CATV Packet Classifier Provisioning for Low-Latency DOCSIS
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Solution Overview
Problem
The DOCSIS 3.1 standard does not specify how to provision packet classifiers in Cable Modem Termination Systems (CMTS) and cable modems for bifurcating traffic into low-latency and non-low-latency flows, posing a challenge for implementing Low Latency DOCSIS (LLD) services.
Innovation Solution
A method and apparatus for provisioning low latency services by partially mirroring packet flows, configuring packet classifiers, and using LLD agents to add classifiers to traffic, reducing latency by separating low-latency and high-latency packets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If packet classifiers are added to CMTS and cable modems for bifurcating traffic into low-latency and non-low-latency flows, then latency-sensitive application performance is improved, but device complexity and provisioning difficulty increase
Solution Approach 1:
The system enables automated self-provisioning of packet classifiers through interaction between LLD agents on network and customer premises. The agents automatically detect latency-sensitive applications, configure appropriate packet classifiers, and establish service flows without manual intervention, allowing the network to serve itself while reducing provisioning complexity
Solution Approach 2:
LLD agents act as intermediary components between the network infrastructure and latency-sensitive applications. These agents facilitate automated classification and routing by mediating between packet flows and packet classifiers, enabling intelligent traffic bifurcation without increasing overall system complexity
2Reliability
If all packet flows are monitored and classified for low-latency services, then service quality is improved, but processing overhead and network resource consumption increase
Solution Approach 1:
The system applies quality differentiation by treating only latency-sensitive packet flows with special classification and prioritization, while allowing non-latency-sensitive traffic to flow through standard processing paths. This localized application of quality enhancement reduces overall processing overhead while maintaining service quality where it matters most
Solution Approach 2:
Rather than applying comprehensive classification to all packet flows, the system performs partial classification only on flows identified as latency-sensitive through LLD agent detection. This partial action approach reduces processing overhead while still achieving the desired service quality improvement for critical applications
Data Source
AI summary
The present disclosure describes techniques for provisioning low latency services. In an aspect, a method comprises partially mirroring packet flows associated with a latency-sensitive application and passing through one or more network ports of a first network element, partially mirrored packets being mirrored in a second network element. The method further comprises configuring, by the second network element, a third network element to add one or more packet classifiers to traffic flowing through the third network element, the packet classifiers used to reduce latency of the classified traffic.


