Neural Network Cache Invalidation for Aggregated Data
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Solution Overview
Problem
Existing cache invalidation systems, such as Least Recently Used (LRU) and Time to Live (TTL), are based on static empirical formulations, which fail to efficiently invalidate or refresh caches after data aggregation operations, leading to delayed requests and resource monopolization, especially in distributed cache systems.
Innovation Solution
A method that predicts a subset of aggregated data and a time period for cache invalidation and loading using request data analysis, involving a neural network to determine an optimal time for cache refresh that minimizes request delays and resource usage, based on priority levels and data aggregation operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static empirical formulations (LRU, TTL) are used for cache invalidation, then the system is simple to implement, but the cache refresh timing is suboptimal causing request delays and resource monopolization
Solution Approach 1:
The patent transitions from static cache invalidation formulations (LRU, TTL) to a dynamic machine learning-based approach that adapts to actual system behavior patterns. The neural network model dynamically determines optimal cache invalidation timing based on learned request patterns, thereby improving cache refresh efficiency while managing complexity through automated learning.
Solution Approach 2:
The system implements feedback mechanisms by using the neural network to continuously learn from past request patterns and cache performance data. The model receives feedback about actual request timings and uses this to refine its predictions for future cache invalidation timing, creating a closed-loop system that improves efficiency over time.
2Reliability
If cache invalidation occurs frequently to ensure data freshness, then data accuracy is improved, but resource usage increases and request delays occur
Solution Approach 1:
The neural network model performs preliminary analysis of request patterns to predict the optimal timing for cache invalidation. By anticipating when requests are most likely to occur, the system can schedule cache refresh operations in advance during low-activity periods, ensuring data accuracy while minimizing impact on request response times.
Solution Approach 2:
The system dynamically adjusts the cache invalidation timing parameter based on learned patterns from the neural network. Instead of using fixed intervals, the invalidation timing is changed adaptively according to predicted request patterns, thereby maintaining data accuracy while reducing request delays.
3Reliability
If data aggregation operations are performed, then data accuracy is improved, but resource monopolization occurs and request processing is delayed
Solution Approach 1:
The system applies preliminary anti-action by using the neural network to predict and prevent resource monopolization before it occurs. The model identifies patterns that lead to resource contention during data aggregation operations and schedules these operations to avoid periods of high request activity, thereby maintaining data accuracy while minimizing impact on request processing speed.
4Reliability
If the entire cache is invalidated and reloaded, then data freshness is ensured, but the time required for cache maintenance increases
Solution Approach 1:
The patent extracts only the necessary portion of cache invalidation by using the neural network to identify and invalidate only the specific cache entries that need refreshing, rather than invalidating the entire cache. This selective approach ensures data freshness for critical entries while significantly reducing the time required for cache maintenance.
Solution Approach 2:
The system applies partial action by invalidating only a subset of cache entries based on predicted request patterns and data freshness requirements. Instead of performing excessive full-cache invalidation, the neural network identifies the minimal necessary invalidation set, reducing maintenance time while maintaining adequate data freshness.
Data Source
AI summary
A method, system, and computer program product for maintaining a cache obtain request data associated with a plurality of previously processed requests for aggregated data; predict, based on the request data, (i) a subset of the aggregated data associated with a subsequent request and (ii) a first time period associated with the subsequent request; determine, based on the first time period and a second time period associated with a performance of a data aggregation operation that generates the aggregated data, a third time period associated with instructing a memory controller managing a cache to evict cached data stored in the cache and load the subset of the aggregated data into the cache; and provide an invalidation request to the memory controller managing the cache to evict the cached data stored in the cache and load the subset of the aggregated data into the cache during the third time period.