Paper Contents
Abstract
To optimize cloud computing resources according to actual demand and to reduce the cost of cloud services. Such approaches mostly focus on a single factor (i.e., compute power) optimization, but this can yield unsatisfactory results in real-world cloud workloads which are multi-factor, dynamic and irregular. It presents a novel approach which uses anomaly detection, machine learning and particle swarm optimization to achieve a cost-optimal cloud resource configuration.
Copyright
Copyright © 2023 E.GOKULAPRIYA. This is an open access article distributed under the Creative Commons Attribution License.
Paper ID:
IJPREMS30600014396
ISSN:
2321-9653
Publisher:
ijprems