Comparative Study on Various Storage Optimisation Techniques in Machine Learning based Cloud Computing System
Jyoti L. Bangare, Dhiraj Kapila, Pallavi Nehete, Sulakshana Malwade, K. Sankar, Samrat Ray
Abstract
Cloud computing is gaining more popularity in various industries as it enables in creating better services to different individuals, companies and government. With the progressive aspect on the concept, it offers flexible solutions to the growing computing problems related to infrastructure, application and other aspects. Cloud computing enables in minimizing the investments on IT infrastructure as it offers better and extensive support in saving various data and information of the companies, government and individuals, enable in retrieving them as and when required. Through the application of cloud computing the users received better and reliable services from the service providers which enables in reducing the investments, optimizing the cost and enable in enhancing products of the company in the long run. The cloud computing is taking better aspects and is considered as the paramount aspect for the business and individuals. This study is more focused in making a comparative understanding in the optimization techniques using machine learning based cloud computing systems. This research is more focused in analysing the application of machine learning as a key technique which can be applied in order to estimate the request patterns of the clients related to cloud storage. The research further focuses on the machine learning techniques covering linear regression model, Artificial neural networks, Support vector machines etc. are being explored in order to understand the optimization techniques for services the users in a better manner. The research applies questionnaire method for collecting the information and extensive analysis using SPSS is applied for analysing the data.