IP Library Granted Patent US 10,726,590
Granted Patent B2
US 10,726,590 · App. 15/339,271 · Granted Jul 28, 2020

Correlate application performance with hardware performance via heatmap

Inventors: Harish Nataraj (Berkeley, CA); Dhairyashil Dilip Padalkar (San Francisco, CA); Payman Kamali (San Francisco, CA); Divyanshu Mittal (San Francisco, CA); Suneet Lawrence Saldanha (San Francisco, CA); Zachary Julian Sherman (San Francisco, CA)
Assignee: Cisco Technology, Inc.
G06T11/206
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 10,726,590
App. No.
15/339,271
Granted
Jul 28, 2020
Kind
B2
Abstract

In one aspect, a system for correlating application performance data with hardware performance data via heat maps is disclosed. The system includes: a processor; a memory; and one or more modules stored in the memory and executable by a processor to perform operations. The operations include: receive data associated with monitored applications and hardware; identify application performance data and hardware performance data from the received data; generate interactive heat maps of the application performance data and interactive heat maps of the hardware performance data; provide a user interface for displaying the generated heat maps; and display, through the user interface, the generated heat maps of the application performance data and the generated heat maps of the hardware performance data using a common time scale.

Claims (63)

1. A system for correlating application performance data with hardware performance data, the system including:

a processor;

a memory; and

one or more modules stored in the memory and executable by a processor to perform operations including:

receive data associated with monitored applications and hardware configured to perform a monitored business transaction;

identify application performance data and hardware performance data associated with the monitored business transaction from the received data;

generate interactive heat maps of the application performance data and interactive heat maps of the hardware performance data, wherein, in a correlated view, a mapping of the interactive heat maps of the application performance data to the interactive heat maps of the hardware performance data is shown as a correlation between application performance and hardware performance;

provide a user interface for displaying the generated heat maps;

display, through the user interface, the generated heat maps of the application performance data and the generated heat maps of the hardware performance data using a common time scale;

receive, through the user interface, a selection of multiple blocks from the application performance heat maps or the hardware performance heat maps;

responsive to the received selection of the multiple blocks, identify one or more common entities that correlate to the selection of the multiple blocks, the common entities being from the hardware configured to perform the monitored business transaction; and

display, through the user interface, one or more blocks from the hardware performance heat maps or the application performance heat maps based on the received selection of the multiple blocks that represent the identified one or more common entities.

2. The system of claim 1 , wherein when generating the interactive heat maps, the one or more modules are executable to perform operations including:

generate interactive heat maps for a specific application performance data metric or a specific hardware performance metric or both a specific application performance data metric and a specific hardware performance metric.

3. The system of claim 2 , wherein the specific application performance data metric includes call counts.

4. The system of claim 2 , wherein the specific hardware performance data metric includes CPU usage percentage, memory usage percentage, or both CPU usage percentage and memory usage percentage.

5. The system of claim 1 , wherein the one or more modules are executable to perform operations including:

receive a selection of a block from the application performance heat maps or the hardware performance heat maps; and

responsive to the received selection of the block, display one or more blocks from the hardware performance heat maps or one or the application performance heat maps based on the received selection of the block to visually indicate the correlation.

6. The system of claim 5 , wherein the one or more modules are executable to visually indicate the correlation by performing operations including:

apply highlights, colors, shading, brightness, transparency, or text to indicate the correlation between the selection of the block and the one or more or blocks.

7. The system of claim 5 , wherein the one or more modules are executable to perform operations including:

display additional information to identify the selection of the block and the one or more correlated blocks.

8. A method for correlating application performance data with hardware performance data, the method including:

receiving data associated with monitored applications and hardware configured to perform a monitored business transaction;

identifying application performance data and hardware performance data associated with the monitored business transaction from the received data;

generating interactive heat maps of the application performance data and interactive heat maps of the hardware performance data, wherein, in a correlated view, a mapping of the interactive heat maps of the application performance data to the interactive heat maps of the hardware performance data is shown as a correlation between application performance and hardware performance;

providing a user interface for displaying the generated heat maps;

displaying, through the user interface, the generated heat maps of the application performance data and the generated heat maps of the hardware performance data using a common time scale;

receiving, through the user interface, a selection of multiple blocks from the application performance heat maps or the hardware performance heat maps;

responsive to receiving the selection of the multiple blocks, identifying one or more common entities that correlate to the selection of the multiple blocks, the common entities being from the hardware configured to perform the monitored business transaction; and

displaying, through the user interface, one or more blocks from the hardware performance heat maps or the application performance heat maps based on the received selection of the multiple blocks that represent the identified one or more common entities.

