DESIGN ARCHITECTURE-BASED ON WEB SERVER AND APPLICATION CLUSTER IN CLOUD ENVIRONMENT

Sundarapandian et al. (Eds) : CCSEA, EMSA, DKMP, CLOUD, SEA - 2014
pp. 111–119, 2014. © CS & IT-CSCP 2014 DOI : 10.5121/csit.2014.4311
DESIGN ARCHITECTURE-BASED ON WEB
SERVER AND APPLICATION CLUSTER IN
CLOUD ENVIRONMENT
Gita Shah 1
, Annappa2
and K. C. Shet3
1,2,3
Department of Computer Science & Engineering,
National Institute of Technology, Karnataka, Surathkal, India
geet107@gmail.com, annappa@ieee.org, kcshet@rediffmail.com
ABSTRACT
Cloud has been a computational and storage solution for many data centric organizations. The
problem today those organizations are facing from the cloud is in data searching in an efficient
manner. A framework is required to distribute the work of searching and fetching from
thousands of computers. The data in HDFS is scattered and needs lots of time to retrieve. The
major idea is to design a web server in the map phase using the jetty web server which will give
a fast and efficient way of searching data in MapReduce paradigm. For real time processing on
Hadoop, a searchable mechanism is implemented in HDFS by creating a multilevel index in
web server with multi-level index keys. The web server uses to handle traffic throughput. By web
clustering technology we can improve the application performance. To keep the work down, the
load balancer should automatically be able to distribute load to the newly added nodes in the
server.
KEYWORDS
Compute Cloud, Hadoop, MapReduce, load balancing, Web server.
1. INTRODUCTION
Cloud is emerging as a cost effective solution in the computing world. The data capacity of the
cloud has gone up to a zettabyte from gigabyte. Cloud Computing are the virtual pool of
computing resources that provides an online pool of resources for computing, where the
computing is performed by efficient sharing of resources, which we call as load balancing/
resource sharing. Cloud relies on its resources for handling the application in the local and
personal devices.
MapReduce [2] is the heartbeat of Hadoop framework. It is a programming paradigm that allows
for huge scalability across hundreds or thousands of servers in a cluster. It is a programming
model that is associated with the implementation of processing and generating large data sets. The
term MapReduce basically refers to two separate and distinct tasks that Hadoop programs
perform [6]. MapReduce also plays an important factor in Cloud computing environment,
because it decreases the complexity of the distributed programming and is easy to be developing
on large clusters of common machine [12]. The data flow architecture of MapReduce shows the
techniques to analyze and produce the search index [12] as shown in figure1.

112 Computer Science & Information Technology (CS & IT)
The main purpose of this work is to develop a technique for a fast and efficient way of searching
data in the MapReduce paradigm of the Hadoop Distributed File System. Hadoop is a framework
which is specifically designed to process and handle vast amounts of data [10]. It is based on the
principle of moving computation to the place of data which is cheaper than moving large data
blocks to the place of computation.
The Hadoop [1] MapReduce framework is a master - slave architecture, which has a single master
server called a job tracker and several slave servers called task trackers, one per node in the
cluster. The job tracker is the point of interaction between users and the framework. The user
submits map and reduce jobs to the job tracker, which puts them in a queue of pending jobs and
executes them on a first-come-first-served basis.
Figure 1. MapReduce data flow
In this paper the schematic view of the design architecture is given, with effective scenario to
investigate the performance of Hadoop in high speed retrieval of data in the cloud environment by
replacing the map phase of the MapReduce paradigm with a web server. For real time processing
on Hadoop, a searchable mechanism is implemented in HDFS by creating multilevel index in web
server with multi-level index keys in NameNode.
The rest of the paper is structured as follows. Module 2 & 3 highlights the related works and
Hadoop Distributed File System implementation. Module 4 describes the web application in a
cloud and architecture design. Module 5 describes the implementation details followed by the
results & analysis. Finally work done is concluding, with future work in module 6.
2. RELATED WORK
Hadoop [10] is an open source Java-based programming framework which is a part of the Apache
project which was sponsored by the Apache Software Foundation. MapReduce is a part of
Hadoop framework. It is a programming concept and Hadoop is a framework to run MapReduce
program. In February 2003 first MapReduce library was introduced @Google. In 2004
MapReduce was simplified for data processing on large cluster [4]. Google introduced
MapReduce in 2004 to support large distributed computing, on large clusters of computers to
implement huge amounts of data set [3]. MapReduce is a framework that processed parallel
problems of huge data set using a large number of computers or nodes that is collectively referred
to as a cluster or a grid [7]. A distributed file system (DFS) facilitates rapid data transfer rates
among nodes and allows the system to continue operating uninterrupted in case of a node failure.
It was originally developed to support distribution for the Nutch search engine project. In January
2008 Hadoop made apache as a top level project center.

