System and method for operational intelligence based on network traffic

Patent metadata

Field	Value
Publication number	US-11848833-B1
Application number	US-202217977732-A
Country	US
Kind code	B1
Filing date	Oct 31, 2022
Priority date	Oct 31, 2022
Publication date	Dec 19, 2023
Grant date	Dec 19, 2023

Abstract

System and computer-implemented method for analyzing software-defined data center (SDDC) components in a computing environment uses network traffic data, which is correlated with an inventory of SDDC components in the computing environment to calculate a metric collection parameter for each SDDC component in the computing environment based on data flow associated with that SDDC component. Relevant metrics from each of the SDDC components in the computing environment are collected according to the metric collection parameter for that SDDC component to analyze the SDDC components.

First claim

What is claimed is:

1. A computer-implemented method for analyzing software-defined data center (SDDC) components in a computing environment, the method comprising:
monitoring network traffic in the computing environment to produce network traffic data;
correlating the network traffic data for the computing environment with an inventory of SDDC components in the computing environment to calculate a metric collection parameter for each SDDC component in the computing environment based on data flow associated with that SDDC component, wherein an amount of metrics collected for each SDDC component is dependent on a corresponding metric collection parameter for that SDDC component;
collecting relevant metrics from each of the SDDC components in the computing environment according to the metric collection parameter for that SDDC component; and
analyzing the SDDC components in the computing environment using the relevant metrics for the SDDC components.

2. The computer-implemented method of claim 1 , wherein correlating the network traffic data for the computing environment includes calculating a frequency of metric collection for each SDDC component in the computing environment based on the data flow associated with that SDDC component.

3. The computer-implemented method of claim 2 , wherein calculating the frequency of metric collection includes calculating a first frequency of metric collection for a first SDDC component in the computing environment when the data flow associated with the first SDDC component exceeds a threshold and calculating a second frequency of metric collection for a second SDDC component in the computing environment when the data flow associated with the second SDDC component does not exceed the threshold, wherein the first frequency is higher frequency than the second frequency.

4. The computer-implemented method of claim 1 , wherein correlating the network traffic data for the computing environment includes calculating a granularity of metrics to be collected for each SDDC component in the computing environment based on the data flow associated with that SDDC component.

5. The computer-implemented method of claim 4 , wherein calculating the granularity of metrics to be collected includes calculating a first granularity of metrics to be collected for a first SDDC component in the computing environment when the data flow associated with the first SDDC component exceeds a threshold and a second granularity of metrics to be collected for a second SDDC component in the computing environment when the data flow associated with the second SDDC component does not exceed the threshold, wherein the first granularity of metrics to be collected includes deeper level of metrics than the second granularity of metrics to be collected.

6. The computer-implemented method of claim 1 , further comprising grouping the SDDC components based on network traffic pattern to provide contextual health view of the SDDC components.

7. The computer-implemented method of claim 1 , wherein the SDDC components include virtual machines, containers or container clusters.

8. The computer-implemented method of claim 1 , wherein the metrics include CPU capacity usage, disk throughput usage and memory capacity usage.

9. A non-transitory computer-readable storage medium containing program instructions for analyzing software-defined data center (SDDC) components in a computing environment, wherein execution of the program instructions by one or more processors causes the one or more processors to perform steps comprising:
monitoring network traffic in the computing environment to produce network traffic data;
correlating the network traffic data for the computing environment with an inventory of SDDC components in the computing environment to calculate a metric collection parameter for each SDDC component in the computing environment based on data flow associated with that SDDC component, wherein an amount of metrics collected for each SDDC component is dependent on a corresponding metric collection parameter for that SDDC component;
collecting relevant metrics from each of the SDDC components in the computing environment according to the metric collection parameter for that SDDC component; and
analyzing the SDDC components in the computing environment using the relevant metrics for the SDDC components.

10. The non-transitory computer-readable storage medium of claim 9 , wherein correlating the network traffic data for the computing environment includes calculating a frequency of metric collection for each SDDC component in the computing environment based on the data flow associated with that SDDC component.

11. The non-transitory computer-readable storage medium of claim 10 , wherein calculating the frequency of metric collection includes calculating a first frequency of metric collection for a first SDDC component in the computing environment when the data flow associated with the first SDDC component exceeds a threshold and calculating a second frequency of metric collection for a second SDDC component in the computing environment when the data flow associated with the second SDDC component does not exceed the threshold, wherein the first frequency is higher frequency than the second frequency.

12. The non-transitory computer-readable storage medium of claim 9 , wherein correlating the network traffic data for the computing environment includes calculating a granularity of metrics to be collected for each SDDC component in the computing environment based on the data flow associated with that SDDC component.

13. The non-transitory computer-readable storage medium of claim 12 , wherein calculating the granularity of metrics to be collected includes calculating a first granularity of metrics to be collected for a first SDDC component in the computing environment when the data flow associated with the first SDDC component exceeds a threshold and a second granularity of metrics to be collected for a second SDDC component in the computing environment when the data flow associated with the second SDDC component does not exceed the threshold, wherein the first granularity of metrics to be collected includes deeper level of metrics than the second granularity of metrics to be collected.

14. The non-transitory computer-readable storage medium of claim 9 , wherein the steps further comprise grouping the SDDC components based on network traffic pattern to provide contextual health view of the SDDC components.

15. The non-transitory computer-readable storage medium of claim 9 , wherein the SDDC components include virtual machines, containers or container clusters.

16. The non-transitory computer-readable storage medium of claim 9 , wherein the metrics include CPU capacity usage, disk throughput usage and memory capacity usage.

17. A system comprising:
memory; and
at least one processor configured to:
monitor network traffic in a computing environment to produce network traffic data;
correlate the network traffic data for the computing environment with an inventory of SDDC components in the computing environment to calculate a metric collection parameter for each SDDC component in the computing environment based on data flow associated with that SDDC component, wherein an amount of metrics collected for each SDDC component is dependent on a corresponding metric collection parameter for that SDDC component;
collect relevant metrics from each of the SDDC components in the computing environment according to the metric collection parameter for that SDDC component; and
analyze the SDDC components in the computing e

Assignees

• Vmware Inc

Inventors

• Govindaraju Agila
• Dhamale Rutuja Ganesh

Classifications

• H04L43/02 Primary

  Capturing of monitoring data · CPC title

• H04L41/40

  using virtualisation of network functions or resources, e.g. SDN or NFV entities · CPC title

• H04L43/04

  Processing captured monitoring data, e.g. for logfile generation · CPC title

• H04L43/20 Primary

  the monitoring system or the monitored elements being virtualised, abstracted or software-defined entities, e.g. SDN or NFV · CPC title

• H04L43/0876

  Network utilisation, e.g. volume of load or congestion level · CPC title