APPLICATION PERFORMANCE ANALYTICS PLATFORM

• US 20200133814A1
• Filed: 10/25/2018
• Published: 04/30/2020
• Est. Priority Date: 10/25/2018
• Status: Active Grant

• Current Assignee: Capital One Services LLC (Capital One Financial Corporation)
• Original Assignee: Capital One Services LLC (Capital One Financial Corporation)
• Inventors: PRABATH, Anu, SINGAM, Ashish, BODALA, Manisha, SZERENYI, Laszlo, THORNTON, Eric
• Granted Patent: US 10,671,507 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 11/0709 in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems
• CPC Class Codes: G06F 11/0772 Means for error signaling, e.g. using interrupts, exception flags, dedicated error registers
• CPC Class Codes: G06F 11/302 where the computing system component is a software system
• CPC Class Codes: G06F 11/327 Alarm or error message display
• CPC Class Codes: G06F 11/3409 for performance assessment
• CPC Class Codes: G06F 11/3447 Performance evaluation by modeling
• CPC Class Codes: G06F 11/3466 Performance evaluation by tracing or monitoring
• CPC Class Codes: G06F 16/21 Design, administration or maintenance of databases
• CPC Class Codes: G06F 16/2228 Indexing structures
• CPC Class Codes: G06F 16/2379 Updates performed during online database operations; commit processing
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

Various embodiments are generally directed to techniques of collecting analyzing information on various types of applications in an environment by an application performance analytics platform and acting on the analyzed information. The application performance analytics platform may include at least a monitoring system, a time series database, and an APM tool collector, all of which may be programmatically interfaced with or connected to each other. The APM tool collector may access or pull APM metrics from one or more APM tools and input the metrics to the monitoring system. The time series database may access or pull log files and extract log data for analysis. Based on the analysis, the application performance analytics platform may generate one or more alerts.

Claims

1. A system for an application performance analytics platform, the system comprising:

at least one networking device; and

one or more computing devices connected to the at least one networking device, wherein the one or more computing devices comprises;

a memory to store instructions; and

processing circuitry, coupled with the memory, operable to execute the instructions, that when executed, cause the processing circuitry to;

access a monitoring system and a time series database;

interface, programmatically, the monitoring system with the series database;

interface, programmatically, an application performance management (APM) tool collector with the monitoring system;

pull or access, by the APM tool collector, a plurality of APM metrics from two or more APM tools to aggregate and input the plurality of APM metrics to the monitoring system;

pull or access a plurality of log files and input the plurality of log files to the time series database;

perform analysis, by the monitoring system, on at least the plurality of APM metrics;

extract, by the time series database, log data from the plurality of log files;

perform analysis on extracted log data; and

generate one or more alerts based on the analysis performed on the plurality of APM metrics and/or the analysis performed on the extracted log data.

2. The system of claim 1, wherein the processing circuitry is further caused to access, load, or communicate with a node exporter, wherein the node exporter measures one or more machine related resources from at least one cloud-based server, the one or more machine related resources including at least memory and central processing unit (CPU) utilization.

3. The system of claim 2, wherein the processing circuitry is further caused to access, load, or communicate with a cloud-based monitoring service exporter, wherein the cloud-based monitoring service exporter pulls or accesses monitoring data from a cloud-based monitoring service.

4. The system of claim 3, wherein the node exporter and the cloud-based monitoring service exporter are programmatically interfaced with the monitoring system and the measured one or more machine related resources from the at least one cloud-based server and the monitoring data from the cloud-based monitoring service are input to the monitoring system for analysis.

5. The system of claim 1, wherein the analysis performed on the extracted log data comprises:

applying analytics and/or machine learning on the extracted log data; and

providing one or more results of the applied analytics and/or machine learning to an open-source visualization tool.

6. The system of claim 5, wherein the one or more results of the applied analytics and/or machine learning comprises one or more of the following:

(i) a predicted trend, (ii) a future application workload forecast, (iii) historical transaction data, and (iv) an incident related to performance and/or capacity.

7. The system of claim 6, wherein the one or more alerts include an e-mail alert and/or an incident alert, and wherein the one or more alerts are based on any one of the one or more results of the applied analytics and/or machine learning on the extracted log data.

