cache-eviction-policy

In-memory LRU Cache implementation using multiple Design Patterns

Implementation of the SIEVE cache eviction algorithm in Go

Projekt (Simulace o počítačových službách) z předmětu Modelování a simulace (IMS), pátý semestr bakalářského studia BIT na FIT VUT/BUT, ak.rok 2024/2025

This repository is a CacheLib prototype of TARDIS, a learned cache eviction policy as part of the LDOS Expeditions project at UT Austin. It implements LRUForgive and S3FIFOForgive with a log-structured multithreaded architecture, and is evaluated against LRU & S3FIFO on real traces to to measure miss-ratio improvement under high eviction pressure.

Implementation of cache eviction policies including Least Recently Used (LRU), Least Frequently Used (LFU), and First-In-First-Out (FIFO) algorithms. This project demonstrates efficient cache management techniques to optimize data retrieval and memory usage.

A comprehensive collection of cache eviction policies implemented in python, providing practical examples for Least Recently Used (LRU), Least Frequently Used (LFU), and other strategies to optimize data caching in your applications.

Design Application Cache/LRU Cache

🚧 Work in Progress 🚧 A middleware library for caching data with various eviction policies, cache invalidation, and cache coherency strategies.

Deep learning-driven cache eviction policy using LSTM forecasting and uncertainty estimation, deployed as a production microservice with a full MLOps pipeline.