# PerOS: Personalized Self-Adapting Operating Systems in the Cloud

arXiv: https://arxiv.org/abs/2404.00057 · HTML: https://mlsanity.com/paper/2404.00057/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 101 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Builds on (most cited first, 15 of 15)

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- [FLINT: A Platform for Federated Learning Integration](https://mlsanity.com/paper/2302.12862/) (2023)
- [Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Caching](https://mlsanity.com/paper/2305.03152/) (2023)
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- [Benchmarking, Analysis, and Optimization of Serverless Function Snapshots](https://mlsanity.com/paper/2101.09355/) (2021)
- [CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification](https://mlsanity.com/paper/2211.00640/) (2022)
- [Learning-based Application-Agnostic 3D NoC Design for Heterogeneous Manycore Systems](https://mlsanity.com/paper/1810.08869/) (2018)
- [Tab2Know: Building a Knowledge Base from Tables in Scientific Papers](https://mlsanity.com/paper/2107.13306/) (2021)
- [Valorising the IoT Databox: Creating Value for Everyone](https://mlsanity.com/paper/1609.03312/) (2016)

## Abstract (arXiv)

Operating systems (OSes) are foundational to computer systems, managing hardware resources and ensuring secure environments for diverse applications. However, despite their enduring importance, the fundamental design objectives of OSes have seen minimal evolution over decades. Traditionally prioritizing aspects like speed, memory efficiency, security, and scalability, these objectives often overlook the crucial aspect of intelligence as well as personalized user experience. The lack of intelligence becomes increasingly critical amid technological revolutions, such as the remarkable advancements in machine learning (ML). Today's personal devices, evolving into intimate companions for users, pose unique challenges for traditional OSes like Linux and iOS, especially with the emergence of specialized hardware featuring heterogeneous components. Furthermore, the rise of large language models (LLMs) in ML has introduced transformative capabilities, reshaping user interactions and software development paradigms. While existing literature predominantly focuses on leveraging ML methods for system optimization or accelerating ML workloads, there is a significant gap in addressing personalized user experiences at the OS level. To tackle this challenge, this work proposes PerOS, a personalized OS ingrained with LLM capabilities. PerOS aims to provide tailored user experiences while safeguarding privacy and personal data through declarative interfaces, self-adaptive kernels, and secure data management in a scalable cloud-centric architecture; therein lies the main research question of this work: How can we develop intelligent, secure, and scalable OSes that deliver personalized experiences to thousands of users?