Telemetry Collection Feedback Tuning for Resource-Fault Balance
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Solution Overview
Problem
Existing systems face challenges in efficiently balancing compute resource usage with fault assurance in distributed microservice architectures, as statically configured collection parameters often lead to over-subscription or inadequate data collection, failing to meet service-level agreements and varying administrator requirements.
Innovation Solution
A distributed score model dynamically tunes telemetry collection parameters at the source using a feedback loop, incorporating adaptive score managers and intent-based models to optimize compute resource usage while ensuring fault assurance, considering user-defined SLAs and application objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If statically configured collection parameters are used, then data collection is simple to implement, but compute resources are over-subscribed or inadequate for fault assurance
Solution Approach 1:
The patent implements dynamic tuning of telemetry collection parameters using a feedback loop. The score model continuously adjusts collection parameters (such as collection interval and data types) based on real-time system state, transitioning from static configuration to adaptive dynamic adjustment. This resolves the contradiction by making the system both reliable through continuous optimization and implementable through automated feedback mechanisms.
Solution Approach 2:
The patent employs a feedback-based tuning mechanism where collection parameters are adjusted based on system performance metrics and fault assurance requirements. The feedback loop monitors system state and automatically tunes parameters to maintain optimal balance between resource usage and fault detection capability, resolving the implementation vs. reliability trade-off.
2Reliability
If telemetry collection parameters are increased to ensure fault assurance, then fault detection capability is improved, but compute resource usage increases
Solution Approach 1:
The patent changes collection parameters dynamically based on system state. Instead of using fixed high-level parameters that consume excessive resources, the system adjusts parameters (collection interval, data granularity, sampling rate) according to actual needs, achieving fault assurance only when necessary and reducing resource consumption during normal operation.
Solution Approach 2:
The system transitions from static parameter configuration to dynamic parameter adjustment through a score model that continuously optimizes collection parameters based on real-time system state, balancing fault detection capability with compute resource usage efficiency.
3Productivity
If telemetry collection parameters are decreased to minimize compute resource usage, then resource efficiency is improved, but fault assurance capability deteriorates
Solution Approach 1:
The patent implements parameter changes based on system state feedback. Collection parameters are reduced to minimize resource usage during normal operation but are automatically increased when fault indicators are detected, maintaining both resource efficiency and fault assurance capability through adaptive adjustment.
Solution Approach 2:
The feedback loop monitors system health metrics and automatically adjusts collection parameters to maintain fault assurance capability while minimizing resource consumption. When faults are detected, the system increases monitoring intensity; during normal operation, it reduces monitoring overhead for optimal resource efficiency.
4Adaptability or versatility
If dynamic tuning of collection parameters is implemented, then resource allocation flexibility is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service through an automated score model that performs dynamic parameter tuning without manual intervention. The system autonomously monitors its own state and adjusts collection parameters accordingly, providing adaptability while managing complexity through automation rather than manual configuration.
Solution Approach 2:
The feedback-based automatic tuning mechanism provides resource allocation flexibility by adapting to changing system conditions, while managing complexity through automated control loops that replace manual configuration and adjustment processes.
Data Source
AI summary
A method of tuning telemetry collection parameters may include, with a collector, receiving source data defining at least one application running on a plurality of nodes, the nodes utilizing a finite number of compute resources. With the collector, a number of score models within a scoring agent of the collector may be executed to define telemetry collection parameters used by the collector for source data collection. The method may also include computing, with the scoring agent, a number of scores based on disturbance features and adaptive feedback, and tuning, with the collector, the telemetry collection parameters based on the scores to obtain tuned telemetry collection parameters.