Just-in-Time QoS Modulation for Network Resource Efficiency
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Solution Overview
Problem
Current QoS management in access networks is static and inflexible, leading to inefficient resource allocation and high costs for both users and operators, as it does not adapt to real-time demands or user behavior, and lacks fine granularity for service-level changes.
Innovation Solution
A method for just-in-time modulation of quality of service (QoS) that records user behavior, predicts resource needs, and coordinates concurrent demands across multiple users, using a scheduler server to dynamically allocate network resources based on service usage and user profiles, allowing for adaptive QoS adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static QoS allocation is used at subscription time, then network resources are allocated in advance, but resource efficiency deteriorates and costs increase due to unused capacity
Solution Approach 1:
The patent transforms static QoS allocation into dynamic QoS modulation by continuously adjusting bandwidth allocation based on real-time service usage patterns and user behavior. The system transitions from fixed subscription-based allocation to adaptive modulation that responds to actual demand, resolving the contradiction between QoS guarantee and resource efficiency.
Solution Approach 2:
The system changes the parameter of QoS allocation from static to dynamic by monitoring service usage and user behavior over time. The bandwidth allocation parameter is continuously adjusted based on observed usage patterns, allowing the network to optimize resource distribution while maintaining QoS guarantees for active services.
2Ease of manufacture
If static subscription types are used, then QoS is configured administratively at subscription time, but adaptability deteriorates and cannot respond to real-time service demands
Solution Approach 1:
The system implements self-service QoS modulation where the network automatically adjusts QoS parameters based on observed service usage and user behavior patterns. This eliminates the need for complex administrative reconfiguration when services change, allowing the system to adapt autonomously while maintaining ease of initial configuration.
Solution Approach 2:
The patent introduces feedback mechanisms that continuously monitor service usage and user behavior, using this information to automatically adjust QoS allocation. This feedback loop enables the system to adapt to real-time demands while maintaining simple initial configuration, resolving the contradiction between ease of configuration and adaptability.
3Device complexity
If connection-level QoS allocation is used, then QoS is assigned at connection setup, but granularity deteriorates and cannot accommodate fine-level service requirements
Solution Approach 1:
The patent segments QoS allocation from the connection level to the service level by introducing service-specific QoS modulation. This allows fine-grained control of bandwidth allocation for individual services within a connection, increasing precision without proportionally increasing overall system complexity through automated service-level management.
4Ease of operation
If user-triggered QoS change is used, then QoS adaptation is initiated by user preferences, but user burden increases and requires manual matching judgments
Solution Approach 1:
The system performs self-service QoS optimization by automatically analyzing service usage patterns and user behavior to determine optimal QoS allocation. This eliminates the need for users to manually match preferences with behavior patterns, reducing user burden while maintaining ease of operation through transparent automated management.
Data Source
AI summary
The invention relates to observing requests, deriving quality of service (QoS) demands and scheduling the network's resources in terms of QoS. A scheduler is modulating the QoS based on service usage and user-behavior just-in-time. It relates to a method for efficient use of network resources by just-in-time modulation of quality of service based on real-time service-usage and user-behavior comprising steps recording events, generating a synthesis of user-behavior for a QoS user profile according to QoS user preferences, predicting required QoS demand based on current user behavior and user QoS profile, according to QoS user preferences, deriving and propagating QoS demands and allocations, and co-ordination of QoS request of a manifold of users, based on requests, QoS user profiles, QoS user preferences and resources. Further it relates to computer software product, client terminals, a scheduler server, a network element, and a network.