QoE Policy Engine for Custom Applications Based on Feedback
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Solution Overview
Problem
Network administrators face challenges in defining service level agreement (SLA) threshold values for applications due to the lack of effective tools for evaluating quality of experience (QoE), leading to arbitrary policy configurations that do not accurately reflect user experience in real-life networks.
Innovation Solution
A QoE policy engine that injects predefined network impairments during evaluation sessions, collects live user feedback, and correlates it with network conditions to generate application-specific SLA thresholds for custom applications, optimizing network policies based on user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If arbitrary SLA thresholds are used for policy configuration, then policy configuration can be performed, but the accuracy of user experience reflection is poor
Solution Approach 1:
The system implements a feedback mechanism where user experience data is collected from feedback sources, correlated with network impairments, and used to generate SLA threshold recommendations. This closed-loop feedback process enables the system to learn from actual user experiences and automatically refine SLA threshold definitions, transforming arbitrary configurations into data-driven, accurate representations of user experience requirements.
Solution Approach 2:
The system performs preliminary QoE evaluation sessions before finalizing SLA thresholds. By conducting these evaluation sessions in advance with predefined network impairments and collecting feedback from multiple sources, the system prepares and validates SLA threshold recommendations before they are applied to actual network policies, ensuring accuracy is achieved without compromising operational ease.
2Measurement precision
If QoE evaluation sessions with multiple feedback sources are conducted, then accurate SLA thresholds can be determined, but evaluation time and complexity increase
Solution Approach 1:
The evaluation process is segmented into distinct phases: predefined impairment scenarios are defined, feedback sources are identified, correlation analysis is performed, and SLA recommendations are generated. This segmentation allows the system to efficiently manage the complex evaluation process by breaking it down into manageable steps that can be executed systematically without excessive time consumption.
Solution Approach 2:
The system uses predefined impairment scenarios that replicate common network conditions as copies of real-world network states. By evaluating against these standardized scenarios rather than requiring exhaustive testing of all possible network conditions, the system achieves accurate SLA threshold determination while significantly reducing the time and complexity of evaluation.
3Measurement precision
If network impairments are injected for evaluation, then QoE can be measured under controlled conditions, but network performance may be temporarily degraded
Solution Approach 1:
The system injects network impairments selectively and partially - only during dedicated evaluation sessions for specific feedback sources, rather than continuously degrading network performance. By applying impairments only when needed for measurement purposes and using controlled, measured levels of degradation, the system achieves accurate QoE measurement while minimizing impact on overall network reliability.
Solution Approach 2:
The evaluation function is extracted from normal network operation. By separating QoE measurement activities into distinct evaluation sessions that can be independently controlled and terminated, the system allows network impairments to be injected for measurement purposes without permanently affecting network reliability. The evaluation process can be paused or stopped when measurement objectives are achieved.
Data Source
AI summary
In one embodiment, a method herein may comprise: causing, for a quality-of-experience evaluation session, one or more network impairments to be injected according to a set of predefined scenarios on application traffic for a plurality of feedback sources that are using a particular application in a computer network; obtaining experience-based feedback from the plurality of feedback sources for the quality-of-experience evaluation session; correlating the experience-based feedback with the one or more network impairments to produce an evaluation result for the quality-of-experience evaluation session; and generating a quality-of-experience-based network policy recommendation for the particular application based on the evaluation result.