PMA Improvements – Strategies Employed for Faster Mitigation, Increased Capacity, and Cost Savings (2022)

By Jonathan Leech, Andy Martushev; Comcast

A Profile Management Application (PMA) is a critical component of DOCSIS downstream and upstream for both speed and reliability. This is especially true with increased bandwidth demands in recent years. As such, it is critical to react quickly to issues in order to provide the best customer experience. Faster mitigation of network issues reduces customer impact and call volumes. The profile recommendation interval was lowered from 6 hours in the previous system (Harb, 2020) to 5 minutes in the DOCSIS 3.0 (D3.0) upstream (US), and from 3.5 days to 1 day in the DOCSIS 3.1 (D3.1) US and downstream (DS), while reducing operational costs and improving capacity.

Ingesting and analyzing large amounts of data at a high rate creates high demand for both storage and CPU. The technology stack was refactored and costs were lowered by eliminating redundancy and leveraging streaming, batching, cloud computing, and parallel processing. Aligning the polling and PMA processing using Simple Storage Service (S3), Simple Notification Service (SNS), and Simple Queueing Service (SQS) allows for processing of a single batch of related data immediately after polling. Storage demand was reduced by moving components from a relational database to S3 with a large batch size. CPU demand was reduced by moving the analysis logic from a large Apache Spark cluster to a smaller Elastic Kubernetes Service (EKS) cluster. CPU demand was further reduced by refactoring the clustering algorithm to use Single Instruction Multiple Data (SIMD) parallel processing.

Making PMA recommendations more often improves capacity to an extent, but larger capacity gains were made by making changes to the profile selection and clustering algorithms. For D3.1, the Modulation Error Ratio (MER) data model was improved by using histograms and a time decay function. Better utilization and capacity estimates were created by using the model and the added time dimension. Optimal percentiles and corresponding weights are generated for each modem and used as inputs to the clustering algorithm. The result was capacity gains of greater than 8% and 5 Tb/s.

By clicking the "Download Paper" button, you are agreeing to our terms and conditions.

Similar Papers

Deploying PMA-Enabled OFDMA in Mid-Split and High-Split
By Maher Harb, Dan Rice, Kevin Dugan, Jude Ferreira, Robert Lund; Comcast
Full Scale Deployment of PMA
By Maher Harb, Bryan Santangelo, Dan Rice & Jude Ferreira, Comcast
Software Revolution of Field Meters Using a Field-Capable Measurement Device
By Anthony Curran & Andy Martushev, Comcast
Repair the Ides Of March: COVID-19 Induced Adaption of Access Network Strategies
By Dr. Robert Howald, Comcast
Access Capacity Planning: Staying Well Ahead of Customer Demand Helped Ensure Stability During COVID-19
By Bruce E. Barker Jr., Claude Bou Abboud & Erik Neeld, Comcast Cable
A Machine Learning Pipeline for D3.1 Profile Management
By Maher Harb, Jude Ferreira, Dan Rice, Bryan Santangelo & Rick Spanbauer, Comcast
Measuring DOCSIS 3.1 & 4.0 Capacity
By Claude Bou-Abboud, Priyan Sarathy, Ganesh Chandrasekaran, Alexandru Tufescu, Santosh Dadisetti, Comcast
Photon Avatars in the Comcast Cosmos: An End-to-End View of Comcast Core, Metro and Access Networks
By Venk Mutalik, Steve Ruppa, Fred Bartholf, Bob Gaydos, Steve Surdam, Amarildo Vieira, Dan Rice; Comcast
Roaring into the ‘20s with 10G
By Dr. Robert Howald, Robert Thompson, Sebnem Ozer, Daniel Rice & Larry Wolcott, Comcast; Dr. Tom Cloonan, Dr. Ruth Cloonan & John Ulm, CommScope; Jan Ariesen, Technetix
Key Learnings from Comcast’s Use of Open Source Software in the Access Network
By Louis Donofrio & Qin Zang, Comcast Cable; Vignesh Ramamurthy, Infosys Consulting
More Results >>