8. The system of claim 1, wherein one or more results of the analysis performed on the plurality of APM metrics, and/or the one or more alerts, are generated and displayed on a user interface.

9. The system of claim 3, wherein the APM tool collector is programmed in python and/or Java programming language, wherein the node exporter is programmed in GO programming language, and wherein the cloud-based monitoring service exporter is programmed in Java programming language.

10. The system of claim 1, wherein the analysis performed on the plurality of APM metrics by the monitoring system comprises comparing Key Process Indicators (KPIs) among the two or more APM tools across a plurality of environments.

11. The system of claim 1, wherein the analysis performed on at least the plurality of APM metrics by the monitoring system comprises comparing a first application infrastructure with a second application infrastructure based on one or more of the following:

(i) one or more tiers, (ii) a number of servers in each of the one or more tiers, (iii) one or more hardware specifications, (iv) connectivity between the one or more tiers and one or more nodes, and (v) one or more application process level resources.

12. The system of claim 1, wherein the time series database automatically captures data on one or more of the following:

(i) a concurrent set of users, (ii) a plurality of business transactions, (iii) a plurality of transaction usage patterns, and (iv) a transaction response time, and wherein the time series database generates a performance test scenario based on the automatically captured data.

13. The system of claim 1, wherein the application performance analytics platform is implemented in a Development Virtual Private Cloud (VPC) and connects to one or more production VPCs and/or one or more non-production VPCs, wherein the production VPCs are VPCs associated with one or more business units of an enterprise.

14. The system of claim 1, wherein the application performance analytics platform is cloud native.

15. An apparatus comprising:

a memory to store instructions; and

processing circuitry, coupled with the memory, operable to execute the instructions, that when executed, cause the processing circuitry to;

access a monitoring system;

access, load, or communicate with an application performance management (APM) tool collector, wherein the APM tool collector is programmatically interfaced with the monitoring system;

pull or access, by the APM tool collector, a plurality of APM metrics from two or more APM tools to aggregate and input the plurality of APM metrics to the monitoring system;

perform analysis, by the monitoring system, on at least the plurality of APM metrics;

receive, by the monitoring system, one or more results of analysis performed on log data, wherein the analysis on the log data is performed by a time series database; and

generate one or more alerts based on the performed analysis on the plurality of APM metrics and/or the one or more results of the analysis performed on the log data.

16. The apparatus of claim 15, wherein the processing circuitry is further caused to access, load, or communicate with:

(i) a node exporter, wherein the node exporter measures one or more machine related resources from at least one cloud-based server, the one or more machine related resources including at least memory and central processing unit (CPU) utilization, and

(ii) a cloud-based monitoring service exporter, wherein the cloud-based monitoring service exporter pulls monitoring data from a cloud-based monitoring service, and

wherein the measured one or more machine related resources from the at least one cloud-based server and the monitoring data from the cloud-based monitoring service are input to the monitoring system for analysis.

17. The apparatus of claim 15, wherein the apparatus is a cloud-based server computer device.

18. A non-transitory computer-readable storage medium storing computer-readable program code executable by a processor to:

pull or access, via an application performance (APM) tool collector, a plurality of APM metrics from two or more APM tools;

aggregate and input the plurality of APM metrics to a monitoring system;

pull or access a plurality of log files;

input the plurality of log files to a time series database;

perform analysis, via the monitoring system, on at least the plurality of APM metrics;

extract, via the time series database, log data from the plurality of log files;

perform analysis on extracted log data; and

generate one or more alerts based on the performed analysis on the plurality of APM metrics and/or the performed analysis on the extracted log data.

19. The non-transitory computer-readable storage medium of claim 18, wherein the analysis performed on the extracted log data comprises:

applying analytics and/or machine learning on the extracted log data; and

providing one or more results of the applied analytics and/or machine learning to an open-source visualization tool.

20. The non-transitory computer-readable storage medium of claim 18, wherein the one or more results of the applied analytics and/or machine learning comprises one or more of the following:

(i) a predicted trend, (ii) a future application workload forecast, (iii) historical transaction data, and (iv) an incident related to performance and/or capacity.