Correlating Client and Host Performance Data for Network Issue Identification
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Solution Overview
Problem
Network-based service administrators face challenges in diagnosing issues affecting client devices and intermediary networks due to lack of direct access and the diversity of client devices and network infrastructures, which complicates providing a consistent experience and troubleshooting performance problems.
Innovation Solution
A system and method for collecting and correlating performance data from both client devices and host devices, using identifiers to facilitate analysis and presentation of interactions, enabling administrators to diagnose issues across the network-based services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If administrators review only host device log files to diagnose issues, then host device configuration issues can be identified, but client device and network performance issues cannot be detected
Solution Approach 1:
The system segments performance monitoring into separate data collection components on client devices and host devices, then correlates them through a common data platform. This allows independent collection of client-side metrics (app performance, network conditions) and host-side metrics (server logs, system events) to be integrated for comprehensive issue diagnosis.
Solution Approach 2:
A data intake and query system acts as an intermediary platform that receives, normalizes, and correlates performance data from both client devices and host devices. This intermediary enables administrators to query and analyze correlated data from both sources without direct access to client devices, resolving the visibility gap.
2Adaptability or versatility
If the system supports diverse client device types and network connections, then client compatibility is improved, but diagnostic complexity increases
Solution Approach 1:
The system implements a universal data collection framework that works across diverse client devices (mobile, desktop, tablets) and network types (cellular, Wi-Fi, Ethernet). A standardized data model and common identifier system enable consistent performance tracking regardless of device type or network connection, simplifying diagnostics despite diversity.
3Loss of information
If performance data is collected from both client and host devices, then system-wide performance visibility is improved, but data correlation complexity increases
Solution Approach 1:
The system implements feedback mechanisms where performance data from client devices is correlated with host device data using common identifiers. This feedback loop allows the system to automatically match client-reported issues with corresponding server logs and system events, reducing manual correlation complexity while maintaining data completeness.
Data Source
AI summary
A data intake and query system collects performance data from client devices and host devices and stores the performance data in one or more indexes. The system is further configured to facilitate correlation of the performance data collected from the client devices and the separate performance data collected from the host devices. For example, based on a determination that one or more identifiers stored in a portion of performance data received from client devices match one or more identifiers stored in a portion of the performance data received from host devices, a data intake and query may determine that the data portions are related. The portions of performance data, for example, may correspond to events the data intake and query system derives from the performance data collected from both client devices and host devices.