Fair Bandwidth Management Algorithm for Cable Network Congestion
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Solution Overview
Problem
The rapid increase in traffic levels and network congestion in cable networks due to a small percentage of heavy users leads to significant revenue loss and decreased network performance for normal users, necessitating effective bandwidth management to ensure fair usage and minimize impact on heavy users.
Innovation Solution
The implementation of Fair Bandwidth Management (FBM) within a DOCSIS network, which identifies and prioritizes heavy users by applying 'gates' to control their data usage, redistributing bandwidth to maintain optimal network utilization below 59%, thereby improving responsiveness for normal users while allowing heavy users maximum bandwidth without deteriorating network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If more bandwidth is provided to heavy users, then their traffic consumption is satisfied, but network congestion increases and performance for normal users deteriorates
Solution Approach 1:
The patent implements dynamic bandwidth management through the FBM algorithm that continuously monitors network conditions and adjusts bandwidth allocation in real-time. The system dynamically identifies heavy users and applies gating mechanisms adaptively based on current network utilization levels, transitioning between different bandwidth allocation states to maintain optimal performance for all users
Solution Approach 2:
The system changes the parameter of bandwidth allocation by applying gating factors to heavy users. The FBM algorithm calculates appropriate gate values to reduce heavy user bandwidth consumption during congestion while maintaining fair bandwidth distribution. This parameter change allows the system to shift from static to adaptive bandwidth allocation based on network conditions
2Speed
If bandwidth is capped for heavy users, then network responsiveness for normal users improves, but heavy user bandwidth consumption is reduced
Solution Approach 1:
The FBM algorithm performs preliminary identification of heavy users before congestion fully develops. By monitoring traffic patterns and pre-applying gating mechanisms to users who show signs of heavy consumption, the system prevents congestion before it occurs, maintaining network responsiveness while managing bandwidth proactively
Solution Approach 2:
The system implements feedback through continuous monitoring of network utilization metrics and heavy user bandwidth consumption. The FBM algorithm uses this feedback to adjust gating factors dynamically, increasing restriction when congestion is detected and relaxing it when network conditions improve, thereby maintaining responsiveness while managing heavy user consumption
3Quantity of substance
If infrastructure capacity is increased to handle heavy user traffic, then bandwidth availability improves, but infrastructural and operational costs increase
Solution Approach 1:
The FBM system enables self-service bandwidth management where the network automatically identifies heavy users and applies appropriate gating without requiring manual intervention or infrastructure expansion. The algorithm autonomously monitors, detects, and manages bandwidth allocation, reducing the need for additional infrastructural investment while maintaining service quality
Solution Approach 2:
Instead of increasing physical infrastructure capacity, the system changes the parameter of bandwidth allocation through software-based gating mechanisms. This virtualization of bandwidth management allows the network to handle heavy user traffic through intelligent allocation rather than physical expansion, avoiding increased infrastructural costs
Data Source
AI summary
A process of managing bandwidth in a computer network having normal users and users that consume a disproportionate amount of bandwidth includes creating bandwidth gates for m users causing disproportionate traffic on the network, each bandwidth gate providing a bandwidth of y, and setting m to a minimum and y to a maximum for the set m.