Telemetry Sampling Policy Coordination for Distributed Storage Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sampling policies for telemetry data in distributed storage networks (DSNs) are statically configured, leading to inefficiencies due to human error, continuous monitoring needs, and failure to adapt to changing system conditions, resulting in ineffective use of resources and potential missed critical events.
Innovation Solution
A global coordinator that receives and transmits coordination messages to adjust sampling policies dynamically, aggregating information from multiple DSTN managing units and providing adaptive sampling policies to optimize telemetry data transmission and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static sampling policies are used for telemetry data, then configuration is simple, but the system cannot adapt to changing conditions and resources are used inefficiently
Solution Approach 1:
The patent implements dynamic sampling policies that automatically adjust based on real-time system conditions. The global coordinator receives telemetry data, analyzes current system state, and dynamically modifies sampling rates and thresholds without requiring manual reconfiguration, enabling the system to adapt to changing workloads and conditions automatically
Solution Approach 2:
The system performs self-configuration through automated policy adjustment. The global coordinator autonomously analyzes telemetry data and adjusts sampling policies without human intervention, and the DSTN managing units automatically apply received policy updates, eliminating the need for continuous manual monitoring and reconfiguration
2Reliability
If continuous monitoring of sampling policies is performed, then system efficiency is maintained, but additional computational load and resource consumption occur
Solution Approach 1:
The system implements periodic sampling policy reviews triggered by specific conditions rather than continuous monitoring. The global coordinator evaluates whether policy adjustments are needed based on accumulated telemetry data and system state changes, performing adjustments only when necessary to maintain effectiveness while minimizing unnecessary computational overhead
Solution Approach 2:
The system uses feedback from telemetry data to intelligently determine when policy adjustments are needed. The global coordinator analyzes incoming data patterns and system performance metrics, adjusting sampling policies only when the feedback indicates improved effectiveness is achievable, thereby maintaining reliability while avoiding wasteful continuous reconfiguration cycles
3Ease of manufacture
If manually configured sampling policies are used, then initial setup is straightforward, but human error occurs and policies become ineffective over time
Solution Approach 1:
The system performs self-configuration through automated policy adjustment. The global coordinator autonomously analyzes telemetry data and adjusts sampling policies without human intervention, eliminating human error while maintaining initial setup simplicity through automatic adaptation to actual system conditions
Solution Approach 2:
The patent replaces manual mechanical configuration with automated electronic adjustment. The global coordinator uses software-based policy generation and distribution, substituting human operators with an automated system that analyzes data and generates optimized policies, thereby eliminating human error while maintaining ease of initial setup through automated processes
4Loss of information
If all telemetry data is transmitted continuously, then complete information is available, but network congestion occurs and resources are wasted
Solution Approach 1:
The system dynamically changes sampling parameters based on system conditions and policy adjustments. The global coordinator modifies sampling rates, data retention periods, and transmission intervals according to current workload and network conditions, optimizing the balance between information completeness and resource consumption
Solution Approach 2:
The sampling policy itself is dynamic rather than static. The system adjusts which data points are collected, how frequently they are sampled, and when they are transmitted based on real-time conditions, ensuring critical information is captured while minimizing unnecessary data transmission and network usage
Data Source
AI summary
In some implementations, a global coordinator may receive, from a distributed storage and task processing network (DSTN) managing unit, a first coordination message that indicates currently configured sampling policy information for the DSTN managing unit. The global coordinator may transmit, to an analytics agent, the currently configured sampling policy information for the DSTN managing unit. The global coordinator may receive, from the analytics agent, adjusted sampling policy information for the DSTN managing unit. The global coordinator may transmit, to the DSTN managing unit, a second coordination message that indicates the adjusted sampling policy information for the DSTN managing unit.