Principled Performance Tunability in Operating System Kernels
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866912762453557248 |
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| author | Chen, Zhongjie Zhang, Wentao Tang, Yulong Shu, Ran Ren, Fengyuan Xu, Tianyin Liu, Jing |
| author_facet | Chen, Zhongjie Zhang, Wentao Tang, Yulong Shu, Ran Ren, Fengyuan Xu, Tianyin Liu, Jing |
| contents | The Linux kernel source code contains numerous constant values that critically influence system performance. Many of these constants, which we term perf-consts, are magic numbers that encode brittle assumptions about hardware and workloads. As systems and workloads evolve, such constants often become suboptimal. Unfortunately, deployed kernels lack support for safe and efficient in-situ tuning of perf-consts without a long and disruptive process of rebuilding and redeploying the kernel image.
This paper advocates principled OS performance tunability. We present KernelX, a system that provides a safe, efficient, and programmable interface for in-situ tuning of arbitrary perf-consts on a running kernel. KernelX transforms any perf-const into a tunable knob on demand using a novel mechanism called Scoped Indirect Execution (SIE). SIE precisely identifies the binary boundaries where a perf-const influences system state and redirects execution to synthesized instructions that update the state as if new values were used. KernelX goes beyond version atomicity to guarantee side-effect safety, a property not provided by existing kernel update mechanisms. KernelX also provides a programmable interface that allows policies to incorporate application hints, hardware heuristics, and fine-grained isolation, without modifying kernel source code or disrupting deployed OS kernels.
Case studies across multiple kernel subsystems demonstrate that KernelX enables significant performance improvements by making previously untunable perf-consts safely tunable at runtime, while supporting millisecond-scale policy updates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12530 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Principled Performance Tunability in Operating System Kernels Chen, Zhongjie Zhang, Wentao Tang, Yulong Shu, Ran Ren, Fengyuan Xu, Tianyin Liu, Jing Operating Systems The Linux kernel source code contains numerous constant values that critically influence system performance. Many of these constants, which we term perf-consts, are magic numbers that encode brittle assumptions about hardware and workloads. As systems and workloads evolve, such constants often become suboptimal. Unfortunately, deployed kernels lack support for safe and efficient in-situ tuning of perf-consts without a long and disruptive process of rebuilding and redeploying the kernel image. This paper advocates principled OS performance tunability. We present KernelX, a system that provides a safe, efficient, and programmable interface for in-situ tuning of arbitrary perf-consts on a running kernel. KernelX transforms any perf-const into a tunable knob on demand using a novel mechanism called Scoped Indirect Execution (SIE). SIE precisely identifies the binary boundaries where a perf-const influences system state and redirects execution to synthesized instructions that update the state as if new values were used. KernelX goes beyond version atomicity to guarantee side-effect safety, a property not provided by existing kernel update mechanisms. KernelX also provides a programmable interface that allows policies to incorporate application hints, hardware heuristics, and fine-grained isolation, without modifying kernel source code or disrupting deployed OS kernels. Case studies across multiple kernel subsystems demonstrate that KernelX enables significant performance improvements by making previously untunable perf-consts safely tunable at runtime, while supporting millisecond-scale policy updates. |
| title | Principled Performance Tunability in Operating System Kernels |
| topic | Operating Systems |
| url | https://arxiv.org/abs/2512.12530 |