Computer Science & Information Technology (CS & IT) 113
In July 2009 new Hadoop subproject was found where Hadoop core is renamed as Hadoop
Common. In this subproject MapReduce and Hadoop distributed file system (HDFS) get
separated for doing separate work. HDFS is designed to store very large data sets [10]. In 2010
web application based process is used to handle high throughput traffic [6]. Facebook has
claimed that they had the largest Hadoop cluster in the world with 21 PB of storage and on July
27th, 2011 they have announced that the data had grown up to 30PB.
In 2012 the load rebalancing problem in cloud DFSs is illustrated by Hsueh-Yi Chung Che-Wei
Chang Hung Chang Hsiao, Yu-Change Chao. The file system in cloud shall incorporate
decentralized load rebalancing algorithm to eliminate the performance [17]. In May 2013 a fully
distributed load rebalancing algorithm is presented to cope with the load imbalance problem by
Hung-Chang, Haiying Shen. This algorithm is a centralized approached in a production system
and a competing system [18].
3. HADOOP DISTRIBUTED FILE SYSTEM
Recent trends in the analysis of big data sets from scientific applications show adoption of the
Google style programming infrastructure, MapReduce [2]. Hadoop, which is a collection of open-
source projects originated by “Doug Cutting in 2006” to apply the Google MapReduce
programming framework across a distributed system [8]. It provides an easily obtained
framework for distributed processing, and a number of open-source projects quickly emerged
which leveraged this to solve very specific problems. Hadoop in a cloud computing environment
supports the processing of large data sets in a distributed computing environment, primarily in
data warehouse systems and plays an important role in support of big data analytics to understand
the user behavior and their needs in web services [9].
HDFS stores file system and application data separately. HDFS also provides high throughput
access to the application of data and it is suitable for the application that has large data sets.
HDFS is highly fault-tolerant, which provides high throughput access to the application data and
is a perfect application that has large data sets. It adopts the master and slave architecture.
HDFS cluster consists of DataNode and NameNode shown in Figure 2. NameNode which is a
central server, also called as a Master server, is responsible for managing the file system
namespace and client access to files and DataNode in the cluster node are responsible for files
storage. In HDFS, a file is divided into one or more number of blocks for storing data [13].
Figure 2. HDFS Architecture

