Cache Units with Dynamic State Transitions

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Solution Overview

Problem

Existing cache systems are inefficient in dynamically adapting to changing request patterns over time, leading to suboptimal data eviction strategies that can increase the load on data stores due to inefficient handling of requests for different types of data.

Innovation Solution

Implementing a cache system with multiple defined states for cache units that can switch between states based on hit and miss ratios, allowing for dynamic data eviction and updating of cache contents to prioritize frequently requested data, thereby reducing the load on the data store.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a cache system uses a single state for all cache units, then the system is simpler to manage, but it cannot dynamically adapt to changing request patterns, leading to suboptimal data eviction strategies

Engineering Contradiction:

Improvedynamic adaptation to request patternsVSAvoidcache state management complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements multiple cache states (e.g., hot, warm, cold) that cache units can transition between based on access patterns. This dynamic state management allows the cache to adapt to changing request patterns while maintaining manageable complexity through defined state transition rules and automated monitoring of hit/miss ratios.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes the state parameter of cache units based on performance metrics such as hit ratios and access frequency. By monitoring these parameters and transitioning cache units between states accordingly, the system achieves adaptability without requiring complex manual intervention, as the state changes are driven by automated parameter thresholds.

Inventive Principle:

Principle #35Parameter changes

2Productivity

If cache units frequently switch states to optimize for frequently requested data, then the load on the data store is reduced, but the complexity of managing cache contents increases

Engineering Contradiction:

Improvedata retrieval efficiencyVSAvoidcache content management complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The cache system continuously monitors performance feedback such as hit ratios and access patterns for each cache unit. Based on this feedback, the system automatically makes state transition decisions, adjusting cache contents to prioritize frequently requested data. This feedback mechanism improves data retrieval efficiency while keeping management complexity manageable through automated decision-making based on clear performance thresholds.

Inventive Principle:

Principle #23Feedback

3Reliability

If the cache system maintains detailed state information for each cache unit, then data eviction strategies are optimized, but the overhead for tracking and managing states increases

Engineering Contradiction:

Improveeviction strategy effectivenessVSAvoidstate tracking overhead

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system tracks state information selectively based on access patterns and performance metrics. Rather than maintaining exhaustive state information for all possible scenarios, the cache monitors key parameters such as hit ratios and transitions states when predefined thresholds are met. This partial monitoring approach maintains reliable eviction strategies while minimizing the time overhead associated with state tracking.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS10339055B2Cache system with multiple cache unit states

Publication Date: 2019.07.02 RED HAT INC

AI summary

A cache system stores a number of different datasets. The cache system includes a number of cache units, each in a state associated with one of the datasets. In response to determining that a hit ratio of a cache unit drops below a threshold, the state of the cache unit is changed and the dataset is replaced with that associated with the new state.