Aryaka Networks, Inc.

Cdnetworks Co., Ltd.

Vikas Garg

Shyamtanu Majumder

Parth Arvindbhai Patel

Justin Joseph

Johny Nainwani

Ashwath Nagaraj

Vivek Padmanabhan

Arijit Ghosh

A method implemented through a data processing device in a computing network 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.

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

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.

A distributed data cache included in a content delivery network expedites retrieval of data for application execution by a server in a content delivery network. The distributed data cache is distributed across computer-readable storage media included in a plurality of servers in the content delivery network. When an application generates a query for data, a server in the content delivery network determines whether the distributed data cache includes data associated with the query. If data associated with the query is stored in the distributed data cache, the data is retrieved from the distributed data cache. If the distributed data cache does not include data associated with the query, the data is retrieved from a database and the query and associated data are stored in the distributed data cache to expedite subsequent retrieval of the data when the application issues the same query.

Distributed data cache for on-demand application acceleration

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Saratoga, CA, United States of America

Vikas Garg