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.

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.

Abstract: Disclosed is a method of incremental computation of billing percentile values in a cloud based application acceleration as a service environment. In one aspect, a method includes sampling a usage data of a network entity of an application acceleration as a service provider in intervals of five minutes using a processor and a memory. A 95th percentile value is automatically calculated based on a next value in the billing cycle after the top 5% of samples in the billing cycle. The 95th percentile value of each of a plurality of billable units for each of billing measurements for a large scale data associated with the network entity is incrementally computed by computing the 95th percentile value upon a newest set of data arrived to the network entity in each five minute interval. A billing amount is determined based on an incremental computation.

Abstract: Disclosed is a method of incremental computation of billing percentile values in a cloud based application acceleration as a service environment. In one aspect, a method includes sampling a usage data of a network entity of an application acceleration as a service provider in intervals of five minutes using a processor and a memory. A 95th percentile value is automatically calculated based on a next value in the billing cycle after the top 5% of samples in the billing cycle. The 95th percentile value of each of a plurality of billable units for each of billing measurements for a large scale data associated with the network entity is incrementally computed by computing the 95th percentile value upon a newest set of data arrived to the network entity in each five minute interval. A billing amount is determined based on an incremental computation.