1 ai approaches to network fault management andrew learn 29 nov 2001

1

AI Approaches to Network Fault Management

Andrew Learn

29 Nov 2001

2

Outline

• Fault Management Process

• AI Approaches– Expert Systems– Neural Networks– Case-based Reasoning

3

Network Faults

• Hardware– Wear and tear– Cut cables– Improper installation

• Software– Incorrect design– Bugs– Incorrect data (e.g. routing tables)

4

Fault Management Process

1. Collect alarms

2. Filter and correlate alarms

3. Diagnose faults

4. Restoration and repair

5. Evaluate effectiveness

5

1. Collect Alarms

• Types of alarms– Physical: Failure in communication

• e.g. loss of signal, CRC failure

– Logical: Statistical values exceed threshold• e.g. number of packets dropped

• Communication with components– Control protocol: Simple Network Management

Protocol (SNMP)– Data format: Management Information Base (MIB-

II, 1990) has ~170 manageable objects

6

• Sample MIB Entry

• Sample SNMP “get” call

ipInReceives OBJECT-TYPE SYNTAX Counter ACCESS read-only STATUS mandatory DESCRIPTION "The total number of input datagrams received from interfaces, including those received in error." ::= { ip 3 }

snmpget netdev-kbox.cc.cmu.edu public system.sysUpTime.0

Name: system.sysUpTime.0 Timeticks: (2270351) 6:18:23

7

2. Filter and Correlate Alarms

• Filter– Eliminate redundant alarms– Suppress noncritical alarms– Inhibit low-priority alarms in presence of

high-priority alarms

• Correlate– Analyze and interpret multiple alarms to

assign new meaning (derived alarm)

8

3. Diagnose Faults

• May require additional tests/diagnostics on circuits or components– Automated or manual

• Analyze all info from alarms, tests, performance monitoring

• Identify smallest system module that needs to be repaired or replaced

9

4. Restoration and Repair

• Restoration: Continue service in presence of fault

– Switch over to spares– Reroute around trouble spot– Restore software or data from backup

• Repair– Replace parts– Repair cables– Debug software

• Retest to verify fault is eliminated

10

5. Evaluate Effectiveness

• Questions to answer :– How often do faults occur?– How many faults affect service?– How long is service interrupted?– How long to repair?

• Provides assessment of:– Performance of fault management system– Reliability of equipment

11

AI Approaches to Fault Management

• Well-developed approach:– Expert systems

• New approaches:– Neural networks– Case-based reasoning– Other

12

Why AI?

• Need for intelligence– Data analysis– Pattern recognition– Clustering and categorization– Problem solving

• Need for automation– Manual analysis/solution takes time– Limited manpower– Limited expertise

13

Well-developed approach: Expert Systems

• Expert systems = Rule-base + Working Memory• Three parts to rules:

1. Context trigger (when should rule be considered)2. Condition ( if X . . . )3. Conclusion ( . . . then Y)

• Used since 1980’s by major telecomm companies– Bell: Automated Cable Expertise (ACE) system– GTE: Central Office Maintenance Printout Analysis &

Suggestion System (COMPASS)– AT&T: Network Management Expert System

(NEMESYS)

14

Need for New Approaches

• Weaknesses of expert systems– Brittle in unforeseen situations– Cannot learn from experience– Hard to maintain (adding/deleting/modifying rules)– Knowledge acquisition bottleneck– Can’t handle incomplete or probabilistic data

• Factors driving new approach– Rapidly changing technology– Dynamic network topology– Network complexity– Competition, demand for QoS

15

Neural Nets

• Structure: input, hidden, output layers

• Training– Supervised: Input pattern & desired output– Unsupervised: Clustering of similar inputs

Input

Hidden

Output

weights

16

Neural Nets• Advantages

– Pattern matching & generalization– Fast & efficient– Trainable– Handles incomplete, ambiguous data

• Disadvantages– Black box– Lack of training data

17

Neural Net Example

• Example: Alarm correlation in cell phone networks (Univ of Hannover, Germany)

Base Stations

Mobile units

Base Station Controller

Switching Centers

BS2

BS1 MC

BSCMicrowave Links

Maintenance Center

18

Neural Net Example

BSC alarms

Initial Cause

• Test Results:

– 94 alarms

– 99.76% correct classification with up to 25% noise

ML-1 fault

ML-2 fault

BS-2 alarms

BS-1 alarms

.

.

.

.

.

.

19

Case-Based Reasoning

• Case-based reasoning = matching previous examples– Case library: Set of previous faults, diagnoses,

solutions– Usually based on “trouble ticket” help-desk

databases

• Design considerations:– What are key attributes of a case?– What attributes will be used to index & access a

case?

20

Case-Based Reasoning

• Advantages– Easier knowledge acquisition than expert

systems– Can learn by adding new cases– Doesn’t require extensive maintenance

• Disadvantages– Requires time-consuming user interaction – No help for first-time problems

21

Case-Based Reasoning ExampleCase 134

Problem Type: Performance

Description: High error rate in comm between POA-SP & DF

No access: Intermittent

Retrieval: Case 103 [Similarity = 0.69]

Description: 64kb line from VendorX drops big datagrams.

Additional Info requested: Is there loss of big datagrams in ping test? (Result: Yes)

Cause: Link 34 inside Bldg 207 was defective

Solution: Vendor replaced cabling.

22

Summary of 3 AI Methods• Expert systems

– If / then rules– Well-developed technology– Brittle, hard to maintain

• Neural networks– Output = weighted transform of inputs– Fast pattern matching, robust to noise– Black box, lack of training data

• Case-based systems– Trouble-ticket retrieval– Easy to build, maintain– Slower diagnosis, takes time to build

23

Other Approaches

• Bayesian networks– Model statistical probabilities and

dependence of faults

• Mobile intelligent agents– Independent software agents cooperate to

collect info, suggest solutions

24

Future Trends

• Proactive fault detection– Recognizing trouble signs and taking

corrective action before service degrades

• Hybrid systems– Multiple AI methods integrated