Self-serve diagnostics for cloud workload telemetry
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Solution Overview
Problem
Customers face difficulties in diagnosing and resolving issues with their cloud workloads due to the complexity of cloud environments and the lack of actionable insights from existing monitoring and logging platforms.
Innovation Solution
A method that includes using a detector to analyze backend telemetry data and generate insights for issues observed, providing an interactive interface for users to interact with detectors and perform recommended actions, and displaying a resource mesh to visualize resource dependencies and health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If customers use traditional monitoring and logging platforms to observe cloud workload issues, then they can collect raw data, but they struggle to map symptoms, isolate problems, and find fixes due to lack of actionable insights
Solution Approach 1:
The system enables customers to self-diagnose and self-resolve cloud workload issues by providing automated detectors that generate actionable insights and recommended actions directly from their telemetry data, eliminating the need for manual troubleshooting by support engineers
Solution Approach 2:
The system introduces an intermediary layer of automated detectors and insight generators that sit between the raw telemetry data and the customer, transforming complex raw data into actionable insights and recommended actions that customers can understand and execute
2Productivity
If customers manually troubleshoot cloud workload issues, then they can investigate problems step-by-step, but it increases downtime and reduces productivity
Solution Approach 1:
The system performs preliminary analysis of telemetry data continuously through automated detectors, so when an issue occurs, the detectors have already prepared insights and recommended actions that can be immediately executed to resolve the issue quickly
Solution Approach 2:
The system provides continuous feedback to customers through automated detectors that monitor workload health and immediately generate actionable insights when issues are detected, enabling rapid response and resolution without manual investigation delays
3Measurement precision
If customers use detailed telemetry data for diagnosis, then they have comprehensive information, but it becomes difficult to interpret and act on the information
Solution Approach 1:
The system extracts the most critical and actionable information from the comprehensive telemetry data by analyzing it through specialized detectors, separating the valuable actionable insights from the overwhelming raw data to make troubleshooting easy while maintaining diagnosis accuracy
Solution Approach 2:
The system applies different levels of analysis to different parts of the telemetry data through specialized detectors, generating detailed insights only where needed while providing high-level summaries elsewhere, making the overall system easy to operate while maintaining precision where required
Data Source
AI summary
The systems and methods relate to a self-serve diagnostic experience that enables users to help themselves when issues or problems emerge with a customer workload. The systems and methods provide an interactive interface that guides users through a troubleshooting journey. Users may enter a problem with a customer workload using the interactive interface and may receive one or more insights automatically generated by one or more detectors based on an analysis of the backend telemetry data for the customer workload. The insights may provide contextual information about the issues and recommendations for steps to fix the issues. The interactive interface may also provide a visual overview of a plurality of resources, the resource dependencies, and the resource health for the plurality of resources. The systems and methods may also guide users in building one or more detectors for troubleshooting the one or more issues.