Rack-Scaling: An efficient rack-based redistribution method to accelerate the scaling of cloud disk arrays

Lin, Zhehan; Guo, Hanchen; Wu, Chongqing; Li, Jie; Xue, Guangtao; Guo, Minyi

doi:10.1109/ipdps49936.2021.00098

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…Tak et al propose a caching scheme that utilizes the local storage of physical node as a block-level cache for the remote storage blocks in a cloud, so that both the network traffic and I/O cost are reduced [32]. Lin et al propose a data redistribution method to minimize the cross-site data migration in a cloud, so as to accelerate rack level scaling process in cloud storage systems [33]. However, none of these studies are proposed for the record/replay usage.…”

Section: Vm Disk Formatsmentioning

confidence: 99%

RR-Row: Redirect-on-Write Based Virtual Machine Disk for Record/Replay

ZHAO,

XIAN,

et al. 2024

IEICE Trans. Inf. & Syst.

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Record/replay is one essential tool in clouds to provide many capabilities such as fault tolerance, software debugging, and security analysis by recording the execution into a log and replaying it deterministically later on. However, in virtualized environments, the log file increases heavily due to saving a considerable amount of I/O data, finally introducing significant storage costs. To mitigate this problem, this paper proposes RR-Row, a redirect-on-write based virtual machine disk for record/replay scenarios. RR-Row appends the written data into new blocks rather than overwrites the original blocks during normal execution so that all written data are reserved in the disk. In this way, the record system only saves the block id instead of the full content, and the replay system can directly fetch the data from the disk rather than the log, thereby reducing the log size a lot. In addition, we propose several optimizations for improving I/O performance so that it is also suitable for normal execution. We implement RR-Row for QEMU and conduct a set of experiments. The results show that RR-Row reduces the log size by 68% compared to the currently used Raw/QCow2 disk without compromising I/O performance.

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Section: Vm Disk Formatsmentioning

confidence: 99%