4. WEB APPLICATION IN A CLOUD AND ARCHITECTURE DESIGN
In the following module, the schematic design of the architecture for a scaling structure and the
dynamic scaling for a web application in cloud is designed. The structure is based on using a
front-end load balancer to dynamically route user requests to backend, Master web servers that
host the web application. The number of web servers should automatically scale according to the
threshold on the number of current active sessions in each web server instance to maintain service
quality requirements.
High speed data retrieval from cloud storage has been an active area of research. The Apache
Hadoop project design, architecture which is based on the web server and application cluster in
cloud environment was started with this as one aim in mind. We review here the basic details of
the design architecture, apache load balancer and the details of the Master web server with the
multi-level indexing. In the following module, the schematic design of the architecture for a
scaling scenario and the dynamic scaling web application for the web in a Cloud is clustered.
4.1. Architecture Design
The data in Hadoop Distributed File System is scattered, searching and data retrieval is one of the
most challenging tasks, so to overcome these tasks a searchable mechanism in HDFS has been
implemented. A web server has been designed in the Map phase of MapReduce. The jetty web
server is considered as a web interface. Web applications are generated in the HDFS and a web
server is started in the form of Hadoop job by submitting our keys to URL Hadoop url/file/key.
The Apache load balancer is used in between the client and server to balance the work load of the
localhost because if one level is not sufficient, then the system will automatically expand to the
second level of indexing.
For real time processing on Hadoop, a searchable mechanism is implemented in HDFS by
creating a multilevel index in web server with multi-level index keys. To keep the work down, the
load balancer should automatically be able to distribute load to the newly added nodes in the
server. The load balancer is used to balance the workload across servers to improve its
availability, performance and scalability. To be able to exploit the elasticity of a cloud
infrastructure, the applications usually need to be able to scale horizontally, i.e. it must be
possible to add and remove nodes offering the same capabilities as the existing ones. In such
scenarios, a load balancer is usually used.
NameNode is divided into Master and Slave server. Master server contains multi - level index and
slave have data and key index. In the server at the time of the data request, the key is being passed
on multi level indexes. Indexing all the process is done by Master server and all retrieval process
are done by slave serve. The overall view of the design architecture has been shown in the figure
3.

Figure 3. Schematic view of design architecture
4.2. Load Balancer
It is used to balance the workload across servers to improve its availability, performance and
scalability. Its purpose is to improve the performance of the distributed system through an
appropriate distribution of the application [17]. In a distributed system, it is possible for some
computers to be heavily loaded while others are lightly loaded. In this situation system can lead to
poor system. The goal of load balancing is to improve the performance by balancing the loads
among computers.
The Apache load balancer will give better performance of the workload. These can be configured
to know a certain number of workers and their host names, but no workers can be added
automatically during runtime [14]. It is implemented in between clients and servers to balance the
work load of the web server. These servers run on each NameNode and DataNode of which web
application package is deployed on the HDFS [13].
4.3. Load Balancing With The Master Web Server
A common approach is used in combination of the Apache module mod proxy [15] and mod proxy
balancer [16]. After adding these modules to the Apache Web Server 2, a virtual host configuration
has to be created that configures the load balancer and its balance members. By using the directive
ProxySet, the load balancing method can be set to balance either by the number requests, by
counting the bytes of the traffic, or by pending requests. By using the Balancer Member directive,
a worker with the given URL can be introduced to the load balancer, including how much load it
should receive.
The Master server runs on each NameNode and DataNode (Figure 4), web server application
package is deployed on the HDFS. The web server in NameNode is divided into Master/slave
architecture, according to HDFS storage features, it will store all the keys in the indexing form.
All client requests are first sent to the NameNode through URL Hadoop url/file/key, and Master
server in NameNode will decide which the Master server either master/slave on DataNode to
respond to the request according to the location information of web files stored in the multi-level
indexing on NameNode, and then redirected URL to the node and request the same Uri, finally, a
connection can be established between the current Master web server and the browser, and
sending files and data between them. To be able to exploit the elasticity of a cloud infrastructure,
the applications usually need to be able to scale horizontally, i.e. it must be possible to add and

