Efficient Predictive Model for Utilization of Computing Resources using Machine Learning Techniques

• Published in: 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) pp. 351 - 357
• Main Authors: Sendhil Kumar, K.S., Anbarasi, M., Shanmugam, G. Siva, Shankar, Achyut
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-01-2020
• Subjects: Computational modeling; confidence prediction; Data models; decision tree; Linear regression; Machine learning; Mathematical model; naive Bayesian classifier; predictive analytics; Predictive models; regression techniques; Regression tree analysis
• DOI: 10.1109/Confluence47617.2020.9057935

Data mining is a viable innovation to break down and extract patterns from crude information, which can change the original data into up-to-date information. Predictive analytics includes an assortment of factual systems that analyze present and historical facts to make forecasts about future or generally obscure occasions. Machine learning incorporates statistical methods for regression and classification. The objective of machine learning is to create a predictive model that is unclear from the correct model. The assessed relative execution qualities were evaluated by Ein-Dor and feldermesser utilizing a linear regression method considering the properties machine cycle time, minimum main memory, maximum main memory, cache memory, minimum channels, and maximum channels. This relationship is communicated as a mathematical statement that predicts the reaction variable published relative performance as a linear function of the parameters. The proposed technique utilizes machine learning work to re-phrase prediction as an optimization problem. Confidence prediction and polynomial regression include imaginative application utilization and promising research. The experimental evaluation platform contains detailed performance analysis of the preferred methods. It is expected that this machine learning approach gives a quick and straightforward approach to fabricate applications.