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Storage systems and their OS components are designed to accommodate a wide variety of applications and dynamic workloads. Storage components inside the OS contain various heuristic algorithms to provide high performance and adaptability for different workloads. These heuristics may be tunable via parameters, and some system calls allow users to optimize their system performance. These parameters are often predetermined based on experiments with limited applications and hardware. Thus, storage systems often run with these predetermined and possibly suboptimal values. Tuning these parameters manually is impractical: one needs an adaptive, intelligent system to handle dynamic and complex workloads. Machine learning (ML) techniques are capable of recognizing patterns, abstracting them, and making predictions on new data. ML can be a key component to optimize and adapt storage systems. In this position paper, we propose KML, an ML framework for storage systems. We implemented a prototype and demonstrated its capabilities on the well-known problem of tuning optimal readahead values. Our results show that KML has a small memory footprint, introduces negligible overhead, and yet enhances throughput by as much as 2.3×. CCS CONCEPTS• Software and its engineering → Operating systems; File systems management; • Computing methodologies → Machine learning.

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Our project aims to create a movie recommendation and community platform where users can discover and share their favorite movies with others. The platform will utilize a recommendation system to suggest personalized movie recommendations based on the user's preferences and viewing history. Users will also be able to rate and review movies, create watchlists, and follow other users with similar movie tastes. The community aspect of the platform will allow users to engage with others through forums, discussions, and private messaging. This will create a space for movie enthusiasts to connect and share their thoughts on the latest releases, hidden gems, and all-time favorites. To develop the recommendation system, we will use collaborative filtering and content-based filtering techniques. The platform will also utilize machine learning algorithms to analyze user behavior and provide more accurate recommendations over time. Overall, our movie recommendation and community platform will provide a comprehensive and interactive movie-watching experience for users while promoting a sense of community among movie enthusiasts.

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