Autonomic Approach For Fault Tolerance Using Scaling Replication And Monitoring Of Servers In Cloud Computing

Download or read book Autonomic approach for fault tolerance using scaling, replication and monitoring of servers in cloud computing written by Ashima Garg and published by GRIN Verlag. This book was released on 2016-08-05 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2015 in the subject Computer Science - Technical Computer Science, , course: M.Tech (CSE), language: English, abstract: This work introduces an autonomic prospective on managing the fault tolerance which ensure scalability, reliability and availability. HAProxy has been used to provide scaling to the web servers for load balancing in proactive manner. It also monitors the web servers for fault prevention at the user level. Our framework works with autonomic mirroring and load balancing of data in database servers using MySQL master- master replication and Nginx respectively. Here nginx is used to balance the load among the database servers. It shifts the request to the appropriate DB server. Administrator keeps an eye on working of servers through Nagios tool 24X7 monitoring can’t be done manually by the service provider. The proposed work has been implemented in the cloud virtualization environment. Experimental results show that our framework can deal with fault tolerance very effectively. Cloud based systems are more popular in today’s world but fault tolerance in cloud is a gigantic challenge, as it affects the reliability and availability for the end users. A number of tools have been deployed to minimize the impact of faults. A fault tolerable system ensures to perform continuous operation and produce correct results even after the failure of components up to some extent. More over huge amount of data in the cloud cannot monitor manually by the administrator. Automated tools, dynamic deploying of more servers are the basic requirements of the today’s cloud system in order to handle unexpected traffic spikes in the network.