Adaptive Control Plane Policing for Network Convergence
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Solution Overview
Problem
Static control plane policing (CoPP) in networks is inefficient as it does not adapt to changing network traffic patterns, leading to suboptimal utilization of control plane capacity and impacting data plane convergence and application performance, especially in scaled environments.
Innovation Solution
Implementing an adaptive CoPP system that uses machine learning models, such as Random Forest or LSTM neural networks, to dynamically adjust CoPP rates based on real-time network traffic patterns, proactively managing thresholds for lower CoS traffic to optimize CPU protection and improve convergence and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static CoPP thresholds are used to protect the control plane, then CPU protection is improved, but data plane convergence and application performance deteriorate
Solution Approach 1:
The patent implements dynamic CoPP thresholds that automatically adjust based on real-time network conditions. The system monitors control plane traffic patterns and dynamically modifies policing thresholds to adapt to changing network demands, allowing the CoPP to transition between protective and permissive states as needed. This resolves the contradiction by making the protection mechanism flexible rather than static.
Solution Approach 2:
The patent changes the parameters of CoPP thresholds based on network traffic patterns and CPU utilization levels. By modifying policing parameters dynamically according to actual network conditions rather than using fixed values, the system can optimize both CPU protection and data plane performance simultaneously.
2Reliability
If aggressive CoPP is applied to data plane traffic, then CPU protection is improved, but convergence speed and application performance deteriorate
Solution Approach 1:
The patent applies different CoPP policies to different traffic classes and scenarios. Rather than uniform aggressive policing, the system implements localized quality control where critical control traffic receives protective policing while data plane traffic receives adaptive policing based on actual network conditions, allowing faster convergence when needed.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor network convergence status and CPU utilization in real-time. This feedback loop allows the CoPP to adjust its aggression level dynamically, reducing policing strictness when convergence is needed and increasing it when CPU protection is the priority.
3Reliability
If manual tuning of CoPP parameters is performed, then CPU protection is optimized, but operational complexity and time consumption increase
Solution Approach 1:
The patent implements self-service CoPP management where the system automatically monitors network conditions and adjusts its own parameters without requiring manual intervention. The system performs self-optimization by analyzing traffic patterns and autonomously modifying policing thresholds, eliminating the complexity of manual tuning while maintaining optimal CPU protection.
Solution Approach 2:
The patent replaces manual mechanical tuning processes with automated software-based adjustment mechanisms. Instead of requiring network administrators to manually configure and reconfigure CoPP parameters, the system uses software algorithms that automatically optimize parameters based on real-time data, simplifying operations while maintaining effectiveness.
Data Source
AI summary
Techniques are described for an adaptive CoPP that can adapt and change based on actual network control traffic rather than static CoPP rates. An aggressive CoPP can protect the CPU (route processor) of a network device, e.g., routers and switches, but may also penalize convergence and performance. An adaptive CoPP may protect CPU as well as boost convergence and performance parameters. In particular, traffic between two sites may be managed by proactively changing the thresholds of lower CoS traffic based on the CoPP utilization of various protocol/BPDU class traffic, thereby improving data plane convergence and application performance in scaled environments.