Systems and Methods for Monitoring Performance of Payment Networks Through Distributed Computing

Patent metadata

Field	Value
Publication number	US-2020320520-A1
Application number	US-202016908205-A
Country	US
Kind code	A1
Filing date	Jun 22, 2020
Priority date	Jul 16, 2014
Publication date	Oct 8, 2020
Grant date	—

Abstract

Systems and methods for use in monitoring performance of payment networks through use of distributed computing. One example method includes generating metrics and/or events associated with a deployed region of the agent, correlating the metrics and/or events over at least one time interval, the time interval dependent on at least one of historical data related to the deployed region and a known event, detecting, at the agent, at least one variance in the metrics and/or events over the at least one time interval based on a statistical analysis with at least one tolerance, and publishing sampled data, to an associated collector, based on at least one of a sampling rule and the at least on variance.

First claim

What is claimed is:

1 . A method for use in proactively remediating payment network degradation, the method comprising:
collecting, by a processing engine, new sampled data and variances in the new sampled data, from multiple agents deployed in a network, in response to a data query, the sampled data including response time data and/or resource utilization data, the multiple agents remote from the processing engine, wherein the new sampled data is indicative of performance of the network and utilization of devices connected to the network, and wherein the new sampled data is new since a last data query, whereby the new sampled data and the variances are collected from the multiple agents to which processing of the data is distributed; determining, by the processing engine, at least one dependency between sets of metrics and/or events in the new sampled data from at least one of the multiple agents; performing, by the processing engine, real-time continuous regression analysis on the new sampled data; predicting, by the processing engine, through predictive analytics on the at least one dependency and the real-time continuous regression analysis, at least one future variance associated with the network based on a pattern occurring in the new sampled data; altering, by the processing engine, a remediation rule based on the predicted at least one future variance, the remediation rule indicating at least one action to be taken by at least one of the multiple agents from which the new sampled data was collected in order to address the predicted at least one future variance; and transmitting, by the processing engine, the remediation rule to the at least one of the multiple agents from which the new sampled data was collected, whereby the at least one of the multiple agents receives the remediation rule and is permitted to address the predicted at least one future variance based on the remediation rule.

2 . The method of claim 1 , further comprising deploying the multiple agents to each of multiple computing devices associated with the network.

3 . The method of claim 1 , wherein collecting the new sampled data includes collecting the new sampled data and other data from the multiple agents, via at least one collector; and
wherein the at least one dependency is not based on the other data.

4 . The method of claim 1 , wherein collecting the new sampled data includes:
receiving, by a collector from the multiple agents, data related to payment transactions; aggregating, by the collector, based on time and/or distribution of the multiple agents, the data, events received from at least some of the multiple agents, and/or a remedial action associated with at least one of the multiple agents; determining, by the collector, at least one variance based on at least one of the events received from the multiple agents; and publishing, by the collector to the processing engine, the at least one variance and the sampled data.

5 . The method of claim 1 , further comprising creating content aware clusters across multiple types and/or classes of the multiple agents and metrics associated with said multiple agents.

6 . The method of claim 1 , wherein altering the remediation rule includes appending the remediation rule to a set of remediation rules, said remediation rule directing at least one of the multiple agents to route transaction data away from one or more other of multiple agents.

7 . The method of claim 2 , wherein each of the multiple computing devices is a point of sale terminal.

8 . A system for use in proactively remediating payment network degradation, the system comprising:
one or more computing devices for connection to multiple agents deployed in association with a payment network, wherein the multiple agents are geographically distributed from the one or more computing devices, wherein the sampled data is related to payment transactions processed by the payment network, and wherein the one or more computing devices include computer executable instructions embodied therein defining at least one collector and a processing engine; wherein the at least one collector is configured to:
receive, from the multiple agents, the sampled data relating to the payment transactions, the sampled data including response time data and/or resource utilization data; and
provide at least a portion of the sampled data to the processing engine; and
wherein the processing engine is configured to:
determine at least one dependency between sets of metrics and/or events in the sampled data received from the at least one collector;
perform real-time continuous regression analysis on the sampled data;
predict, through predictive analytics on the at least one dependency and the real-time continuous regression analysis, at least one future variance associated with the payment network based on a pattern occurring in the sampled data;
alter a remediation rule based on the predicated at least one future variance, the remediation rule indicating at least one action to be taken by at least one of the multiple agents in order to address the predicted at least one future variance; and
transmit the remediation rule to at least one of the multiple agents.

9 . The system of claim 8 , wherein the at least one collector is further configured to:
aggregate the sampled data, at least one event received from the multiple agents, and at least one remedial action associated with at least one of the multiple agents; and determine at least one variance based on regression analysis of the sampled data and at least one of the at least one event and the at least one remedial action; and wherein the at least a portion of the sampled data includes the at least one variance and the aggregated sampled data associated with the at least one variance; and wherein the at least one dependency is based on the at least one variance.

10 . The system of claim 9 , wherein the at least one collector is configured to aggregate the sampled data based on time and/or distribution of the multiple agents.

11 . The system of claim 9 , wherein the one or more computing devices include a distributed storage memory data grid;
wherein the at least one collector is configured to store the aggregated sampled data in the distributed storage memory data grid.

12 . A computer-implemented method for use in proactively remediating payment network degradation in a payment network, the payment network including multiple computing devices distributed across a geographic region, the method comprising:
receiving, by a collector computing device, sampled data relating to payment transactions, from multiple agents deployed in association with the payment network, the sampled data including response time data and/or resource utilization data, the multiple agents including an application agent, a Platform as a Service (PaaS) agent, and an Infrastructure as a Service (IaaS) agent; aggregating, by the collector computing device, based on learned time intervals and distribution of the multiple agents, the sampled data, events received from at least some of the multiple agents, and a remedial action associated with at least one of the multiple agents; determining, at the collector computing device, at least one variance based on at least one of the events over the learned time intervals; and publishing, to a processing engine, the at least one variance and the aggregated data, whereby the processing engine receives the published at least one variance and the aggregated data to perform predictive analytics and determine whether to alter a remedial rule for use by at least one of the multiple agents to determine an action to

Assignees

• Mastercard International Inc

Inventors

• Sidhu Navjot Singh
• Hibbeler Craig
• Bhuvanagiri Vijayanath K
• Tsivtsivadze Revaz
• Dukkipati Narendra

Classifications

• G06Q20/38 Primary

  Payment protocols; Details thereof · CPC title

• G06Q20/4016

  involving fraud or risk level assessment in transaction processing · CPC title