IoLens: Visual Analytics System for Exploring Storage I/O Tracking Process

Changmin Jeon, Jiwon Ha, Hyolim Hong, Hyeon Jeon, Hyeonsang Eom, Heonyoung Yeom, Jinwook Seo

Abstract: As we enter the era of big data, a substantial amount of Input/Output (I/O) requests to storage devices are generated, making the maintenance of I/O performance important. Furthermore, I/O performance directly affects the overall user experience in edge devices. However, with the increasing complexity of systems, numerous factors influencing I/O performance have emerged, making it challenging to analyze and explore the overall I/O processing workflow. To address this issue, we introduce IoLens, a visual analytics tool that helps users explore system I/O performance from kernel I/O stack up to virtual file system and storage device drivers. Our tool helps users analyze I/O performance by identifying the overall workload of I/O requests and intuitively identifying anomalies. The effectiveness and applicability of IoLens have been validated through a usage scenario following a system engineer working on system kernels. A user study with four domain experts is conducted to further validate the usability of the tool.