Capital One Services, LLC

Kadhiresan Kanniyappan

Arunkumar Natarajan

Venkata Mandali

Sateesh Mamidala

Krystan R Franzen

Abel Fletcher

Anand Annamalai

Manohar Malapati

Elijah Carter

Priyanka Kulkarni

Raghava Bidathanapalli

Emmanuel Obogbaimhe

Cuong Ly

Yasawy Rajendraprasad Ravala

Cory Williams

Srinivasan Subramanian

Prasad Konduri

Fnu Ashish Kumar

Patrick Barnes

Matthew Zheng

Matthew Blake Ackard

Joseph Knight

Bhaskar Banerjee

Gayatri Vishnubhotla

Keith Adler

Aruna Alla

Yasaswy Ravala

Paul Pelayo

Kevin P Poffenberger

In some implementations, a device may cause execution of a batch processing job. The batch processing job may be associated with an application and at least one user. The device may determine that execution of the batch processing job is associated with one or more execution errors. The device may determine, based on the one or more execution errors, an error remediation action for the batch processing job. The device may cause performance of the error remediation action for the batch processing job.

Batch processing and error resolution management

In some embodiments, the present disclosure provides an exemplary method that may include steps of identifying at least one computing specification image within a plurality of computing specification images; monitoring each data agent within the plurality of preinstalled data agents for a predetermined period of time to establish a data agent usage baseline associated with each data agent within the plurality of preinstalled data agents; utilizing a chaos engineering algorithm to dynamically perturb each data agent; calculating a usage test score for each data agent within the plurality of preinstalled data agents; calculating an overall data agent-specific usage score associated with each data agent within the plurality of preinstalled data agents based on the plurality of data agent-specific usage test scores; and rejecting at least one data agent within the plurality of preinstalled data agents from being utilized to launch the instance of the software application.

Computer-based systems configured for dynamic performance scoring of software agents and methods of use thereof

Disclosed herein are system, method, and computer program product embodiments for determining monitoring compliance for an enterprise application deployed on an application delivery platform. Existing resources, monitors and alerts are discovered with gaps in monitoring being calculated based on a comparison of monitoring objectives and the existing monitors and alerts. Gaps in monitoring are reported in a GUI reflecting effectiveness of existing monitors.

Automated monitoring of infrastructure and application on cloud

A method including receiving, from a user device, a user request to access data associated with a web page; generating, by a processor, a first transaction identification; collecting transaction information, the transaction information comprising server-side metrics; integrating, by the processor, the first transaction identification with the transaction information; transmitting, by the processor, the first transaction identification to the user device; receiving, from the user device, client-side data associated with a second transaction identification; integrating, by the processor, the server-side metrics and the client-side data; and analyzing, by the processor, the integrated server-side metrics and the client-side data.

Methods and systems for measuring user and system metrics

An exemplary system, method, and computer-accessible medium for classifying data associated with an application performance management ('APM') agent(s) deployed on a server(s), can include, for example, receiving first information related to data to be read by the APM agent(s) located at the server(s), generating second information by assigning a score to the first information using machine learning procedure(s), classifying the data based on the second information. A data plugin(s) associated with the data can be determined based on the classification. A determination can be made as to whether the data is labeled as a data type. The second information can be generated based on (i) a log(s) associated with the data, (ii) transaction data associated with the data, (iii) server metrics associated with the data, (iv) a time stamp(s) associated with the data, (v) infrastructure data associated with the data, (vi) domain knowledge, or (vii) environment knowledge.

Utilizing application performance management automatic discovery data for plugin priority

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Ashburn, VA, United States of America

Kadhiresan Kanniyappan