A Multivariate Statistical Approach To Network Anomaly Detection In Cloud Data Migration

Abstract

Automation of the migration process is a very tedious and challenging task. It involved several parameter including monitoring of the enviroment. Forecasting and taking proper measurement for smooth migration is challenging.Using AI and other techniques we are trying to solve the migration issues. One such attempt is detection of network anamolies in migration environment in quantitative approach.This paper presents a comprehensive analysis of metric anomaly detection techniques specifically designed for cloud computing environments. We examine the unique challenges posed by the dynamic, scalable, and distributed nature of cloud systems, and analyze how metric anomalies can impact the operational efficiency and security posture of cloud services. In addition, we explore emerging methodologies, including machine learning models and statistical analysis techniques, that enhance the precision and speed of metric anomaly detection.By addressing these critical concerns, this paper aims to provide cloud service providers, IT professionals, and researchers with practical insights and effective strategies for identifying and responding to metric anomalies. It serves as a valuable resource for understanding the complexities of metric monitoring in cloud environments and offers guidance on best practices for building robust, adaptive, and resilient cloud infrastructures.