Network Throughput Optimization for Heterogeneous Traffic
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Solution Overview
Problem
In networks with heterogeneous data traffic, such as IoT applications, efficiently allocating bandwidth between inelastic and elastic data flows to maximize throughput is challenging, especially in scenarios with limited bandwidth, where existing methods often fail to optimize both real-time and non-real-time data transmission simultaneously.
Innovation Solution
A method that determines the required throughput for each station based on back-off counter time, packet length, and successful transmission probability, and adjusts the transmission attempt rate to maximize network throughput while ensuring optimal fixed throughput for inelastic data flow and proportional throughput for elastic data flow, using utility functions and asymptotic analysis to optimize channel access in IEEE 802.11 protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bandwidth is allocated to maximize overall network throughput, then elastic data flow throughput increases, but inelastic data flow throughput may be compromised
Solution Approach 1:
The patent segments the network throughput optimization into two independent parts: (1) guaranteeing minimum throughput for inelastic data flows, and (2) maximizing remaining throughput for elastic data flows. This segmentation allows the system to first ensure real-time transmission requirements are met, then optimize overall throughput without compromising either objective.
Solution Approach 2:
The patent changes the throughput allocation parameters dynamically based on traffic type. For inelastic data flows, a minimum throughput parameter is enforced; for elastic data flows, proportional fairness parameters are applied. This parameter differentiation resolves the contradiction by allowing different optimization criteria for different data types simultaneously.
2Productivity
If transmission attempt rate is increased to maximize elastic data flow throughput, then network productivity improves, but collision probability increases and inelastic data flow reliability deteriorates
Solution Approach 1:
The patent applies partial action by only increasing transmission attempt rates for elastic data flows after ensuring inelastic data flows receive their guaranteed minimum throughput. This partial optimization of elastic traffic without compromising inelastic traffic reliability resolves the contradiction between productivity and reliability.
Solution Approach 2:
The system performs preliminary action by first allocating guaranteed throughput to inelastic data flows before allowing elastic data flows to compete for remaining bandwidth. This preliminary guarantee ensures real-time transmission reliability is established before optimizing overall throughput.
3Reliability
If fixed throughput is allocated to inelastic data flow, then real-time transmission reliability is ensured, but overall network throughput is reduced
Solution Approach 1:
The patent segments the throughput optimization problem into guaranteeing minimum throughput for inelastic flows and maximizing remaining throughput for elastic flows. This segmentation allows the system to first ensure real-time transmission requirements are met, then optimize overall throughput without compromising either objective.
Solution Approach 2:
The patent changes the throughput allocation parameters dynamically based on traffic type. For inelastic data flows, a minimum throughput parameter is enforced; for elastic data flows, proportional fairness parameters are applied. This parameter differentiation resolves the contradiction by allowing different optimization criteria for different data types simultaneously.
4Adaptability or versatility
If proportional throughput ratio is applied to elastic data flow, then bandwidth allocation fairness improves, but inelastic data flow throughput may be under-allocated
Solution Approach 1:
The patent segments the throughput optimization into two independent parts: (1) guaranteeing minimum throughput for inelastic data flows, and (2) applying proportional fairness for elastic data flows. This segmentation ensures that fairness optimization for elastic traffic does not compromise the guaranteed throughput of inelastic traffic.
Solution Approach 2:
The system performs preliminary action by first allocating guaranteed throughput to inelastic data flows before allowing elastic data flows to compete for remaining bandwidth. This preliminary guarantee ensures real-time transmission reliability is established before optimizing overall throughput with proportional fairness.
Data Source
AI summary
A method for optimizing throughput of a network with stations adapted to transmit data to an access point includes the step of determining a respective required throughput for each station based on: respective time periods required for decreasing a count of a respective back-off counter associated with each of the stations, a transmission packet length of the respective station, and a probability of successful transmission for of the respective station. The respective required throughput so determined is a function of a respective transmission attempt rate for the station. The method further includes the step of determining the respective transmission attempt rate for each station for maximizing a sum of the respective required throughput such that a respective fixed throughput is provided for inelastic data flow in the network, a respective proportional throughput ratio is provided for elastic data flow in the network, and the throughput of the network is maximized.