Inverse Leaky Bucket Traffic Modeling for Bandwidth Allocation
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Solution Overview
Problem
Specifying appropriate Leaky Bucket parameters for network packet traffic is challenging, making it difficult for users to choose the right service level and for service providers to allocate bandwidth effectively.
Innovation Solution
A method and system for modeling packet traffic in terms of Leaky Bucket parameters, where nonconforming packets are identified and modified parameters are adjusted to ensure conformance, allowing for the determination of optimal target packet inter-arrival intervals and storage of modified parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Leaky Bucket parameters are used to limit packet traffic, then network service levels can be controlled and bandwidth can be allocated, but it becomes difficult to specify appropriate parameters T and L for a given traffic flow
Solution Approach 1:
The system automatically characterizes traffic patterns and determines optimal Leaky Bucket parameters without requiring manual input from users. The traffic characterization module analyzes packet arrival times and lengths to self-determine parameters T and L, making the system self-serve the parameter specification task.
Solution Approach 2:
The system dynamically adjusts Leaky Bucket parameters based on actual traffic conditions. By changing parameters T (target inter-arrival interval) and L (tolerance) according to observed packet characteristics, the system adapts to different traffic flows and service levels automatically.
2Adaptability or versatility
If users do not know how to characterize their traffic, then they cannot choose the right service level, but providing traffic characterization tools adds system complexity
Solution Approach 1:
The traffic characterization module operates autonomously, collecting packet data and computing parameters without user intervention. This self-service approach enables users to select service levels without needing to understand or specify traffic characteristics manually.
Solution Approach 2:
The system replaces manual traffic characterization processes with automated computational analysis. Instead of requiring users to mechanically specify parameters, the system uses algorithmic analysis of packet streams to derive characteristics automatically.
3Productivity
If service providers do not know traffic characteristics, then bandwidth allocation is difficult, but implementing comprehensive monitoring and analysis increases operational complexity
Solution Approach 1:
The system extracts key traffic characteristics (arrival intervals, packet lengths, conformance status) from the complex stream of network packets. By taking out only the essential parameters needed for bandwidth allocation decisions, the system achieves efficient allocation without requiring comprehensive analysis of all packet details.
Solution Approach 2:
The bandwidth allocation system is segmented into independent modules: traffic characterization module, parameter determination module, and allocation decision module. This segmentation allows each module to process specific aspects of traffic data independently, reducing overall system complexity while maintaining allocation efficiency.
Data Source
AI summary
An apparatus, method, and computer program of modeling packet traffic in terms of Leaky Bucket parameters. The Leaky Bucket parameters are tested based on conformance of packet traffic. If a result of the testing is nonconformance of the packet traffic, one or more of the Leaky Bucket parameters is modified such that the packet traffic is conforming. The one or more modified Leaky Bucket parameters is stored in a computer-readable storage medium.