PROACTIVE OPERATIONS, ADMINISTRATION, AND MAINTENANCE SYSTEMS AND METHODS IN NETWORKS USING DATA ANALYTICS

• US 20150317197A1
• Filed: 05/05/2014
• Published: 11/05/2015
• Est. Priority Date: 05/05/2014
• Status: Active Grant

• Current Assignee: Innovation Asset Collective
• Original Assignee: Ciena Corporation
• Inventors: Blair, Loudon Thomas
• Granted Patent: US 9,628,340 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 11/079 Root cause analysis, i.e. error or fault diagnosis in a hardware test environment G06F11/22; in a software test environment G06F11/36
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 41/149 for prediction of maintenance
• CPC Class Codes: H04L 41/16 using machine learning or artificial intelligence
• CPC Class Codes: H04L 41/40 using virtualisation of network functions or resources, e.g. SDN or NFV entities

Abstract

A computer-implemented method, a system, and a network include receiving network data from a network and non-network sourced data from one or more external sources relative to the network; performing data mining on the network data and the non-network sourced data; developing a predictive analytics model based on the data mining; and performing predictive analytics on the network data and the non-network sourced data using the predictive analytics model to detect likely future failures in the network. The network can include a Software Defined Network (SDN) operating at any of Layers 0, 1, 2 and/or 3.

Claims

1. A computer-implemented method, comprising:

receiving network data from a network and non-network sourced data from one or more external sources relative to the network;

performing data mining on the network data and the non-network sourced data;

developing a predictive analytics model based on the data mining; and

performing predictive analytics on the network data and the non-network sourced data using the predictive analytics model to detect likely future failures in the network.

2. The computer-implemented method of claim 1, wherein the network comprises a Software Defined Network (SDN) operating at any of Layers 0, 1, 2 and/or 3.

3. The computer-implemented method of claim 2, further comprising:

receiving the network data from the network via an SDN controller in the network via an Application Programming Interface (API) on the SDN controller.

4. The computer-implemented method of claim 1, further comprising:

receiving a notification from the predictive analytics that a component in the network is likely to fail; and

performing a proactive maintenance activity on the component.

5. The computer-implemented method of claim 1, further comprising:

continually updating the data mining and the predictive analytics model based on ongoing occurrences of failures and data associated therewith.

6. The computer-implemented method of claim 1, wherein the network data and the non-network data are each classified as either analog or digital and either internal to the network or external to the network and prioritized.

7. The computer-implemented method of claim 6, wherein the network data comprises network Operations, Administration, and Maintenance (OAM) data collected by the network, and the non-network data comprises data collected from external sources relative to the network.

8. A system, comprising:

a network interface, a data store, and a processor, each communicatively coupled therebetween; and

memory storing instructions that, when executed, cause the processor to;

receive, via the network interface, network data from a network and non-network sourced data from one or more external sources relative to the network;

perform data mining on the network data and the non-network sourced data;

develop a predictive analytics model based on the data mining; and

perform predictive analytics on the network data and the non-network sourced data using the predictive analytics model to detect likely future failures in the network.

9. The system of claim 8, wherein the network comprises a Software Defined Network (SDN) operating at any of Layers 0, 1, 2 and/or 3.

10. The system of claim 9, wherein the network data is received from the network via an SDN controller in the network via an Application Programming Interface (API) on the SDN controller.

11. The system of claim 8, wherein, responsive to a failure prediction by the predictive analytics, a notification is provided when a component in the network is likely to fail to alert an operator for proactive maintenance activity on the component.

12. The system of claim 8, wherein the memory storing instructions that, when executed, cause the processor to:

continually update the data mining and the predictive analytics model based on ongoing occurrences of failures and data associated therewith.

13. The system of claim 8, wherein the network data and the non-network data are each classified as either analog or digital and either internal to the network or external to the network and prioritized.

14. The system of claim 13, wherein the network data comprises network Operations, Administration, and Maintenance (OAM) data collected by the network, and the non-network data comprises data collected from external sources relative to the network.

15. A network, comprising:

a plurality of network elements communicatively coupled therebetween and operating at any of Layers 0, 1, 2 and/or 3;

a controller communicatively coupled to one or more of the plurality of network elements, wherein the controller comprises a processor and memory storing instructions that, when executed, cause the processor to;

receive, via a network interface, network data from a network and non-network sourced data from one or more external sources relative to the network;

perform data mining on the network data and the non-network sourced data;

develop a predictive analytics model based on the data mining; and

perform predictive analytics on the network data and the non-network sourced data using the predictive analytics model to detect likely future failures in the network.

16. The network of claim 15, wherein the controller comprises a Software Defined Network (SDN) controller, and wherein the network data is received from the network via the SDN controller in the network via an Application Programming Interface (API) on the SDN controller.

17. The network of claim 15, wherein, responsive to a failure prediction by the predictive analytics, a notification is provided when a component in the network is likely to fail to alert an operator for proactive maintenance activity on the component.

18. The network of claim 15, wherein the memory storing instructions that, when executed, cause the processor to:

continually update the data mining and the predictive analytics model based on ongoing occurrences of failures and data associated therewith.

19. The network of claim 15, wherein the network data and the non-network data are each classified as either analog or digital and either internal to the network or external to the network and prioritized.

20. The network of claim 19, wherein the network data comprises network Operations, Administration, and Maintenance (OAM) data collected by the network, and the non-network data comprises data collected from external sources relative to the network.