AI-Driven Application Caching Optimization via AST Analysis

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Solution Overview

Problem

Optimizing application caching and locking in dynamic environments is challenging due to varying platform conditions and limitations, requiring manual tuning that does not translate across different vendors' cloud services, and existing methods struggle to determine optimal caching algorithms and locking strategies automatically.

Innovation Solution

A generative artificial intelligence algorithm that analyzes the operating environment and original code base to automatically determine caching and locking methods, selecting the appropriate caching algorithm and optimizing caching solutions by considering signatures and limitations, using class-based and method-based abstract syntax trees (ASTs) and logical neural networks for generating optimized code snippets.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If manual tuning of caching algorithms is performed, then optimization for specific platform conditions is achieved, but scalability across different vendors' cloud services is lost

Engineering Contradiction:

Improvecaching optimization precisionVSAvoidcross-platform adaptability

Core Design Contradiction:

Manufacturing precisionVSAdaptability or versatility

Solution Approach 1:

The system performs self-optimization by automatically analyzing platform conditions, code signatures, and performance metrics to select and tune caching algorithms without human intervention. The AI model continuously learns from runtime data and adapts caching strategies autonomously, eliminating the need for manual tuning while maintaining platform-specific optimization.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically changes caching parameters such as TTL values, cache size limits, and algorithm selection based on real-time platform conditions. By monitoring metrics like memory pressure, CPU usage, and disk I/O, the system adjusts caching behavior adaptively, enabling it to optimize performance across different cloud platforms without manual reconfiguration.

Inventive Principle:

Principle #35Parameter changes

2Ease of manufacture

If existing methods are used to determine optimal caching algorithms, then caching implementation is achieved, but automatic determination of optimal algorithms and locking strategies fails

Engineering Contradiction:

Improvecaching implementation easeVSAvoidautomatic optimization capability

Core Design Contradiction:

Ease of manufactureVSExtent of automation

Solution Approach 1:

The system implements a feedback loop where performance metrics from cached operations are continuously monitored and fed back to the AI model. This feedback enables the system to learn from actual performance data and automatically refine its caching and locking strategies, transitioning from static implementation to dynamic, self-improving automation.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces manual mechanical tuning processes with an AI-based automated system. Instead of requiring developers to manually analyze performance and adjust parameters, the system uses machine learning models to automatically determine optimal caching algorithms and locking strategies based on code signatures and platform conditions.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If caching algorithms are manually selected, then specific performance optimization is achieved, but system scalability and reduced manual intervention are compromised

Engineering Contradiction:

Improveperformance optimization efficiencyVSAvoidmanual intervention requirement

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250004951A1Application caching optimization and synchronization

Publication Date: 2025.01.02 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

An approach is provided for optimizing application caching and locking. Features specifying an operating environment of an application are extracted. The features include actual and forecasted central processing unit usage and memory, disk, and network pressure. A pairwise set of class-based and method-based ASTs and the extracted features are input into a logical neural network. Symbolic feature vectors are generated for the features by establishing bounds and flattening the features. The symbolic feature vectors and the set of class-based and method-based ASTs are input into a stacked transformer having encoders and decoders. The encoders and decoders are trained on word or token distributions of code ASTs and operating environment bounds associated with the ASTs. Using the stacked transformer, code is generated for replacing a portion of a method represented by a method-based AST. The code adds or changes caching or locking in the application.