Memcached vs Redis Caching Optimization Comparison using Machine Learning

In recent times, the management of caching memory systems has witnessed an unprecedented surge in errors, causing widespread challenges, particularly for developers, given the intricate configuration processes involved. Caching plays a pivotal role in enhancing system performance by facilitating frequent data access. However, several limitations persist, including the vexing issue of cache misses, leading to performance fluctuations as the system resorts to retrieving data from the original source. Moreover, infrequent cache data updates due to limited eviction policies can introduce data inaccuracies. The proliferation of Machine Learning (ML) capabilities has emerged as a pivotal solution for optimizing caching performance, promising to automate these intricate tasks and ensure applications seamlessly integrate the most suitable caching solutions. To address these pressing issues, this research paper embarks on a comparative analysis of Memcached and Redis caching systems, leveraging Machine Learning algorithms. The study aims to discern data access patterns that can optimize eviction policies for Memcached and Redis.