Score-Based Cache Admission and Eviction Policy
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Solution Overview
Problem
Existing computing systems lack a comprehensive cache replacement and admission policy, leading to suboptimal cache performance in terms of item access speed and efficiency.
Innovation Solution
A method that calculates a score for each item based on its characteristics, such as source and content, using machine learning or neural networks, to determine suitability for cache admission and eviction, allowing for selective admission and eviction decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional cache replacement policies (LRU, LFU, FIFO) are used, then cache eviction can be performed, but cache admission policy is lacking and overall cache performance is not optimized
Solution Approach 1:
The patent changes the parameter basis for cache decisions from simple access patterns (LRU/LFU) to a comprehensive score based on multiple characteristics including item content, source, and predicted access probability. This score-based parameter enables both admission and eviction decisions to be optimized together, improving cache productivity without excessive complexity increase.
Solution Approach 2:
The system implements feedback by analyzing item characteristics and access patterns to calculate scores, then using these scores to make admission and eviction decisions. The score calculation incorporates feedback from item source analysis and content evaluation, creating a closed-loop system that continuously optimizes cache performance.
2Reliability
If cache size is increased to improve hit rates, then more items can be stored, but memory usage increases and cost rises
Solution Approach 1:
The patent applies preliminary action by calculating scores for items before they are admitted to cache, evaluating their characteristics and predicted value in advance. This pre-evaluation ensures that only high-value items occupy cache space, maximizing hit rates with minimal memory usage and avoiding the need to increase cache size.
3Measurement precision
If comprehensive item analysis is performed to calculate scores, then cache admission accuracy improves, but computational overhead increases
Solution Approach 1:
The system applies partial action by selectively analyzing item characteristics based on their relevance to cache suitability. Not all items require full comprehensive analysis - the score calculation focuses on the most predictive characteristics (source, content type, access patterns), providing high accuracy while reducing unnecessary computational overhead.
Data Source
AI summary
The subject matter described herein analyzes an item that is a candidate for admission into or eviction from a cache to determine characteristics of the item. The characteristics include at least one of item content and item source. A score associated with the item is calculated based on the determined characteristics. The item is admitted into, or evicted from, the cache based on the calculated score.