remove nodes offering the same capabilities as the existing ones. In such scenarios, a load balancer
is usually used.
It is possible to add the new workers directly to the Master server configuration and starting the
load balancer, a large number of virtual folders can be defined in the virtual host configuration.
Then, whenever a worker registers at the load balancer, the htaccess rewriter writes a forward rule
to the. htaccess file located in htdocs/virtual a request sent to, the URL path route/1 is redirected to
the first URL specified in the htaccess file, a request sent to, the URL path route/2 to the second,
and so on. Since then htaccess file is evaluated for each request, changing it at runtime allows
additions as well as removal of workers. With these measures, it is possible to allow workers to
register at the load balancer automatically upon their startup and shutdown, thus bypass the need to
manually add or remove workers. Even the rules allow for the changes take effect during runtime.
Further modifications of the load factor are possible by collecting monitoring data [11].
A starting load factor can be determined by a performance test. Load balancer knows the potential
workload of all workers; it can calculate a load factor for each worker proportional to each
worker’s potential or monitored workload [5].
Figure 4. Master web servers based on cluster architecture
The features of load balancer are
• If a server dies connection will be directed to the nearest server.
• Once the server comes back online it rejoins the cluster.
• It can be controlled to take into account machine difference.
• It supports complex topologies and failover configuration
• It distributes the client request across the server, redirect to the localhost.
5. RESULT & ANALYSIS
In this module, for the implementation, we configured Hadoop with 6 DataNodes and one
NameNode, 6 Task trackers and one job tracker. With each of the nodes we install the jetty server
and connect it with Hadoop using eclipse. Jetty server is running on each of DataNode of Master
server. Each jetty server works on specific key. Master server contains a list of keys which
correspond to jetty server information. When a read request come from the clients via Master
server then it does look up to find the jetty server for corresponding keys and then forward it to the
corresponding server. For our experiments we use all systems with Core i7 2.93 GHz CPU, 8 GB
DDR3 RAM, 1 TB hard drive. All systems are connected over a 1 Gbps switch.

Web applications are generated in the HDFS and a web server is started in the form of Hadoop job
by submitting our keys to the URL of Hadoop i.e. Hadoop url/file/key. The key and value are
submitted to the local host of a Hadoop URL in the file system of Master web server and read
operations can be performed in the same URL. The read operation performed in Hadoop is done
by Master web server through Uniform Resource Locator. To keep the work down, the load
balancer should automatically be able to distribute load to the newly added nodes in the server.
When one Master Server (jetty server) is running and read operation is carried out from HDFS, it
has been seen that the average time (seconds) keeps on increasing when data input is increased in
HDFS. The average time taken for retrieving data in HDFS is shown in the figure 6 and is 4.2
seconds for 100MB data size, 6.8seconds for 1000MB, 17.3seconds for 10000MB and 24.7second
for 100000MB data size.
Figure 5. Time taken for single Master server (jetty server) in the retrieval of data from HDFS.
Increased of Master Server (jetty server) will give better performance for retrieving data through
the Hadoop url / file/key. It has been seen that the average time (retrieving data) keeps on
decreasing when data input is constant or increasing in HDFS. The Master web server (Jetty
server) takes seconds to read a particular or text data from 100000 MB size of data which has been
stored in the HDFS where the data are kept constant (100000MB). We have increased the number
of servers from 1 to 6 servers for better performance in retrieving of text data from HDFS. The
average time taken for retrieval of text data in HDFS by Master server is shown in Figure 7 and is
22.7 seconds, 17.8seconds, and 14.7seconds, 8.3seconds, 7.6seconds and 4.8seconds form
100000MB data size where data are kept constant and web servers are increasing.

Figure 6. Master Server in retrieval of data from HDFS
6. CONCLUSION & FUTURE WORK
In this paper, we have given the schematic view of the designed architecture with effective
structure to investigate the performance of Hadoop in high speed retrieval of data in a cloud
environment. Apache Web Server a dynamic cloud-ready load balancer was presented as well. It
was shown that the Master web server is a static load balancer when using one of the common load
balancing modules. To avoid having to manually add workers to the load balancer configuration,
we have also added a benchmarking and registration component to the workers.
The results shows that using this architecture in the Hadoop and making map phase as a web
server, faster read operation for MapReduce programs can be achieved through URL. For large
databases of data warehouse applications in cloud, searching process takes a very long time as the
data is scattered. High sped retrieval of data can be particularly useful for real-time applications
based on Hadoop where quick fetching and storage of data is necessary.
In future, experiments can be carried out on large datasets and some real-time applications to
measure the usefulness of the proposed approach. This web server can conceptually be extended to
provide dynamic load balancing capabilities. Apache Web Server cans also be used as a load
balancer in combination with Apache Tomcat workers.
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