Load Balancer to Real-server 1 and 2 Address

and Port Mapping

● Weight configuration

Real-server

● Network interface configuration and web server

configuration on Real-server 1

● Network interface configuration and web server

configuration on Real-server 2

Client

● Network interface configuration

● Web workload testing software

configuration on

client

3. Doing dynamic web workload distribution test

on cluster-based web server system. On this test,

HTTP request rate produced was as big as 0 rps

to 1.000 rps, and distributed to both real-server in

the cluster-based web server system with

Locality-Based Least Connection Algorithm.

The number 1,000 HTTP request per second was

achieved by trial and error mechanism. From trial

and error process, we got this number 1,000

HTTP request per second. At this number of

HTTP request rate, HTTP reply rate from server

began to stable or saturated, not fluctuated. By

the end of the test there will be a data recording.

System Design

System which is designed in this paper is:

Clie

Figure 3: Network of cluster-based web server system.

Analysis

Web server system in this paper is evaluated

according to five test parameters, those are: HTTP

reply rate, response time, throughput, TCP

connection rate, and error. Those five test parameters

are tested for Locality-Based Least Connection

Algorithm. The test is done by producing HTTP

request rate from client dynamicly, and then record

HTTP reply rate, response time, throughput, TCP

connection rate and error between load balancer and

real-servers.

The data recording are presented in data table.

Presentation of those five parameters is done by

presenting text data recording of Locality-Based

Least Connection Algorithm result test. Then, there

will be a graphic chart consist of HTTP reply rate,

response time, throughput, TCP connection rate,

error, etc.

5 RESULTS AND DISCUSSION

After hardware and software configurations on

cluster-based web server system are finished, the next

step is dynamicly web workload distribution test

(with continuous changing HTTP request rate from 0

rps to 1000 rps).

Results of Dynamic Web Workload Test

In this test, HTTP request rate which is produced is

0 rps to 989.6 HTTP request per second, then

distributed to real-server with Locality-Based Least

Connection Algorithm.

The data recording of Dynamic web workload test

results for Locality-Based Least Connection

Algorithm are:

Dynamic Web Workload Distribution Test from 0 Rps to 1000 Rps on Cluster-based Web Server System with Locality-based Least

Connection Algorithm

1051

Table 1: Data Table Recorded for Dynamic Test

from 0

rps to 1000 rps with Locality-Based Least

Connection

Algorithm.

HTTP

request

rate

(rps)

HTTP

reply rate

(replie

s/s)

Response

Time

(ms)

Throug-

hput

(kBps)

TCP

Connec

tion Rate

(cps)

Errors

(err

or)

0 0 0 0 0 0

100 99.8 4.6 35.1 10 0.016

150 149.1 4.5 52.4 15 0.058

200 198.5 4.5 69.8 20 0.077

250 248.5 4.6 87.4 25 0.060

300 298.4 4.8 104.9 30 0.054

350 347.7 4.6 122.3 35 0.062

400 397.2 5.2 139.6 40 0.070

450 446.3 4.9 156.8 45 0.081

500 495.2 5.6 171 49.1 0.091

550 547.1 5.7 192.3 55 0.050

600 596.5 6.6 209.7 60 0.054

650 646.1 6.3 227.1 65 0.056

700 658.4 6.7 227.4 65.5 0.126

750 744.6 7.2 261.7 74.9 0.066

800 793.1 7.8 271.2 77.7 0.075

850 849.2 9.0 298.2 84.8 0

900 892.3 10.9 313.7 89.9 0.077

950 940.5 11.5 330.6 94.8 0.085

1000 989.9 21.8 347.9 99.9 0.091

From Table 1 above, we can see that HTTP

request rates were generated from 0 rps to 1000 rps

with step 50 rps. For each of HTTP request rate

generated, there will be five parameters recorded.

1. The first was HTTP reply rate (in unit of

replies per second) parameter which is

recorded

and presented in second column of Table 1.

2. The second was Response Time (in unit of

millisecond) parameter which is recorded and

presented in third column of Table 1.

