Resource Availability Server for Dynamic QoS Management
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Solution Overview
Problem
In packet networks, especially IMS mobile networks, operators face challenges in providing guaranteed end-to-end quality of service (QoS) due to uncontrolled access networks like Wi-Fi and public broadband, which are non-deterministic and lack resource reservation capabilities, leading to degraded user experience for resource-intensive real-time multimedia services.
Innovation Solution
A method where resource management components in devices and network elements send update information to a resource availability server, which generates predictive models of network resource characteristics, allowing for improved QoS management by notifying session quality management components to optimize session management and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If operators use uncontrolled access networks (Wi-Fi, Ethernet, public broadband) to deliver IP multimedia services, then service coverage and accessibility are improved, but quality of service control and reliability deteriorate
Solution Approach 1:
The patent introduces a resource availability server as an intermediary between the operator's controlled network and the uncontrolled access networks. This server collects resource information from multiple access networks, processes it centrally, and provides QoS decisions back to the network, enabling quality control without direct ownership of the access infrastructure.
Solution Approach 2:
The system segments the end-to-end QoS control function into separate components: resource monitoring at the access network level, central processing at the resource availability server, and QoS decision-making at the application level. This segmentation allows each component to operate independently while maintaining overall QoS control.
2Reliability
If resource reservations are made upfront for media sessions, then QoS guarantee is improved, but network flexibility and real-time adaptation worsen
Solution Approach 1:
The system dynamically adjusts resource reservations based on real-time network conditions. Instead of static upfront reservations, the resource availability server continuously monitors network resource availability and updates QoS parameters during active sessions, allowing both guarantee and flexibility.
Solution Approach 2:
The system performs preliminary resource assessment and prediction before media sessions start, allowing QoS parameters to be negotiated in advance based on predicted availability. This preliminary action enables QoS guarantees while leaving room for real-time adjustments during the session.
3Ease of manufacture
If pre-session QoS negotiation is performed, then service quality planning is improved, but actual QoS delivery reliability worsens due to non-deterministic network conditions
Solution Approach 1:
The system implements continuous feedback loops where the resource availability server monitors actual network performance during sessions and compares it with negotiated QoS parameters. This feedback enables real-time QoS adjustments to maintain reliability despite non-deterministic network conditions.
Solution Approach 2:
The system performs preliminary QoS negotiation and resource prediction before sessions start, establishing baseline quality parameters. This preliminary planning is complemented by real-time adjustments during the session to ensure actual delivery meets the planned quality.
4Reliability
If operators implement comprehensive resource monitoring and predictive modeling, then QoS management capability is improved, but system complexity increases
Solution Approach 1:
The resource availability server performs multiple functions: collecting resource information from diverse access networks, predicting network conditions, making QoS decisions, and interfacing with various applications. This multi-functionality consolidates complexity into a single universal component rather than requiring complex solutions at each access network.
Solution Approach 2:
The resource availability server acts as an intermediary that simplifies the interface between complex access networks and applications. It abstracts the complexity of monitoring and prediction algorithms, presenting a simplified QoS management interface to applications while handling the complex resource monitoring and modeling internally.
Data Source
AI summary
The invention provides real time dynamic resource management to improve end-to-end QoS by mobile devices regularly updating a resource availability server (RAS) with resource update information. Examples of resource update information are device battery status, available memory, session bandwidth, delay, packet loss, and jitter, network element storage capacity, network element processing power. This information is made available by the RAS. In addition, the RAS generates and maintains predictive models and makes available predictive data from these models. Network elements and devices retrieve this information in the form of notifications from the RAS or by way of querying the RAS. The network elements and devices, based on these predictions, act to negotiate sessions to optimise QoS. In one embodiment the RAS is updated by only mobile devices subscribed to the operator which hosts the RAS. The update information is addressed to the RAS as a stand-alone entity. However, it is envisaged that the server may be hosted by multiple operators and may receive updates from devices subscribed to different operators. Also, it is envisaged that not only mobile devices but also network elements such as MMSCs may send update information.