Exponential moving maximum (EMM) filter for predictive analytics in network reporting

Patent metadata

Field	Value
Publication number	US-9602462-B2
Application number	US-201514689823-A
Country	US
Kind code	B2
Filing date	Apr 17, 2015
Priority date	Sep 11, 2014
Publication date	Mar 21, 2017
Grant date	Mar 21, 2017

Abstract

Techniques for an exponential moving maximum (EMM) filter for predictive analytics in network reporting are disclosed. In some embodiments, a process for predictive analytics in network reporting using an EMM filter includes pre-processing network-related data by performing exponential moving maximum (EMM) filtering on the network-related data; and determining predictive analytics based on the EMM filtered network-related data.

First claim

What is claimed is:

1. A system for predictive analytics in network reporting, comprising:
a processor configured to:
pre-process network-related data by performing exponential moving maximum (EMM) filtering on the network-related data, comprising to:
aggregate peak values of numbers of transactions per second (TPS) of network events over a plurality of periods of time to obtain the network-related data, a peak value relating to the maximum value of TPS of network events over a period of time; and
perform the EMM filtering on the network-related data; and

determine predictive analytics based on the EMM filtered network-related data, comprising to:
adjust a network capacity of a network based on the determined predictive analytics; and

a memory coupled to the processor and configured to provide the processor with instructions.

2. The system recited in claim 1 , wherein the network-related data includes monitored network events data.

3. The system recited in claim 1 , wherein the network-related data includes Domain Name System (DNS) data.

4. The system recited in claim 1 , wherein the network-related data includes Domain Name System (DNS) query and DNS response data.

5. The system recited in claim 1 , wherein to determine predictive analytics based on the EMM filtered network-related data includes to:
generate a trend prediction based on the EMM filtered network-related data.

6. The system recited in claim 1 , wherein the processor is further configured to:
generate a trend prediction based on the EMM filtered network-related data; and
generate a visualization of the trend prediction for display.

7. The system recited in claim 1 , wherein the processor is further configured to:
receive network-related data.

8. The system recited in claim 1 , wherein the processor is further configured to:
receive Domain Name System (DNS) data that is collected from an agent executed on a DNS appliance.

9. The system recited in claim 1 , wherein the processor is further configured to:
collect network-related data from one or more network devices.

10. The system recited in claim 1 , wherein the processor is further configured to:
collect network-related data from one or more network devices; and
store the network-related data in a data store.

11. A method of predictive analytics in network reporting, comprising:
pre-processing network-related data by performing exponential moving maximum (EMM) filtering on the network-related data, comprising:
aggregating peak values of numbers of transactions per second (TPS) of network events over a plurality of periods of time to obtain the network-related data, a peak value relating to the maximum value of TPS of network events over a period of time; and
performing the EMM filtering on the network-related data; and

determining predictive analytics based on the EMM filtered network-related data, comprising:
adjusting a network capacity of a network based on the determined predictive analytics.

12. The method of claim 11 , wherein the network-related data includes monitored network events data.

13. The method of claim 11 , wherein the network-related data includes Domain Name System (DNS) data.

14. The method of claim 11 , wherein the network-related data includes Domain Name System (DNS) query and DNS response data.

15. The method of claim 11 , further comprising:
generating a trend prediction based on the EMM filtered network-related data; and
generating a visualization of the trend prediction for display.

16. A computer program product for predictive analytics in network reporting, the computer program product being embodied in a tangible non-transitory computer readable storage medium and comprising computer instructions for:
pre-processing network-related data by performing exponential moving maximum (EMM) filtering on the network-related data, comprising:
aggregating peak values of numbers of transactions per second (TPS) of network events over a plurality of periods of time to obtain the network-related data, a peak value relating to the maximum value of TPS over a period of time; and
performing the EMM filtering on the network-related data; and

determining predictive analytics based on the EMM filtered network-related data, comprising:
adjusting a network capacity of a network based on the determined predictive analytics.

17. The computer program product recited in claim 16 , wherein the network-related data includes monitored network events data.

18. The computer program product recited in claim 16 , wherein the network-related data includes Domain Name Systems (DNS) data.

19. The computer program product recited in claim 16 , wherein the network-related data includes Domain Name Systems (DNS) query and DNS response data.

20. The computer program product recited in claim 16 , further comprising computer instructions for:
generating a trend prediction based on the EMM filtered network-related data; and
generating a visualization of the trend prediction for display.

Assignees

• Infoblox Inc

Inventors

• Yu Bin
• Smith Les
• Threefoot Mark

Classifications

• H04L41/147 Primary

  for predicting network behaviour · CPC title

• H04L41/40

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

• H04L43/20

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

• H04L41/0604

  using filtering, e.g. reduction of information by using priority, element types, position or time · CPC title

• H04L43/08

  Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters · CPC title