Paypal, Inc.

Sedai Inc.

Vaibhav Desai

Suresh Mathew

Shankar Jothi

Benjamin Thomas

Krishnakanth Batta

Arvind Sugumar

Ramakrishnan Sumesh Vadassery

Rami El-Charif

Nikhil Gopinath Kurup

Hari Chandrasekhar

Jigar Chandrakant Desai

Muthukumar Rethinakaleeswaran

There are provided systems and methods for a distributed autonomous patching system. During patching of a cloud computing architecture, an autonomous patching system may operator autonomously with no to minimal operator input to patch the host machines and corresponding computes of the cloud computing architecture's available applications. This may work by receiving a patch and determining corresponding patching factors for an availability zone of computes in the cloud. The system may then determine a patching topology map having an order of patching nodes for the selected computes of the application in the availability zone. Thereafter, while monitoring those computes, the system may select certain computes into an active processing funnel, where the patch is applied to those computes. Fingerprinting of before and after-patch states may be used to ensure proper patching.

Distributed autonomous patching system

Implementations described herein relate to methods, systems, and computer-readable media to identify anomalous applications. In some implementations, the methods may include obtaining application metric data that includes application level metrics and instance level metrics for each instance of a plurality of instances associated with a respective application of a plurality of applications operating over a distributed computing system, generating a first anomaly detection score based on the instance level metrics; generating a second anomaly detection score based on one or more input metrics associated with the respective application, generating a third anomaly detection score based on seasonal metric data associated with the respective application and identifying at least one application of the plurality of applications as an anomalous application based on the first anomaly detection score, the second anomaly detection score, and the third anomaly detection score.

Cloud management system with autonomous aberrant behavior detection

Aspects of the present disclosure involve a system and method for autoscaling application pools. The current disclosure presents a system that can be used to understand the throughput of one instance in an application. The throughput at the one instance in the application and/or traffic trend is used to right-size or auto-scale the application pool for the current traffic trend such that the application pool can be adjusted for varying traffic trends.

Method and system for autoscaling applications

A system and method for adaptively identifying and correcting issues in a computing system, such as a distributed node computing system, are described. The method includes receiving node data from a group of nodes, the node data describing one or more operational characteristics of a node. The operational characteristics may include CPU load, memory load, latency, or other operational data that describes node performance. Reachability data for the group of nodes is generated by trying to contact each node. Code version data is generated for each node that identifies which version of code applications in the node are running. The nodes are grouped into clusters using density-based clustering to identify outliers. A correlation is determined between the reachability, code version, and outlier data to identify problems and issue corrective actions.

Computational node adaptive correction system

Growing community of inventors

Fremont, CA, United States of America

Vaibhav Desai