Design Architecture-Based on Web Server and Application Cluster in Cloud Environment

TL;DR

This paper proposes a web server architecture integrated with clustering and load balancing to enhance data search efficiency in cloud environments, particularly for Hadoop's HDFS, using multi-level indexing and web clustering techniques.

Contribution

It introduces a novel web server framework with multi-level indexing and clustering to improve data search speed and load distribution in cloud-based Hadoop systems.

Findings

Enhanced data retrieval speed in HDFS

Improved load balancing with web clustering

Efficient real-time processing in Hadoop

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.