Dynamic Congestion Management in Communications Networks
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Solution Overview
Problem
Current systems for managing communications network congestion are imprecise and reactive, often requiring manual adjustments after congestion occurs, which can lead to delayed mitigation of network issues and recurring congestion patterns.
Innovation Solution
Implementing dynamic congestion management systems that automatically detect congestion through traffic statistics analysis and dynamically provision traffic shaping rules to mitigate network congestion in real-time, using deep packet inspection (DPI)-enabled traffic management policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual provisioning of traffic management policies is used to address network congestion, then policy changes can be implemented, but the response is delayed and occurs after congestion has passed or with delay in response to alarm
Solution Approach 1:
The system enables automated self-provisioning of traffic management policies by monitoring network congestion conditions and automatically deploying appropriate policies without manual intervention. The system detects congestion patterns, selects suitable policies from a library, and provisions them automatically to congested nodes, eliminating the delay inherent in manual processes.
Solution Approach 2:
The system implements continuous monitoring of network traffic statistics and congestion conditions, using this feedback to trigger automatic policy provisioning when congestion thresholds are exceeded. The feedback loop enables real-time detection and response to congestion events, ensuring timely mitigation while allowing evaluation of policy effectiveness.
2Reliability
If static traffic management policies are provisioned to manage network congestion, then some congestion control is achieved, but the policies are imprecise and require iterative manual adjustments
Solution Approach 1:
The system transitions from static to dynamic policy provisioning by continuously monitoring network conditions and automatically adjusting traffic management policies in real-time. Policies are dynamically selected and deployed based on current congestion patterns, traffic characteristics, and network state, enabling adaptive congestion management without manual iteration.
Solution Approach 2:
The system replaces the manual mechanical process of policy provisioning with an automated electronic system that uses algorithms to analyze traffic statistics, detect congestion patterns, and automatically provision appropriate policies. This substitution eliminates the iterative manual adjustment process while improving precision and reliability.
3Ease of operation
If deep packet inspection (DPI)-enabled traffic management policies are manually provisioned to address congestion, then traffic can be managed, but the process is imprecise and requires repeated manual changes
Solution Approach 1:
The system enables automated self-provisioning of DPI-enabled traffic management policies by automatically detecting congestion conditions, selecting appropriate policies from a library, and deploying them without manual intervention. This eliminates the time-consuming manual provisioning process while maintaining the traffic management capabilities provided by DPI technology.
Data Source
AI summary
Systems and methods for dynamic congestion management in communications networks are disclosed herein. According to an aspect, a method can include determining traffic statistics of at least one node in a communications network. The method can also include determining whether the at least one node is congested based on the traffic statistics. Further, the method can include dynamically changing or provisioning a set of at least one traffic shaping rule for application to the at least one node in response to determining that the at least one node is congested.