Wayfinder: Automated Operating System Specialization

2026-03-24

AI Summary

This work addresses the challenge of manual, expert-driven operating system tuning for application-specific performance, which is hindered by vast configuration spaces, high evaluation costs, and a prevalence of ineffective settings. The authors propose Wayfinder, a novel framework that enables fully automated, end-to-end OS configuration optimization for any quantifiable objective—such as performance, memory usage, or security. Wayfinder integrates automated benchmarking, a neural network–guided search algorithm, online learning, and cross-application transfer learning to dramatically improve tuning efficiency. Experimental results across two operating systems and four applications demonstrate that Wayfinder achieves up to a 24% performance gain and an 8.5% reduction in memory footprint, consistently outperforming baseline approaches including random search and Bayesian optimization.

Technology Category

• Search and Optimization: Algorithm Configuration
• Machine Learning: Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Specializing an OS to optimize the performance of a particular application is typically a manual process that requires great expertise. Specialization through configuration lends itself well to automation; however, it is challenging due to the sheer size of the configuration space of modern OSes, the difficulty to quantify that space, the long time it takes to evaluate a configuration, and the large number of invalid configurations. Hence, existing attempts at specializing OSes automatically are limited to switching features on and off to minimize memory consumption or attack surface, and cannot target metrics such as performance. We present Wayfinder, a framework specializing the configuration of OSes completely automatically and without expert knowledge. It can specialize all aspects of an OS configuration (compile-/boot-/run-time) towards any quantifiable performance, resource consumption, or security metric, for an application processing a given workload on a given hardware setup. Wayfinder consists of an automated OS benchmarking platform, and a neural network-based search algorithm driving the specialization process. This is achieved by learning on the fly which configuration parameters and values impact performance the most, and which ones lead to runtime failures. Optionally, a model pre-trained on one application can be reused to accelerate the specialization of related applications. We evaluate Wayfinder on two OSes, four applications, and two target metrics: Wayfinder fully automatically identifies specialized configurations with up to 24% application performance improvement and 8.5% memory usage reduction compared to default configurations. We highlight the benefits of our neural network, reaching good solutions faster than competing approaches (random and Bayesian), and successfully transferring knowledge between related applications.

Problem

• operating system specialization
• configuration space
• performance optimization
• automated tuning
• system configuration

Innovation

• OS specialization
• neural network-based search
• automated configuration
• performance optimization
• knowledge transfer