Autonomous Infrastructure Module for Storage Feature Telemetry
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Solution Overview
Problem
Current systems lack the ability to effectively determine feature usage across multiple customer sites for storage systems, hindering development and enhancement efforts as there is no visibility into how features are utilized or why they are not used as expected, and existing data sources fail to provide insights into customer workload patterns or feature benefits.
Innovation Solution
Implementing an autonomous infrastructure module in each storage system to collect and aggregate data on feature usage, generating autonomous field telemetry reports that are forwarded to an analytics engine for parsing and visualization, enabling the creation of analytics and dashboards to analyze feature usage and operational patterns across multiple sites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional data collection methods are used, then system simplicity is maintained, but visibility into feature usage and customer workload patterns is lost
Solution Approach 1:
The system segments data collection by deploying autonomous infrastructure modules (AIMs) at each customer site that independently collect and aggregate telemetry data locally before transmitting to the analytics engine. This segmentation allows distributed data collection without requiring centralized control of each data point, resolving the contradiction between gaining comprehensive feature usage visibility and maintaining system simplicity.
Solution Approach 2:
The autonomous infrastructure modules operate independently at customer sites, self-managing data collection, aggregation, and local processing without requiring manual intervention or complex centralized configuration. Each AIM service autonomously pushes telemetry data when storage thresholds are reached, enabling the system to gain extensive feature usage information while keeping operational complexity minimal.
2Measurement precision
If comprehensive telemetry data is collected from all storage systems, then feature usage analysis capability is improved, but data transmission volume and processing load increase
Solution Approach 1:
The autonomous infrastructure modules perform preliminary data aggregation and filtering at the source before transmission. Each AIM collects telemetry data locally, aggregates it by storage system and feature, and only transmits consolidated results to the analytics engine. This preliminary action at the edge reduces the volume of data that needs to be transmitted and processed centrally, while maintaining precise feature usage measurement capabilities.
3Productivity
If manual data collection methods are used, then system complexity is reduced, but insight generation time and productivity are decreased
Solution Approach 1:
The system implements automated feedback loops where the analytics engine receives telemetry data, generates insights about feature usage and customer workload patterns, and stores results in databases for ongoing analysis. This automated feedback mechanism continuously improves insight generation speed without requiring manual data collection, resolving the contradiction between productivity enhancement and system complexity through automation.
Data Source
AI summary
A method of determining feature usage on a set of storage systems deployed across multiple customer sites includes defining metrics related to the features of interest, and pushing the defined metrics to an AIM (Autonomous Infrastructure Module) of an operating system of each storage system. The AIM on each storage system collects data associated with the metrics from the operating system on the storage system. The collected data is aggregated and formatted by the AIM and then used to create an autonomous field telemetry report. Autonomous field telemetry reports are periodically forwarded on a communication network to an analytics engine. The analytics engine parses each autonomous field telemetry report to extract usage information related to the features of interest, loads the parsed data to PostgreSQL staging and historical databases, and uses the parsed data alone or in combination with the historical data to create analytics and visualizations of the analytics.