Predictive Cache Management for Dynamic Memory Optimization

Solution Overview

Problem

Current cache management systems face challenges such as memory occupancy issues due to reserved caches, complex memory management, and inaccurate prediction of hot data, leading to inefficient system performance and data inconsistency.

Innovation Solution

A method that dynamically adjusts cache data based on predicted operation requests by analyzing historical user operation requests, identifying patterns, and pre-caching data for frequently accessed resources, thereby optimizing memory usage and improving system performance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If a reserved cache is employed to improve system performance, then data access speed is improved, but memory occupancy increases and memory management complexity increases

Engineering Contradiction:

Improvedata access speedVSAvoidmemory occupancy

Core Design Contradiction:

SpeedVSQuantity of substance

Solution Approach 1:

The patent implements dynamic cache management by replacing static reserved cache with a predictive system that adapts cache content based on user behavior patterns. The system continuously learns from historical operation requests and dynamically adjusts which data to cache, transforming the cache from a fixed resource into a flexible, adaptive component that optimizes memory usage while maintaining fast access speeds.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system employs machine learning models that automatically identify hot data and predict future access patterns without human intervention. The cache management becomes self-service through automated prediction algorithms that analyze user behavior and autonomously determine which data should be cached, eliminating the need for manual cache configuration and reducing memory management complexity.

Inventive Principle:

Principle #25Self-service

2Speed

If a reserved cache is employed to improve system performance, then data access speed is improved, but memory management complexity increases

Engineering Contradiction:

Improvedata access speedVSAvoidmemory management complexity

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The system employs machine learning models that automatically identify hot data and predict future access patterns without human intervention. The cache management becomes self-service through automated prediction algorithms that analyze user behavior and autonomously determine which data should be cached, eliminating the need for manual cache configuration and reducing memory management complexity.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent implements a feedback mechanism where the system continuously monitors actual user access patterns and compares them with predictions. This feedback loop allows the machine learning models to learn from past performance and continuously improve prediction accuracy, automatically optimizing cache management without requiring complex manual intervention or tuning.

Inventive Principle:

Principle #23Feedback

3Speed

If engineers predictively identify hot data based on industrial experiences to store in cache, then data access speed may be improved, but prediction accuracy is low due to reliance on subjective experience

Engineering Contradiction:

Improvedata access speedVSAvoidprediction accuracy

Core Design Contradiction:

SpeedVSMeasurement precision

Solution Approach 1:

The patent replaces the mechanical/cognitive process of human expert prediction with an automated machine learning system. Instead of relying on engineers' industrial experiences and subjective judgment, the system uses algorithms that objectively analyze historical operation requests and user behavior patterns, substituting human cognitive limitations with computational accuracy and consistency.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent implements a feedback mechanism where the system continuously monitors actual user access patterns and compares them with predictions. This feedback loop allows the machine learning models to learn from past performance and continuously improve prediction accuracy, automatically optimizing cache management without requiring complex manual intervention or tuning.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11347646B2Method, device and computer program product for managing cache based on matching API

Publication Date: 2022.05.31 EMC IP HLDG CO LLC

AI summary

Embodiments of the present disclosure provide a method, device, and computer program product for managing cache. There is provided a method of managing a cache, comprising: receiving a current operation request from a user, data requested by the current operation request being to be duplicated to the cache; obtaining a plurality of historical operation requests of the user, the plurality of historical operation requests being received prior to the current operation request; determining a predicted operation request for the user based on the plurality of historical operation requests and the current operation request; and in accordance with determining that a type of an operation associated with the predicted operation request belongs to predetermined types, adjusting data in the cache based on the predicted operation request. With the embodiments of the present disclosure, it can be determined dynamically and intelligently which data should be cached, the speed of processing user's operation requests can be increased, and the memory space occupied by the cache can be reduced, thereby improving the system performance.