Enterprise Network Experience Management via Composite Service Quality Index
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Solution Overview
Problem
Existing techniques for assessing end-user experience in enterprise networks are inadequate as they rely on manual surveys that provide non-scientific feedback, failing to provide comprehensive insights into network performance and service quality, and do not address network issues holistically.
Innovation Solution
A system and method for proactive management of enterprise networks that collects performance data from multiple devices and services, transforms it into a conducive format, determines key performance indicators (KPIs) and key quality indicators (KQIs), generates alerts based on threshold comparisons, computes a composite service quality (CSQ) index, and identifies actionable items to address detected problem scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual surveys are conducted to assess end-user experience, then feedback can be obtained from users, but the feedback is non-scientific and does not provide consistent information over time
Solution Approach 1:
The patent replaces manual survey methods with an automated system that uses software agents to collect, transform, and analyze performance data from network devices. This substitution of mechanical/manual processes with automated computational systems enables continuous, objective, and scientifically valid measurement of network performance and end-user experience without relying on subjective user feedback.
2Reliability
If multiple network devices and services are monitored separately, then individual device performance can be tracked, but a holistic view of network performance and end-user experience is not achieved
Solution Approach 1:
The patent merges monitoring of multiple network devices, applications, and services into a unified system. The software agent collects performance data from various sources, transforms it into a common format, and integrates it to provide a holistic view of network performance and end-user experience. This combining approach maintains individual device monitoring capabilities while adding comprehensive network-level assessment.
Solution Approach 2:
The monitoring system is designed with multi-functionality to handle diverse network devices, applications, and services through a universal approach. The software agent can collect performance data from multiple types of network elements, transform various data formats into a standardized structure, and analyze combined data to assess both individual device performance and overall network health, making the system universally applicable across heterogeneous network environments.
3Measurement precision
If network performance is assessed by expertise analysis of various inputs, then professional evaluation can be performed, but fast alleviation of end-user experience degradation is not enabled
Solution Approach 1:
The system enables self-service monitoring and assessment through automated software agents that continuously collect performance data, transform it into standardized formats, analyze it against predefined thresholds, and generate alerts without requiring expert intervention. This automation allows the network monitoring system to independently detect performance degradation and notify relevant personnel, significantly reducing the time required for detection and response compared to manual expert analysis.
Solution Approach 2:
The system implements continuous feedback loops where performance data is constantly collected, analyzed, and used to generate alerts when thresholds are breached. This real-time feedback mechanism enables rapid detection of end-user experience degradation and immediate notification to stakeholders, allowing for fast response and alleviation of performance issues without waiting for periodic expert assessments.
Data Source
AI summary
A method for improved end-user experience in an enterprise network is disclosed. In one embodiment, key performance indicators (KPIs) of network devices, applications and input services are determined. Further, KPIs of base services are determined using the KPIs of network devices, applications and input services. Furthermore, the KPIs are compared with KPI threshold levels and KPI level alerts are generated. Moreover, key quality indicators (KQIs) of base services are determined using the KPIs. Also, the KQIs are compared with KQI threshold levels and KQI level alerts are generated. Further, a composite service quality (CSQ) index of a composite service is computed using the KQIs. Furthermore, the CSQ index is compared with CSQ index threshold levels and a CSQ level alert is generated. In addition, possible problem conditions are detected and then possible problem scenarios are determined and analyzed. Also, actionable items are identified based on the possible problem scenarios.