Dynamic Telemetry Sampling for Cloud Application Troubleshooting
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Solution Overview
Problem
Existing telemetry systems in cloud computing environments are inadequate for troubleshooting misbehaving applications, as they lack the ability to dynamically adjust sampling rates to gather detailed data during application misbehaviors, and there is a need for improved security against potential disturbances caused by malicious software.
Innovation Solution
A telemetry controller in the cloud computing environment adjusts telemetry data sampling frequency based on detected misbehaviors, such as application component restarts, using predefined thresholds and configuration objects to enhance data collection for troubleshooting and security monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If telemetry sampling rate is maintained at the lowest possible rate to conserve computational resources, then resource consumption is reduced, but the ability to gather detailed data for troubleshooting misbehaving applications deteriorates
Solution Approach 1:
The patent implements dynamic sampling rate adjustment where the telemetry system transitions from a static low sampling rate to a dynamic system that increases sampling rates in response to detected misbehaviors. The controller monitors application components and automatically elevates sampling rates when misbehaviors are detected, enabling detailed troubleshooting data collection only when needed while maintaining low resource consumption during normal operation.
Solution Approach 2:
The patent changes the sampling rate parameter from a fixed value to a variable parameter that adapts based on system conditions. The controller modifies the sampling rate parameter dynamically - maintaining low rates during normal operation and increasing them when misbehaviors are detected. This parameter change enables the system to balance between resource conservation and detailed data collection for troubleshooting.
2Measurement precision
If sampling rate is increased to gather detailed data for troubleshooting, then measurement precision for misbehaving applications is improved, but computational resource consumption increases
Solution Approach 1:
The patent implements a feedback mechanism where the controller continuously monitors application component behavior and uses this feedback to determine when to increase sampling rates. When misbehaviors are detected through monitoring, the system receives feedback about the misbehavior condition and responds by elevating sampling rates. This feedback loop ensures that high measurement precision is achieved only when misbehaviors are present, avoiding unnecessary resource consumption during normal operation.
Solution Approach 2:
The system performs preliminary monitoring at low sampling rates to detect misbehaviors before initiating detailed data collection. The controller proactively identifies potential issues through continuous monitoring and preemptively increases sampling rates when misbehaviors are detected, ensuring that detailed troubleshooting data is available when needed without maintaining high resource consumption during normal operation.
3Adaptability or versatility
If manual intervention is used to change sampling rate, then adaptability to specific troubleshooting needs is improved, but system response time to misbehaviors deteriorates
Solution Approach 1:
The patent implements self-service functionality where the telemetry system automatically adjusts sampling rates without requiring manual intervention. The controller independently monitors application components, detects misbehaviors, and autonomously modifies sampling rates accordingly. This self-service capability eliminates the time delay associated with manual configuration changes while maintaining the adaptability needed for different troubleshooting scenarios.
Solution Approach 2:
The system performs preliminary configuration of monitoring capabilities during deployment, setting up the framework for automatic response. When misbehaviors occur, the pre-configured automated response mechanism immediately activates, eliminating the need for manual intervention and reducing response time. The preliminary setup includes defining misbehavior detection criteria and sampling rate adjustment policies that are automatically executed when conditions are met.
Data Source
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AI summary
A method for adapting telemetry settings in a cloud computing environment is provided. The method is carried out by a telemetry controller within the cloud computing environment. The method comprises receiving information on at least one misbehavior of an application component, determining, based on the received information, whether to modify a sampling frequency of telemetry data for the application component, and if it is determined to modify the sampling frequency of telemetry data for the application component, initiating modification of the sampling frequency to a modified value.