Benefits of enhanced event analysis in data center OTDR testing

Mark MillerDr. Fang Xu

AFL/Noyes Test & Inspection1

Topics• Overview• Challenges• Techniques and Improvements

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EVENT ANALYSIS OVERVIEWBenefits of enhanced event analysis in data center OTDR testing

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Data Center Architecture• Characteristics

– Huge number of fiber runs– Structured cabling uses short jumpers and contains many connectors– Short networks with closely spaced events

• OTDR Requirements– Short Dead Zones are required to characterize fibers and locate faults– Auto event analysis needed to cope with large number of cables

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What are Events?• Additional structures along a fiber optic

network, such as connectors, fusion splices, optical splitter or macro-bends are called events

• These additional structures leave characteristic “foot prints” on a trace acquired by an Optical Time-Domain Reflectometer (OTDR)

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Events on OTDR Trace and their Measurements

Event Measurements•Location•Type of Event•Reflectivity•Loss

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Why Measure Events?• “Tier 2” OTDR testing required by standards

– Data Center: Standard ANSI/BICSI 002-2011 Data Center Design and Implementation Best Practices

– TIA 568-C.3 Connector Spec– ISO/IEC 11801:2010

• Baseline documentation of networks– Certification reports– Future troubleshooting

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OTDR Event AnalysisAuto Event Analysis

– Computerized finding and measuring of events in optical network from OTDR trace is called Auto Event Analysis

– Fully automatic Event Analysis allows a consistent interpretation of events regardless of user expertise

Manual Trace Analysis– Use of cursors across trace– Time consuming– Subject to human error

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If you can’t trust the accuracy of an OTDR’s event analysis:If you can’t trust the accuracy of an OTDR’s event analysis:

Manual Event Trace Analysis

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Move cursors and zoom for each event

EVENT ANALYSIS CHALLENGESBenefits of enhanced event analysis in data center OTDR testing

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Event Analysis Challenges• Incomplete knowledge of network• Time required to analyze trace• Closely spaced events

– Connector followed by connector– Connector followed by splice– Splice followed by connector

• Accurate determination of start and end of fiber under test– Use of launch and receive cables– Necessity of running a separate calibration test to compensate for length

variations (+/- 10m)• Event types

– Connectors– Splices– Splitters– Macrobends

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Why Event Analysis Misses Events• Perfect event analysis is not achievable• It is hard to find all events in practice as we also want

limit false events• What conditions can trick auto event analysis?

– Small event magnitude – hard to separate from noise– Close events – limited by used pulse-width– Event sequence – i.e.. non-reflective followed by a

reflective event– Hardware limitation - event during saturation recovery – Algorithm problem – usually seen as missing obvious event

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1.5 dB

EDZ

The OTDR Trace: Dead Zones

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• Event Dead Zone At 1.5dB down from peak

reflection(unsaturated event) where the user can accurately measure the distance between 2 events.

• Attenuation Dead Zone Distance from the start of the event

to the point where the power following the peak reflectance has returned to within 0.5 dB above the level of backscatter

Effect of ADZ on Trace EventsSeparated and “overlapping” events

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Events with similar reflectance

Separated

Overlapping

Events with different reflectance

Separated

Overlapping

No Industry Event Analysis Specification

• There is no standard method for specifying the performance of auto event analysis

• Good event analysis performance is taken for granted by OTDR users

• Dead zone specifications only apply to the trace

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Event Analysis Performance Evaluation

• Matched event rate: Are all actual events being detected? This should be as high as possible, with 100% being perfect.

• False event rate: Are false events being detected? This is usually due to noise spikes. This should be as low as possible, with 0% being perfect

• Pass rate: Are the results repeatable when the same network is tested multiple times? This should be as high as possible, with 100% being perfect.

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Satisfactio

n

Frustratio

n

Confidence

Tools for Event Analysis Evaluation

What people see is a tip of an iceberg: Lab and field testing

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Fiber networksGolden trace library

Traces collected from different units

Event Regression tool

Performance comparison

Networks description

EVENT ANALYSIS TECHNIQUES AND IMPROVEMENTS

Benefits of enhanced event analysis in data center OTDR testing

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Traditional Event Analysis

• Based on apparent event magnitude– Insertion loss– Reflectance

• User sets threshold for these parameters– If greater than threshold, then it is an

event

• Basic methods used inside event analysis– Gradient (derivative)– Linear regression– Correlation

