API Gateway Predictive Caching for Low-Latency Remote Data Access

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Solution Overview

Problem

In remote computing environments, particularly those utilizing microservices architectures, client devices often encounter network latency and instability, leading to the retrieval of incorrect, stale, or no data, impacting user experience and operational efficiency.

Innovation Solution

Predictive caching and recaching techniques are employed to analyze API call records, identify frequently used data requests, and cache corresponding responses at an API gateway, optimizing cache space for specific user needs, thereby ensuring up-to-date and accurate data delivery.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If data is retrieved from remote enterprise system over network, then users can access enterprise resources remotely, but network latency and instability cause clients to receive stale or incorrect data

Engineering Contradiction:

Improveremote access capabilityVSAvoiddata accuracy

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system performs preliminary actions by analyzing API call records to identify frequently accessed data patterns before users actually request them. This predictive analysis allows the cache to pre-position relevant data locally, so when users make requests, they receive accurate, up-to-date data immediately from the local cache rather than waiting for network responses from the remote enterprise system.

Inventive Principle:

Principle #10Preliminary action

2Adaptability or versatility

If microservices architecture is used to enhance scalability, then system maintenance and updating are improved, but additional API calls increase latency and network issues

Engineering Contradiction:

ImprovescalabilityVSAvoidlatency

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The invention extracts frequently accessed API response data from the remote microservices environment and stores it locally in a cache. This extraction eliminates the need for repeated network calls to microservices for the same data, thereby reducing latency and network dependency while preserving the scalability benefits of the microservices architecture.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system introduces an intermediary caching layer between the client devices and the remote microservices. This intermediary captures and stores API responses locally, mediating between user requests and the remote microservices system, thereby reducing direct network calls and associated latency while maintaining access to scalable microservices resources.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Quantity of substance

If cache space is allocated for all API data requests, then comprehensive data coverage is achieved, but cache space is wasted when not all users utilize all cached data

Engineering Contradiction:

Improvecache coverageVSAvoidcache space utilization

Core Design Contradiction:

Quantity of substanceVSLoss of energy

Solution Approach 1:

The system applies local quality by customizing cache content for each user based on their individual API call patterns and preferences. Instead of uniformly caching all possible API responses for all users, the system analyzes each user's specific data access behavior and caches only the relevant subset of data for that user, thereby optimizing cache space utilization while maintaining comprehensive coverage of user-specific needs.

Inventive Principle:

Principle #3Local quality

4Speed

If API responses are cached at API gateway, then data retrieval speed is improved, but cache space is limited and requires optimization

Engineering Contradiction:

Improvedata retrieval speedVSAvoidcache space

Core Design Contradiction:

SpeedVSVolume of stationary object

Solution Approach 1:

The system performs preliminary analysis of API call records to predict which data will be frequently accessed by each user. This predictive preprocessing allows the cache to prioritize and retain only the most relevant data in limited cache space, ensuring that high-value data is readily available for fast retrieval while avoiding waste of cache space on rarely accessed data.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically changes cache parameters based on user behavior patterns, adjusting which data is cached, for how long, and with what priority. By analyzing API call frequencies and user preferences, the system optimizes cache retention policies to maximize the utility of limited cache space while maintaining fast retrieval speeds for predicted user needs.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12580996B2Systems, methods, and media for predicting data for precaching and/or recaching at a computer cache of a computer environment

Publication Date: 2026.03.17 FMR CORP

AI summary

Techniques are provided for predicting data for precaching and/or recaching at a computer cache of a computer environment. Based on a detected login event of a user, a precache API request can be generated for a type of API data request most often utilized by the user over a predetermined time period. An API gateway may use the precache API request to obtain a corresponding precache API response from a microservice. The precache API response may be stored together at an API gateway cache. Subsequently, when information from an API call substantially matches a cache key corresponding to the precache API response, the precache API response can be provided to the user instead of obtaining the response from the microservice. Additionally, the precache API request may include a recache value that can be used to iteratively obtain up-to-date precache API responses from the microservice.