Distributed Measurement Coordination for QoE Management
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Solution Overview
Problem
Existing measurement systems lack scalable solutions for real-time Quality of Experience (QoE) management in both traditional telecommunication and OTT applications, failing to provide granular and correlated user, application, and network-side insights, which are essential for detecting transient degradation and preventing poor user experience.
Innovation Solution
A distributed measurement system with centralized orchestration, using customer experience agents to selectively monitor and enrich packet headers for in-band communication, dynamically delegate analytics and actions among agents, and adapt measurement density based on traffic volume and resource availability, enabling efficient QoE management even in resource-limited environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a distributed measurement system is implemented to provide granular QoE insights, then measurement precision and insight granularity are improved, but device complexity and computational overhead increase
Solution Approach 1:
The measurement system is divided into multiple Customer Experience Agents (CEAs) distributed across different network elements, each responsible for collecting and processing measurements locally. This segmentation allows granular QoE insights to be obtained at multiple points in the network while distributing the computational burden, thus improving measurement precision without concentrating all complexity in a single system component.
Solution Approach 2:
The patent introduces a hierarchical dimension to the measurement system, with CEAs operating at multiple levels (network element level, aggregate level, and end-to-end level). This multi-dimensional approach allows the system to provide granular insights where needed while aggregating data at higher levels to reduce overall computational overhead and manage complexity.
2Measurement precision
If measurement density is increased to detect transient degradation, then measurement precision is improved, but use of energy and computational resources increase
Solution Approach 1:
The measurement density is made dynamic rather than static. CEAs adjust the density of measurements based on network conditions, traffic patterns, and detected anomalies. During normal conditions, measurement density is reduced to conserve resources. When transient degradation is detected or suspected, measurement density is automatically increased to improve detection precision, thus balancing measurement quality with resource consumption.
Solution Approach 2:
The system changes key parameters of the measurement process dynamically, including measurement intervals, sampling rates, and aggregation levels. These parameter changes are based on real-time assessment of network state and resource availability, allowing the system to maintain high measurement precision for transient degradation detection while adapting resource consumption to current conditions.
3Measurement precision
If centralized orchestration is implemented to coordinate distributed agents, then measurement precision and coordination are improved, but device complexity and communication overhead increase
Solution Approach 1:
A centralized orchestration entity acts as an intermediary between distributed CEAs and the network manager. This intermediary coordinates measurement activities, collects results from multiple CEAs, and performs correlated analysis to generate comprehensive QoE insights. The intermediary approach improves measurement precision through coordinated multi-point measurements while isolating the complexity of orchestration in a dedicated component rather than distributing it across all network elements.
4Productivity
If selective monitoring and header enrichment are implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
Selective monitoring and header enrichment are applied locally at specific network points where CEAs are deployed, rather than uniformly across the entire network. Each CEA enriches packet headers with QoE-relevant information only at its local measurement point, and selective monitoring is applied based on local traffic patterns and service requirements. This local quality approach improves QoE management productivity by focusing processing only where needed, while avoiding the complexity of uniform network-wide implementation.
Data Source
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AI summary
A method comprises analysing (110), in a network node, data flows related to a terminal device of a communication system, in order to detect data flows having at least one predefined characteristic. The network node selects (111) the data flows having the at least one predefined characteristic, as significant data flows on which customer experience CE measurements are to be performed.