Proactive Bypass Selection via Traceroute Root Cause Analysis
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Solution Overview
Problem
Current technologies struggle to accurately identify the root cause of degraded user experiences in network paths and effectively bypass problematic segments to prevent service level agreement (SLA) violations, as they rely on predictive models that do not provide insights into the underlying causes of network issues.
Innovation Solution
A device uses traceroute information and prediction models to identify segments causing degraded performance, obtains additional traceroute data to find bypass paths, and reroutes traffic to bypass these segments, employing a proactive approach to prevent SLA violations by analyzing network telemetry and user feedback to optimize quality of experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictive models are used to forecast SLA violations, then the ability to predict degraded user experience is improved, but the ability to identify the actual root cause of network problems deteriorates
Solution Approach 1:
The patent segments the network path into multiple trace segments using traceroute data, allowing identification of specific problematic segments rather than treating the entire path as a single unit. This segmentation enables pinpointing the exact location of network issues while maintaining prediction accuracy.
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes traceroute information to identify root causes. This intermediary layer acts as a bridge between prediction models and network operators, translating raw prediction data into actionable root cause information without losing predictive accuracy.
2Ease of operation
If traditional reactive approaches are used to handle network issues, then the simplicity of operation is maintained, but the loss of time in resolving SLA violations increases
Solution Approach 1:
The patent performs preliminary actions by proactively identifying and flagging problematic network segments before SLA violations occur. By using prediction models to forecast issues and pre-identifying bypass paths, the system prepares remediation actions in advance, reducing response time while maintaining operational simplicity through automated workflows.
Solution Approach 2:
The patent implements feedback mechanisms where traceroute information and performance data are continuously monitored and fed back into the prediction model. This feedback loop enables the system to learn from past performance and improve future predictions, maintaining simplicity through automated feedback processing while reducing resolution time.
3Measurement precision
If comprehensive traceroute analysis is performed to identify root causes, then the precision of problem localization is improved, but the complexity of the device increases
Solution Approach 1:
The patent divides the network path into discrete trace segments using traceroute data, enabling precise localization of problems by analyzing each segment independently. This segmentation approach improves measurement precision while managing complexity through modular analysis of individual segments rather than treating the entire path as one complex unit.
Solution Approach 2:
The patent extracts only the essential and relevant information from comprehensive traceroute data needed for root cause identification. By filtering and extracting key metrics and segments rather than processing all available data, the system achieves high precision in problem localization while minimizing device complexity through selective data processing.
Data Source
AI summary
In one embodiment, a device identifies, based on traceroute information for a path in a network between an endpoint client and an online application, a particular segment of the path as most likely to cause degraded performance along the path. The device makes, using a prediction model, a prediction that routing traffic for the online application via the path will result in degraded quality of experience for the online application. The device obtains, based on the prediction, additional traceroute information in the network, to identify a bypass path in the network between the endpoint client and the online application that bypasses the particular segment. The device causes traffic for the online application to be routed along the bypass path.