Understanding the user experience is the key to optimized application performance and issue resolution. End-User Experience Monitoring solutions from VIAVI utilize powerful machine learning to deliver real-time insights through a single, intuitive score.
End-User Experience Monitoring from VIAVI
Monitoring, troubleshooting, and complaint resolution activities stretch IT teams to their limits. Remote working and SaaS cloud migration only add to this burden. It is no longer feasible to assess network health by reviewing endless lists of KPIs with no clear focus or direction. A simple and effective method of End-User Experience monitoring to gauge the actual usability experience of our end-users is the priority. The End-User Experience solution from VIAVI automatically interprets volumes of complex packet data, and produces a simple yet accurate and actionable score via our patented algorithm. The results are provided via intuitive workflows and out-of-the-box and customizable dashboards. Issues are quickly isolated to the network, application, server, or client domain to enable efficient and proactive IT support.
Important End-User Experience Monitoring Metrics
The volume and diversity of data available to IT teams provide more detailed levels of analytics than were previously possible. While overwhelming when viewed individually, intelligently weighing the most critical End-User Experience metrics provides IT staff with the critical and actionable items needed to improve or restore the End-User's experience.
- Flow records capture important network conversation elements including IP addresses, protocol data, timestamps, and device IDs. VIAVI enriched-flow records accept and combine flow, Syslog, SNMP, AD, and other network element meta-data into a “super record” to establish relationships between the username, IP, MAC, and application.
- Packet Data also contains important user, device, and source IDs along with in-depth browser, application, and VoIP information from the packet payload. Performance indicators such as jitter, loss, and retransmissions can be assessed based on the behavior and content of the packets. The VIAVI GigaStor packet capture, analysis, and storage appliance features industry-leading line-rate capture, data storage, and forensic analysis capabilities.
Application Performance from a User Perspective
End-User Experience monitoring tools from VIAVI make it easy to navigate from a high-level executive dashboard to the root cause of issues jeopardizing end-user satisfaction in just a few steps:
- Review Scores: The dashboard visually displays which applications, tiers, and sites are performing poorly based on end-user monitoring. Color coding highlights any problem areas.
- Review Performance Details: Clicking on any End-User Experience monitoring score brings up a domain scorecard with a breakdown of network, client, server, and application performance. Details on what led to any score deductions are also provided.
- Drill Down to Errors: Once the nature of the problem is identified on the scorecard, auto-generated application/server dependency maps illustrate the source of the issue in greater detail.
- Connection Dynamics: Individual network conversations and their associated user experience scores can be reviewed by selecting “connection dynamics” from the drill-down arrow for any network link. Areas of concern that help identify root cause can be viewed graphically.
- Packet Capture: Traffic forensics, GigaFlow analysis, and trace extraction can also be selected from the drill-down arrow. When packet data is desired for in-depth analysis, unabridged PCAP files can be accessed via a single click.
End-User Experience Monitoring Approach from VIAVI
Using singular data sources or attempting to leverage hundreds of metrics independently are common historical strategies for evaluating user experience. Neither approach supports efficient monitoring or accurate root cause analysis.
- VIAVI end-user experience monitoring software uses adaptive machine learning to run dozens of KPI inputs through multiple algorithms, resulting in one intuitive and meaningful score. This eliminates the noise and false positives that can lessen the value of traditional experience scoring methods.
- End-User Experience visibility in the cloud is hindered by a lack of ownership and disparate data sources. The Observer platform now supports EUE scoring in AWS cloud environments.
- New scoring innovations continue to evolve as analysis methods improve. Artificial intelligence and machine learning, while still in their infancy, are the ideal catalysts for End-User Experience monitoring improvements.
Patented End-User Experience Scoring
The single numeric score generated by Observer Apex is calculated based on analyzing multiple network KPIs.This approach leads to highly accurate End-User Experience scoring results and the ability to quickly isolate the problem domain to Application, Server, Network, or client.
- Scores ranging from 1-10 can reflect a single user’s experience or a group of users defined by site, geolocation, etc.
- 'Red scores' identify the most urgent issues, while yellow scores indicate a less serious (marginal) degradation.
- Scores are calculated in real-time and retained over extended periods to assure both short and long-term end-user high-fidelity satisfaction. .
End-User Experience Monitoring Tools
By focusing on end-user satisfaction and customer collaboration, VIAVI has defined the performance metrics that matter most. Industry-leading network performance monitoring solutions have converted this expertise and insight into unprecedented visibility and optimized service delivery.
- Observer Apex is the first network performance monitoring solution to generate an end-user experience score for every transaction. By integrating multiple data sources (such as packet and flow), Apex provides the industry’s most comprehensive end to end-user experience monitoring. Intuitive dashboards are easily customizable to address specific business and IT priorities. Observer network forensics support deep dive investigations with high fidelity data sources.