US 11,916,765 B2
Correlation score based commonness indication associated with a point anomaly pertinent to data pattern changes in a cloud-based application acceleration as a service environment
Shyamtanu Majumder, Karnataka (IN); Justin Joseph, Karnataka (IN); Johny Nainwani, Rajasthan (IN); and Parth Arvindbhai Patel, Gujarat (IN)
Assigned to ARYAKA NETWORKS, INC., Newark, CA (US)
Filed by Shyamtanu Majumder, Karnataka (IN); Justin Joseph, Karnataka (IN); Johny Nainwani, Rajasthan (IN); and Parth Arvindbhai Patel, Gujarat (IN)
Filed on Jun. 15, 2021, as Appl. No. 17/348,746.
Application 17/348,746 is a continuation in part of application No. 16/660,813, filed on Oct. 23, 2019, granted, now 11,070,440.
Prior Publication US 2021/0314242 A1, Oct. 7, 2021
Int. Cl. H04L 43/04 (2022.01); H04L 67/10 (2022.01)
CPC H04L 43/04 (2013.01) [H04L 67/10 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
detecting, through a server of a cloud computing network comprising a plurality of subscribers of application acceleration as a service provided by the cloud computing network at a corresponding plurality of client devices communicatively coupled to the server, real-time data associated with each network entity of a plurality of network entities of the cloud computing network for each feature thereof sequentially in time;
detecting, through the server, a point anomaly in the real-time data associated with the each network entity based on determining whether the real-time data falls outside a threshold expected value thereof;
representing, through the server, the detected point anomaly in a full mesh Q node graph, wherein Q is a number of features applicable for the each network entity;
capturing, through the server, a transition in the point anomaly associated with a newly detected one of: anomaly and non-anomaly in the real-time data associated with at least one feature of the Q number of features via the representation of the full mesh Q node graph; and
deriving, through the server, a current data correlation score for the point anomaly across the captured transition as:

OG Complex Work Unit Math
wherein CS is the current data correlation score for the point anomaly across the captured transition, APC is a count of a total number of pairs of Y current anomalous features in the Q number of features and is given by YC2+YC1, EWPi is a weight of an edge of the ith pair of the Y current anomalous features in the representation of the full mesh Q node graph, and TSAC is a total number of time samples of the point anomaly comprising the captured transition, and
wherein the current data correlation score is indicative of a commonness of a combination of the Y current anomalous features contributing to the point anomaly with respect to an equivalent Y anomalous features contributing to another previously detected point anomaly associated with the each network entity.