Dynamic Caching Modification via Request History Analysis

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Solution Overview

Problem

In network-based applications with complex, distributed environments, caching efficiency is compromised due to changes in input data, leading to performance issues and potential violations of privacy regulations, as intermediate services may not handle all input data correctly, affecting cache hit rates and storage requirements.

Innovation Solution

A caching evaluation system analyzes the history of requests and responses to determine modifications such as cache configuration, parameter changes, and placement, optimizing caching by identifying frequently used parameters and reducing unnecessary data forwarding, thereby improving cache efficiency and compliance with data handling standards.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If caching is implemented in distributed services, then performance is improved, but cache hit rates decrease due to changes in input data

Engineering Contradiction:

ImproveperformanceVSAvoidcache hit rates

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system dynamically changes cache parameters including configuration, placement, and size based on analyzed request patterns. By monitoring historical data and identifying frequently accessed parameters, the system adapts cache behavior to maintain high hit rates despite input data variations.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system implements feedback loops where cache performance metrics are continuously monitored and analyzed. This feedback drives automated adjustments to cache configuration and parameter selection, enabling the system to respond to changing data patterns and maintain optimal performance.

Inventive Principle:

Principle #23Feedback

2Loss of information

If all input data is forwarded through intermediate services, then data completeness is maintained, but storage requirements increase and privacy compliance becomes difficult

Engineering Contradiction:

Improvedata completenessVSAvoidstorage requirements

Core Design Contradiction:

Loss of informationVSQuantity of substance

Solution Approach 1:

The system extracts and identifies only the essential parameters from input data that are actually needed for service operations. By analyzing request patterns and determining which parameters are frequently used, the system forwards only those critical data elements, reducing storage requirements while maintaining operational completeness.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

Different intermediate services receive different subsets of parameters based on their specific needs. The system tailors the data forwarded to each service according to its function, ensuring each service gets exactly what it needs without receiving unnecessary data, thereby reducing overall storage requirements while maintaining local data completeness.

Inventive Principle:

Principle #3Local quality

3Reliability

If cache size is increased to handle data variations, then cache hit rates improve, but storage requirements increase

Engineering Contradiction:

Improvecache hit ratesVSAvoidstorage requirements

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system implements dynamic cache sizing where cache capacity is automatically adjusted based on analyzed request patterns and data frequency. Rather than maintaining a static large cache, the system dynamically allocates storage to cache the most frequently accessed data, achieving high hit rates with optimized storage utilization.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes cache parameters including size, configuration, and placement based on empirical analysis of request patterns. By identifying which parameters and data elements are most frequently accessed, the system optimizes cache allocation to achieve high hit rates without requiring excessive storage capacity.

Inventive Principle:

Principle #35Parameter changes

4Productivity

If intermediate services handle all input data, then data processing is comprehensive, but privacy regulation compliance becomes difficult

Engineering Contradiction:

Improvedata processingVSAvoidprivacy regulation compliance

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The system extracts and identifies only the minimum necessary parameters required for service processing. By analyzing which data elements are actually needed for operations, the system forwards only those essential parameters, reducing exposure of sensitive information and facilitating privacy compliance while maintaining comprehensive data processing for necessary functions.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

Each intermediate service receives a tailored subset of parameters specific to its processing needs. This localized data distribution ensures that services only handle the minimum necessary data for their function, reducing privacy risks while maintaining comprehensive overall data processing across the service chain.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS11526446B1Modifying caching amongst services from a history of requests and responses

Publication Date: 2022.12.13 AMAZON TECH INC

AI summary

Modifications to caching performed between different services may be determined. A history of requests and responses between the different services may be obtained. The history may be evaluated to determine respective frequencies of parameters between the services. The frequencies of parameters may be evaluated to determine one or more modifications to caching among the different services. The modifications may be provided in order to be applied to change caching performance for subsequent requests.