Efficient detection and prediction of data pattern changes in a cloud-based application acceleration as a service environment

Abstract

A method implemented through a server of a cloud computing network including subscribers of application acceleration as a service provided therethrough includes sampling time series data associated with each network entity for each feature thereof into a smaller time interval as a first data series and a second data series including a maximum value and a minimum value respectively of the sampled time series data for the each feature within the smaller time interval, and generating a reference data band from predicted future data sets. The method also includes detecting, based on the reference data band, an anomaly in real-time data associated with the each network entity for the each feature thereof and determining an event associated with a pattern of change of the real-time data associated with the each network entity based on executing an optimization algorithm to determine a series of anomalies including the detected anomaly.