9. The method of claim 8 , wherein generating the interactive heat maps includes:

generating interactive heat maps for a specific application performance data metric or a specific hardware performance metric or both a specific application performance data metric and a specific hardware performance metric.

10. The method of claim 9 , wherein the specific application performance data metric includes call counts.

11. The method of claim 9 , wherein the specific hardware performance data metric includes CPU usage percentage, memory usage percentage, or both CPU usage percentage and memory usage percentage.

12. The method of claim 8 , including:

receiving a selection of a block from the application performance heat maps or the hardware performance heat maps; and

responsive to receiving the selection of the block, displaying one or more blocks from the hardware performance heat maps or one or the application performance heat maps based on the received selection of the block to visually indicate the correlation.

13. The method of claim 12 , including:

applying highlights, colors, shading, brightness, transparency, or text to indicate the correlation between the selection of the block and the one or more correlated blocks.

14. The method of claim 12 , including:

displaying additional information to identify the selection of the block and the one or more correlated blocks.

15. A non-transitory computer readable medium embodying instructions when executed by a processor to cause operations to be performed for correlating application performance data with hardware performance data, the operations including:

receiving data associated with monitored applications and hardware configured to perform a monitored business transaction;

identifying application performance data and hardware performance data associated with the monitored business transaction from the received data;

generating interactive heat maps of the application performance data and interactive heat maps of the hardware performance data, wherein, in a correlated view, a mapping of the interactive heat maps of the application performance data to the interactive heat maps of the hardware performance data is shown as a correlation between application performance and hardware performance;

providing a user interface for displaying the generated heat maps;

displaying, through the user interface, the generated heat maps of the application performance data and the generated heat maps of the hardware performance data using a common time scale;

receiving, through the user interface, a selection of multiple blocks from the application performance heat maps or the hardware performance heat maps;

responsive to receiving the selection of the multiple blocks, identifying one or more common entities that correlate to the selection of the multiple blocks, the common entities being from the hardware configured to perform the monitored business transaction; and

displaying, through the user interface, one or more blocks from the hardware performance heat maps or the application performance heat maps based on the received selection of the multiple blocks that represent the identified one or more common entities.

16. The non-transitory computer readable medium of claim 15 , wherein generating the interactive heat maps includes:

generating interactive heat maps for a specific application performance data metric or a specific hardware performance metric or both a specific application performance data metric and a specific hardware performance metric.

17. The non-transitory computer readable medium of claim 16 , wherein the specific application performance data metric includes call counts.

18. The non-transitory computer readable medium of claim 16 , wherein the specific hardware performance data metric includes CPU usage percentage, memory usage percentage, or both CPU usage percentage and memory usage percentage.

19. The non-transitory computer readable medium of claim 15 , wherein the operations include:

receiving a selection of a block from the application performance heat maps or the hardware performance heat maps; and

responsive to receiving the selection of the block, displaying one or more blocks from the hardware performance heat maps or one or the application performance heat maps based on the received selection of the block to visually indicate the correlation.

20. The non-transitory computer readable medium of claim 19 , wherein the operations include:

applying highlights, colors, shading, brightness, transparency, or text to indicate the correlation between the selection of the block and the one or more correlated blocks.

21. The non-transitory computer readable medium of claim 19 , wherein the operations include:

displaying additional information to identify the selection of the block and the one or more correlated blocks.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 10, 2017
From: APPDYNAMICS LLC
To: CISCO TECHNOLOGY, INC.
Reel/Frame 044173/0050 →
CHANGE OF NAME Recorded Jun 23, 2017
From: APPDYNAMICS, INC.
To: APPDYNAMICS LLC
Reel/Frame 042964/0229 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 28, 2017
From: NATARAJ, HARISH; PADALKAR, DHAIRYASHIL DILIP; KAMALI, PAYMAN; MITTAL, DIVYANSHU; SALDANHA, SUNEET LAWRENCE; SHERMAN, ZACHARY JULIAN
To: APPDYNAMICS, INC.
Reel/Frame 041403/0947 →
Continuity (1)
Related Publication 20180122110A1 · May 3, 2018
Cited By (1)
US 12,229,136