NETWORK ROUTING ADAPTATION BASED ON FAILURE PREDICTION

• US 20130010610A1
• Filed: 03/21/2011
• Published: 01/10/2013
• Est. Priority Date: 03/22/2010
• Status: Abandoned Application

• Current Assignee: British Telecommunications (BT Group Plc) (BT Group PLC)
• Original Assignee: British Telecommunications (BT Group Plc) (BT Group PLC)
• Inventors: Nauck, Detlef, Karthikeyan, Vidhyalakshmi
• Application Number:
  US13/636,417
• Publication Number:
  US 20130010610A1
• Time in Patent Office: Days
• US Class Current: 370/242
• CPC Class Codes: H04L 41/0213 Standardised network management protocols, e.g. simple network management protocol [SNMP]
• CPC Class Codes: H04L 41/046 comprising network management agents or mobile agents therefor
• CPC Class Codes: H04L 41/069 using logs of notifications; Post-processing of notifications
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 41/16 using machine learning or artificial intelligence
• CPC Class Codes: H04L 45/22 Alternate routing
• CPC Class Codes: H04L 45/28 using route fault recovery

Abstract

Embodiments of the invention provide a system that predicts network events and triggers a pre-emptive response. An example implementation predicts network link failures and creates a change in the network before the failure actually happens by instigating policy-based adjustment of routing parameters. In particular, an embodiment of the invention operates in two phases. In the first phase the historical operation of a network is observed (B.4.2), to determine observed relationships between link or cluster failures that have occurred, and subsequent failures of different links or clusters. From these observed relationships failure rules can be derived (B.4.4) that are then applied to control routing in the network during a second, control, phase. That is, in the second, control, phase, the derived failure rules are applied such that if a link or cluster failure occurs, then from the rules a prior knowledge of what additional links may fail in the next a time period is obtained, and remedial action can then be taken such as routing data traffic away from the links that are predicted to fail (B.4.6).

Claims

1. A method of routing data in a data network, comprising:

a) storing failure rules defining predicted consequent failure(s) of one or more network components in dependence on the occurrence of one or more antecedent network events;

b) monitoring operation of the data network so as to observe the occurrence of any of the antecedent network events;

c) in the case of an antecedent network event being observed, using the stored failure rule or rules relating to the observed antecedent network event to predict the future failure of one or more network components; and

d) controlling the network components so as to change the routing of data in the network in dependence on the prediction of future failure of one or more of the network components.

2. A method according to claim 1, wherein one or more of the failure rules relate an observed consequent failure of a network link to an observed antecedent failure of a network link, the observations being historical network observations.

3. A method according to claim 1, wherein one or more of the failure rules relate an observed consequent failure of a network component to one or more observed antecedent network events, the antecedent network events being one or more from the group comprising:

a network client application starting up;

present time and/or date; and

/or a network log message being generated.

4. A method according to claim 1, and further comprising storing failure rule confidence values, wherein a failure rule is used to predict a network failure in dependence on its confidence value.

5. A method according to claim 1, and further comprising validating a stored failure rule against network observation data obtained after the rule has been implemented so as to determine if said rule should still be applied.

6. A method according to claim 1, wherein the failure rules are generated by:

i) analysing historical network log data so as to identify a data set of tuples of first network component failures followed by second network component failures within a pre-determined time thereafter; and

ii) deriving the failure rules from the tuples in dependence on the frequency of occurrence of the identified tuples in the data set.

7. A method according to claim 6, wherein the deriving is performed using an association rule mining algorithm.

8. A method according to claim 1, wherein the failure rules are repeatedly updated in dependence on the observed network operation.

9. A method according to claim 1, wherein a hierarchy of failure rules is provided, comprising a plurality of sets of failure rules, each set relating to a part of the network, a set of failure rules belonging to a lower set in the hierarchy relating to a smaller part of the network than a set of rules belonging to a higher set in the hierarchy.

10. A method according to claim 9, wherein each set of failure rules is associated with a network management server responsible for a part of the network, a hierarchy of network management servers being provided corresponding to the hierarchy of the sets of failure rules, wherein a failure rule relating to network components in two or more parts of the network is administered by a network management server at a level in the hierarchy that encompasses the parts concerned.

11. A computer program or suite of computer programs so arranged such that when executed by a computer it/they cause the computer to operate according to claim 1.

12. A computer readable media storing the computer program or at least one of the suite of computer programs according to claim 11.

13. A system for routing data in a data network, comprising:

a) a rule store arranged to store failure rules defining predicted consequent failure(s) of one or more network components in dependence on the occurrence of one or more antecedent network events;

b) a network monitor arranged to monitor operation of the data network so as to observe the occurrence of any of the antecedent network events, and in the case of an antecedent network event being observed, use the stored failure rules or rules relating to the observed antecedent network event to predict the future failure of one or more network components; and

c) one or more network controllers arranged to change the routing of data in the network in dependence on the prediction of future failure of one or more of the network components.

14. A system, comprising:

a) means for storing failure rules defining predicted consequent failure(s) of one or more network components in dependence on the occurrence of one or more antecedent network events;

b) means for monitoring operation of the data network so as to observe the occurrence of any of the antecedent network events;

c) means for using the stored failure rules or rules relating to the observed antecedent network event to predict the future failure of one or more network components; and

d) means for controlling the network components so as to change the routing of data in the network in dependence on the prediction of future failure of one or more of the network components.

15. An apparatus, comprising:

at least one processor; and

at least one memory including computer program code

the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following;

a) store failure rules defining predicted consequent failure(s) of one or more network components in dependence on the occurrence of one or more antecedent network events;

b) monitor operation of the data network so as to observe the occurrence of any of the antecedent network events;

c) in the case of an antecedent network events being observed, use the stored failure rules or rules relating to the observed antecedent network event to predict the future failure of one or more network components; and

d) control the network components so as to change the routing of data in the network in dependence on the prediction of future failure of one or more of the network components.