Dynamic Moving Average for Network Congestion Management
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Solution Overview
Problem
Existing network congestion management techniques using fixed window averages are ineffective in responding to sudden changes due to either being too slow or overly responsive, leading to inefficient bandwidth utilization and unstable network behavior.
Innovation Solution
Implementing a dynamic moving average system that adjusts its window size based on statistical analysis of network data, such as packet loss and data rate, to determine network congestion levels, allowing for responsive yet stable routing decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a fixed window size is used for moving average calculation, then the system structure is simple, but the system responds slowly to sudden changes
Solution Approach 1:
The patent applies the dynamics principle by making the window size adaptive rather than fixed. The window size automatically adjusts based on network conditions - expanding when network metrics are stable to smooth variability, and contracting when sudden changes occur to respond quickly. This transforms the static moving average system into a dynamic one that adapts its parameters in real-time based on observed network behavior patterns.
Solution Approach 2:
The patent implements parameter changes by modifying the window size parameter based on statistical analysis of network data. The system monitors metrics such as packet loss and data rate, and when significant changes are detected, it adjusts the window size parameter to optimize the balance between smoothing and responsiveness. This allows the system to change its operational characteristics dynamically without requiring complete system redesign.
2Stability of the object's composition
If a small window size is used for moving average calculation, then the system responds quickly to changes, but the system becomes over-responsive leading to over-dynamic behavior
Solution Approach 1:
The patent applies feedback by continuously monitoring network metrics and using this information to adjust the window size. The system calculates moving averages, compares them against thresholds, and based on the detected patterns (sudden changes, gradual changes, or stable conditions), dynamically adjusts the window size. This closed-loop feedback mechanism prevents over-responsiveness by only reducing window size when actual significant changes are detected, rather than reacting to every minor fluctuation.
Solution Approach 2:
The system dynamically adjusts the window size based on the stability of network conditions. When network metrics show stable patterns, the window size increases to provide smoothing and reduce over-dynamic behavior. When instability or sudden changes are detected, the window size decreases to allow faster response. This dynamic adaptation stabilizes network behavior by matching the smoothing level to the actual network condition volatility.
3Stability of the object's composition
If a large window size is used for moving average calculation, then the system is stable, but the system is slow to respond to sudden changes
Solution Approach 1:
The patent makes the window size dynamic rather than statically large. The system starts with a larger window for stability but automatically reduces it when sudden changes are detected in network metrics. This allows the system to maintain stability during normal operation while being able to quickly reduce the window size to detect and respond to congestion changes promptly, thus reducing time loss without sacrificing overall stability.
Solution Approach 2:
The system performs preliminary analysis of network metrics to detect patterns indicating upcoming changes. By monitoring trends in packet loss, data rate, and other metrics before significant congestion occurs, the system can proactively adjust the window size in anticipation of changes, reducing the time loss associated with detecting congestion while maintaining stability during normal conditions.
Data Source
AI summary
Techniques for managing congestion in a computer network are described. In some examples a network node uses a dynamic moving average to determine a level of network congestion a computer network. The dynamic moving average uses a window that is dynamically resized based on the contents of the window. For example, when the contents of the half of the window containing older samples are sufficiently different from the contents of the half of the window containing newer samples, the older samples are discarded and an average is calculated using just the newer samples.