Adaptive Admission Control via Feedback Parameter Adjustment
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Solution Overview
Problem
Existing admission control methods in IP networks are inefficient as they are static and do not adapt to changing network conditions, leading to suboptimal Quality of Service (QoS) for applications like voice and videoconferencing, and lack dynamic adjustment of system parameters based on performance metrics.
Innovation Solution
Implementing a feedback control system that uses performance predictors and metrics to adjust system parameters, such as window sizes and weighting variables, to improve admission control decisions and adapt to changing network conditions, ensuring better QoS by selecting the best window size for historical data and dynamically weighting parameters for improved prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static admission control algorithms are used with fixed criteria, then the system is simple to implement and analyze, but the system cannot adapt to changing network conditions and leads to suboptimal QoS
Solution Approach 1:
The patent implements dynamic admission control by allowing the system to adjust its decision criteria based on real-time network conditions. The algorithm transitions from static fixed thresholds to dynamic adaptive thresholds that evolve with network state, enabling the system to respond to changing traffic patterns and resource availability without requiring complete redesign of the control logic.
Solution Approach 2:
The patent employs feedback mechanisms where performance metrics and system state information are continuously monitored and fed back into the admission control algorithm. This feedback loop enables the system to learn from past decisions and network conditions, adjusting its parameters to improve future admission decisions and optimize QoS under varying network conditions.
2Reliability
If resource-based admission control reserves network resources for peak bandwidth needs, then guaranteed QoS is provided, but additional flows cannot be admitted without QoS degradation
Solution Approach 1:
The patent changes the parameter of resource reservation from fixed peak bandwidth guarantees to adaptive reservations that adjust based on actual traffic patterns and network conditions. This allows the system to provide QoS guarantees when needed while being flexible enough to admit additional flows when network conditions permit, thereby increasing overall system productivity.
Solution Approach 2:
The patent applies partial resource reservation rather than full peak bandwidth reservation for all flows. By reserving only the necessary resources based on actual demand and traffic characteristics, the system can support more concurrent calls and flows while maintaining acceptable QoS, rather than over-reserving resources that would limit total system capacity.
3Productivity
If measurement-based admission control makes on-line measurements to learn traffic statistics, then the number of calls supported is improved, but the system requires complex traffic specifications and is harder to analyze
Solution Approach 1:
The patent implements self-service admission control where the system automatically learns traffic characteristics and adjusts its own parameters without requiring external intervention or complex manual traffic specifications. The system performs on-line measurements and self-optimizes its admission criteria based on observed traffic patterns, reducing the burden on network administrators while maintaining high call support capacity.
Data Source
AI summary
Methods and apparatus are provided for improving an admission control system using feedback control techniques based on a system performance measure. Generally, one or more performance metrics, such as system output error measurements, are fed back into the admission control system to adjust one or more system parameters and thereby improve the overall performance (e.g., reduce the error measurement). For admission control, one or more performance predictors are used to evaluate an expected quality of the call. The performance of the predictor is evaluated; and at least one of the one or more parameters is adapted based on the evaluation.