Metric Data Management Engine for Utilization-Based Tiered Storage

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Solution Overview

Problem

Centralized metrics management platforms face challenges in efficiently managing growing volumes of metric data, leading to increased storage, processing, and bandwidth requirements, which can result in higher costs and performance degradation.

Innovation Solution

The implementation of a metrics management engine that analyzes metric utilization information to create and apply metric handling rules, such as storing metric data in different tiers of a multi-tier storage architecture, sampling, or compressing data, based on predicted future utilization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If all metric data is stored in a centralized platform, then complete visibility and access to metric data is achieved, but storage costs and processing overhead increase significantly

Engineering Contradiction:

Improvemetric data accessibilityVSAvoidstorage volume

Core Design Contradiction:

Loss of informationVSQuantity of substance

Solution Approach 1:

The patent segments metric data storage across multiple locations (centralized platform, edge devices, and distributed storage) rather than consolidating all data in one place. This allows organizations to maintain access to metric data while reducing the storage burden on any single system and lowering overall storage costs.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements local caching and edge storage of metric data, where frequently accessed metric data is stored locally at edge devices or regional servers. This enables fast access to commonly needed metric data while reducing the need to store and transfer all metric data through the centralized platform, thereby reducing storage costs and processing overhead.

Inventive Principle:

Principle #3Local quality

2Reliability

If metric data is processed and stored in real-time, then complete visibility into device performance is achieved, but processing overhead and bandwidth requirements increase

Engineering Contradiction:

Improvemetric data accuracyVSAvoidprocessing power

Core Design Contradiction:

ReliabilityVSPower

Solution Approach 1:

The patent applies preliminary filtering and aggregation of metric data at the edge devices before data is transmitted to the centralized platform. By pre-processing metric data locally (filtering out redundant data, aggregating time-series data), the system reduces the volume of data requiring further processing, thereby lowering processing overhead and bandwidth requirements while maintaining the reliability of the metric data that is transmitted.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements selective processing where only certain metric data that meets specific criteria (e.g., anomaly detection, threshold violations, frequently accessed metrics) is processed and transmitted in real-time. Less critical metric data is processed asynchronously or stored for batch processing, reducing the immediate processing power requirements while still maintaining overall data reliability.

Inventive Principle:

Principle #16Partial or excessive action

3Productivity

If a multi-tier storage architecture is implemented, then storage efficiency is improved, but system complexity increases

Engineering Contradiction:

Improvestorage efficiencyVSAvoidstorage architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements automated data lifecycle management where the system automatically moves metric data between storage tiers (hot, warm, cold storage) based on access patterns, age of data, and priority levels. This self-service approach to data management reduces the need for manual intervention and complex configuration, making the multi-tier storage architecture easier to operate while maintaining high storage efficiency.

Inventive Principle:

Principle #25Self-service

4Duration of action of stationary object

If metric data retention period is extended, then historical analysis capability is improved, but storage costs and data management complexity increase

Engineering Contradiction:

Improvedata retention periodVSAvoiddata management complexity

Core Design Contradiction:

Duration of action of stationary objectVSDevice complexity

Solution Approach 1:

The patent segments retained metric data into different storage tiers and categories based on age, access frequency, and importance. Historical data is automatically moved to lower-cost cold storage while recent and frequently accessed data remains in faster storage tiers. This segmentation allows extended retention periods without proportionally increasing management complexity, as each segment can be managed with appropriate policies.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12346580B2Systems and methods for managing metric data

Publication Date: 2025.07.01 BITDRIFT INC

AI summary

A computing platform may be configured to (i) receive metric data for a metric that was produced by a metric producer, (ii) identify a metric handling rule that applies to the metric, wherein the identified metric handling rule comprises a handling action of storing metric data for the metric in a specified storage location (e.g., a different tier of a multi-tier storage architecture), and (iii) handle the received metric data for the metric in accordance with the identified metric handling rule by storing the received metric data in the specified storage location.