3. The third was Throughput (in unit of kilo Bytes

per second) parameter which is

recorded and

presented in fourth column of Table 1

4. The fourth was TCP Connection Rate (in

unit

of connections per second) parameter which is

recorded and presented in fifth

column of Table

5. The fifth was Errors (in unit of error) parameter

which is recorded and presented

in sixth column

of Table 1

The next step we take from Table 1 was, we

created and processed those five parameters above

and presented it in five different graphics.

1. The first graphic (see Figure 4) describes

HTTP Reply Rate parameter versus HTTP

Request Rate parameter

2. The second graphic (see Figure 5) describes

Response Time parameter versus HTTP Request

Rate parameter

3. The third graphic (see Figure 6) describes

Throughput parameter versus HTTP Request

Rate parameter

4. The fourth graphic (see Figure 7) describes TCP

Connection Rate parameter versus HTTP

Request Rate parameter

5. The fifth graphic (see Figure 8) describes Errors

parameter versus HTTP Request Rate parameter

Each of graphic was presented bellow.

Figure 4: HTTP Reply Rate parameter versus HTTP

Request Rate parameter.

Figure 5: Response Time parameter versus HTTP Request

Rate parameter.

iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science

1052

Figure 6: Throughput parameter versus HTTP Request

Rate parameter.

Figure 7: TCP Connection Rate parameter versus HTTP

Request Rate parameter.

Figure 8: Errors parameter versus HTTP Request Rate

parameter.

Result of dynamicly testing with continuous

changing HTTP request rate from 0 rps to 1000 rps in

the data recording above shows that HTTP requests

were well-distributed to web server system pool by

Locality-Based Least Connection Algorithm. It

means that all of HTTP requests were replied by pool

of web servers in the cluster. Web server cluster

working together to reply almost all of the request in

certain sequence, according to Locality-Based Least

Connection Algorithm. Locality-Based Least

Connection was working to assigns jobs (directing

HTTP requests from client) destined for the same IP

address to the same server if the server is not

overloaded and available; otherwise assign jobs to

servers with fewer jobs, and keep it for future

assignment.

We can see from Figure 4, Figure 6, and Figure 7

that these three graphics of parameters were

increasing linearly.

Response times were relatively low (see Figure

5). And errors was nearly 0 (see Figure 8).

6 CONCLUSION

Conclusion which can be taken from this research is:

Result of dynamicly web workload testing with

continuous changing HTTP request rate from 0 rps to

1000 rps shows that HTTP requests were well-

distributed to and well-replied from web server system

pool by Locality-Based Least Connection Algorithm.

REFERENCES

Roger L. Freeman. (1998). Telecommunication

Transmission Handbook, 4th edition. Canada: John

Wiley & Sons, Inc.

William Stallings. (2000). Data and Computer

Communication, 6th edition. Upper Saddle River,

New Jersey: Prentice-Hall.

H. Kaplan, B. Noseworthy. (2000). The Ethernet

Evolution: 10 to 10,000 Mbps. Atlanta: Networld

Interop.

J. Gray, P. Shenoy. (2000). Rules of Thumb in Data

Engineering. In IEEE 16th International Conference on

Data Engineering. San Diego, California: IEEE.

IA-32 Intel®. (2003). Architecture Software

Developer’s

Manual Vol. 1: Basic Architecture, Order Number

24547-012. Illionis: Intel Corporation.

Valeria Cardellini, Emiliano Casalicchio, Michele

Colajanni, Philip S. Yu. (2001). The State of the Art

in Locally Distributed Web-server Systems. IBM

Research Report.

G. Gilder. ( 2008) . The Coming Creativity Boom.

http://www.forbes.com/forbes/2008/1110/036.html

N. G. Shivaratri, P. Krueger, M. Singhal. (1992) . Load

Distributing for Locally Distributed Systems. IEEE

Computer.

Dynamic Web Workload Distribution Test from 0 Rps to 1000 Rps on Cluster-based Web Server System with Locality-based Least

Connection Algorithm

1053