Implementation

• Accuracy depending on position– Noise increases as event getting further

from launch

• Extra user intervention needed– Highly depends on user’s knowledge and

skill

• Technical limitations– Derivative is sensitive to noise– Need smart way to determine where a

segment starts and ends to apply linear regression

– There is no universal model of events to run correlation

Limitations

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Traditional user model allows the user to chose limits but can miss small loss event in this example

0.08dB 0.11dB

Traditional Event Analysis Algorithm

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Gradient (derivative) Derivative is sensitive to noise

Linear regression Determine segment start and end is challenging

Correlation There is no universal event model to correlate with before finding events

Traditional Event Analysis

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Traditional user model allows the user to chose limits but can miss small loss event in this example

0.08dB 0.11dB

Implementation Limitations

Based on apparent event magnitudeInsertion lossReflectance

Accuracy depending on positionNoise increases as event getting further from launch

User sets parameter thresholdIf greater than threshold, then it is considered an event

Extra user interventionHighly depends on user’s knowledge and skill

Dead-end for Magnitude Based EAExample of true event and false event found at different threshold using a major OTDR market player

•A slight increase of few event at smaller magnitude raises chance of exponentially increasing false events•1 : 12 is true event to false event increase ratio!•Hard to make further progress due to excessive false event

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Threshold Matched events % False events %0.1dB 60.33 11.54

0.01dB 61.63 27.08

Introduction of Likelihood• Wikipedia

– Likelihood is a function of how likely an event is, which is weaker than probability (or odds in favor).

– In statistics, a likelihood function is a function of the parameters of a statistical model, defined as follows: the likelihood of a set of parameter values given some observed outcomes is equal to the probability of those observed outcomes given those parameter values.

• Wolfram MathWorld– Likelihood is the hypothetical probability that an event

that has already occurred would yield a specific outcome. The concept differs from that of a probability in that a probability refers to the occurrence of future events, while a likelihood refers to past events with known outcomes.

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Likelihood Applied to EA• How event Likelihood is measured

– Expected noise level surrounding event– Shape of event– Magnitude of event– Slope of line segments surrounding event– Etc.

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Smaller magnitudeHigher likelihood

Larger magnitudelower likelihood

Benefits of New ApproachUser can trade-off of sensitivity or reliability by setting likelihood threshold after event analysis

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Default value is balanced between

sensitivity and reliability

Experienced user can choose higher sensitivity if he feels expected event is missing

High reliability is useful in certain cases.

False events

True events

Completely New Event AnalysisEvent analysis built from ground-upMultiple factors are taken into account

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Threshold Matched events % False events %0.1dB 73.42 12.29

0.01dB 74.83 12.83

True event and false event found at different threshold using new algorithm for same test networks

True event to false event increase ratio is 1 : 0.38 !It is still on it’s way for further progress

F @ 0.01dB

Event Analysis Quality Comparison Among OTDRs

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Found event %

False event %

Using magnitude as event criteria has exponential increase on false events(Slope > 45)

Tier 1 performer

Tier 2 performer

Tier 3 performerUsing likelihood as event criteria limits false events(Slope < 45)

D @ 0.02 dB

M310 @ 0.01dB

Default loss threshold at 0.1dB

Increasing matched events with moderate increase of false events is challenging M310 @0.07 dB

Prefect Score on 16-Event Network

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M310 – 16 events (prefect score) OTDR “A” – 29 Events

Application: Jumper Cable Testing Using M310

• Reflectance and loss measured at both ends of cable using estimation algorithm

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2 m jumper cable

Application: Data Center Network Using M310

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9-Event Network: Closely spaced events - Short jumper cables – Gainer- Splice

Event Analysis Checklist• Matched/Missed event occurrence• False event occurrence• Correct identification of event type• Ability to separate closely spaced events• Ability to locate beginning and end of fiber

under test without a separate launch and receive cable calibration test

• Provide all relevant measurements for each event

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Potential for Future Improvement

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Found event

False event

Likelihood technique allows for ongoing improvement

Traditional methods cannot be improved

Traditional algorithms

Direction of progress

Improvement is possible only if false event rate is lowFalse event rate: an indicator of improvement potential

Actual path of progress

Conclusion• Introduction of likelihood in event analysis offers balance between

sensitivity and reliability• New event analysis increases user satisfaction level by offering higher found

event rate• It reduces user frustration with fewer false events• Lower false event rate indicates it’s potential for future performance

improvement

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OTDR performance evolution to match Data Center evolution

Dynamic Range

Short Dead Zones

Event Analysis

Past Present Future