Wayfinder: Automated Operating System Specialization
Alexander Jung, Cezar Cr\u{a}ciunoiu, Nikolaos Karaolidis, Hugo Lefeuvre, Daniel O\~noro Rubio, Felipe Huici, Charalampos Rotsos, Pierre Olivier

TL;DR
Wayfinder automates OS configuration specialization to optimize application performance, resource use, or security without expert input, using neural networks to efficiently explore large configuration spaces.
Contribution
It introduces a fully automated framework that specializes OS configurations for specific workloads and metrics, leveraging neural networks and transfer learning.
Findings
Achieves up to 24% performance improvement.
Reduces memory usage by up to 8.5%.
Faster convergence than competing methods.
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…
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Taxonomy
TopicsSoftware System Performance and Reliability · Security and Verification in Computing · Parallel Computing and Optimization Techniques
