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arXiv · 2603.29052

SteelDB: Diagnosing Kernel-Space Bottlenecks in Cloud OLTP Databases

Mitsumasa Kondo
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Abstract

Modern cloud OLTP databases have sought performance primarily through user-space optimization - separating storage and compute layers, or distributing transactions across multiple nodes using consensus algorithms. This paper turns attention to a previously unexplored layer: kernel-space I/O behavior. From an on-premises perspective, where a single server with local storage delivers excellent performance, these elaborate designs seem puzzling. Why do cloud databases require such architectural complexity? We investigate this through a pathological analysis of databases that rely on OS-level I/O control in cloud-specific storage environments. We show that bottlenecks widely attributed to network or storage architectures in fact originate in kernel-space I/O behavior. Based on this diagnosis, we derive treatment principles and realize them as SteelDB, a zero-patch architecture that improves database performance on general-purpose cloud distributed block storage through strategic I/O optimization without requiring kernel or database patches. TPC-C evaluations demonstrate that SteelDB achieves up to 9x performance improvement at no additional cost. Against Amazon Aurora, SteelDB achieved 3.1x higher performance while reducing costs by 58%, leading to a 7.3x improvement in cost efficiency. While Aurora requires an average of 254 days for major version upgrades due to applying proprietary patches to newly released OSS databases, our zero-patch architecture reduces these software maintenance costs to near zero.

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BibTeXRIS

Mitsumasa Kondo. 2026-03-30. SteelDB: Diagnosing Kernel-Space Bottlenecks in Cloud OLTP Databases. https://arxiv.org/abs/2603.29052

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