Open AccessEnhanced Adaptive Learning Mechanism for Cloud Selection
Author(s) -
V Maruthi,
P. Shanthi,
A. Umamakeswari
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.24.12149
Subject(s) - cloud computing , computer science , scalability , elasticity (physics) , selection (genetic algorithm) , distributed computing , task (project management) , cloud testing , cloud computing security , machine learning , database , engineering , operating system , systems engineering , materials science , composite material
Estimation of cloud services in a distributed computing environment is taking more interests. There is a wealth of developing cloud benefit assets that makes it difficult for the user to select the best administration related to own applications in an evolving multiple cloud environment, particularly for online processing applications. To make clients to choose their interested cloud adequately, we need a model which holds the cloud profits, and hence dynamic cloud benefit determination procedure named Dynamic Cloud Selection (DCS) is adapted. In this procedure of selected services, every cloud benefit business deals with some group of cloud administrations, and executes the DCS method. This paper studies the cloud selection and proposed a way to improve the cloud selection based on related measures. The measures are reliability, response time, throughput, availability, utilization, resilience, scalability, and elasticity. The system is contrived to enhance the cloud benefit choice powerfully and to restore the best administration result to the client. These measures are used to form best selection strategy. User memory requirement is also considered to improve the preferred task. Experimental results proved that using this new strategy, best cloud selection is made efficiently.