Learning Objectives

• Proactive Network Management (PNM)
  • What is PNM, what is pre-equalization, etc.
  • How is PNM data currently used?
• CableMon
  • design goals, rationale, etc.
  • how it advances state of the art?

Background

HFC Architecture

• Key components
  • CMTS, fiber optical node, (trunk/line) RF amplifiers, splitters, cable modem (not shown)

Proactive Network Maintenance (PNM)

• What? Measurement data collected at cable modem (e.g., SNR, FEC stats, etc.)
• Why? Proactively detect, localize, and correct impairments issues
• DOCSIS standardizes how to
  • store it in a local MIB
  • query it using SNMP

PNM Measurement Data

• Previous generation:
  • Data: error rates, SNRs, etc.
  • Limits: not suited for root-cause analysis
• Next generation:
  • Data: pre-equalization coefficients, full-band capture, etc.
  • Limits: hard to scale

Analysis of pre-equalization coefficients was a game changer!

Pre-Equalization (1)

• Objective
  • Compensate for RF impairments to improve upstream performance
• How?
  • CMTS analyzes the quality of signals received from cable modem
  • It computes eqaulizer adjustment values to modem!

Pre-Equalization (2)

Cable modem signal with (top) and without (botton) pre-equalization

Equalizer Taps (Simple)

2-Tap Equalizer

Equalizer Taps (DOCSIS 2.0+)

24-Tap Equalizer

• Pre-main taps (b-8 – b-1): lower is better
• Main tap (b0): higher is better
• Post-main taps (b1 - b15): lower is better

Understanding Tap Values (1)

DOCSIS Pre-Equalization Tap Values

Understanding Tap Values (2)

Frequency Response from Pre-Equalization Data

• Peak-to-valley: 18 dB > Th (0.5 dB)

Understanding Tap Values (3)

• preMTTER:
  • Pre-Main Tap to Total Energy Ratio
• postMTTER:
  • Post-Main Tap to Total Energy Ratio
• MRLevel:
  • Micro-reflection level
• TDR:
  • Time domain reflectometer

Understanding Tap Values (4)

Pre-Equalization Table After Complex FFT

• postMTTER > Th –> micro-reflection
• TDR = ~180 feet –> distance between modem and reflection source

Localizing Faults

• Correlate tap values across modems
• Localize faults using TDR data

Facts

• Cable broadband is available to 93 % of US homes
  • other options: DSL (43 %) and Fiber (29 %)
• FCC requires 99.99 % availability, but currently we only have 99 % availability
• PNM –> diagnose RF impairments –> avoid future outages –> improve availability

Measurement Systems (Collection)

• Approach:
  • periodically collect PNM data (instantaneous) from cable modems (every four hours)
• Questions:
  • Why such low collection frequency?
  • How to collect/analyse data at high frequency?

Measurement Systems (Analysis)

• Approach:
  • Scoreboarding (Comcast):
    • per-signal (pre-specified) thresholds
    • cumulative score across all signals
  • MTR example (less than 18 dB 26% of time)
• Questions:
  • How to handle noisy (?) data?
  • How to adapt thresholds over time?

CableMon

Data

• Collected for 8 months (2019) from 60 K modems
• PNM Data
  • <ts, channel-freq, SNR, Tx/Rx-power, FEC stats, T3/T4 timeouts, pre-equalization coeffs, etc.>–-every 4 hours
• Customer Complaint Tickets
  • <customer-id, creation-time, close-time, etc.>

Design Goals

• Accuracy
  • Ticket prediction accuracy: \(\frac{CableMon \cap Customer}{CableMon}\)
  • Ticket coverage: \(\frac{CableMon }{Customer}\)
• No manual labeling
• No extensive parameter tuning
• Efficient

Approach

• Ticketing rate
  • average number of customer tickets created in a unit time (4 hours)
• Divide PNM metric into bins
• For each bin, compute the average ticketing rate (\(\frac{N_{b}}{T_{b}}\))

Set threshold based on ticketing rate.

Ticketing Rate vs. PNM Data (SNR)

Ticketing rate increases
for low SNR values.

Setting Fault Detection Threshold

• Goal
  • Minimizes both FPs and FNs
• Observation
  • Ticketing rate higher than normal during a faulty periods
• Approach
  • Set threshold that maximizes ticketing rate ratio

Feature Selection

Select top-K features with higher ticketing rate ratio

Minimizing False Positives

• Use a moving window of size y (12, 48 hours)
• If x (8) data points in the window are abnormal, then send dispatch to fix problem

Response can be much slower!

Results (1)

Types of tickets detected

CableMon detected more high-severtiy tickets than state-of-the-art systems

Results (2)

Distribution of Ticket Life Time (end-creation time)

• A longer ticket life time indicates that the problem that triggered the ticket takes a longer time to fix.

Results (3)

• Distribution of Report Waiting Time
  • time difference between when problem started and when customer reported it

• A shorter report waiting time indicates that the problem triggered by the ticket is more urgent.

Summary

• Proactive Network Management (PNM) data
  • What is PNM, how it is used to proactively detect and diagnose RF impairments in the last mile
• CableMon
  • What is CableMon, and how it uses customer complaint tickets to predict process PNM data for inferring RF impairments