Framework for Analyzing Android I/O Stack Behavior: From Generating the Workload to Analyzing the Trace

Jeong, Sooman; Lee, Kisung; Hwang, Jungwoo; Lee, Seongjin; Won, Youjip

doi:10.3390/fi5040591

Cited by 11 publications

(1 citation statement)

References 8 publications

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“…The IO traces [30] were extracted while the user approached facebook and twitter. We replayed the trace through mobibench [31]. Figure 4 (c) shows the normalized throughput to COW.…”

Section: Macrobenchmarkmentioning

confidence: 99%

Copy-on-Write with Adaptive Differential Logging for Persistent Memory

Hwang

Won

2019

IEICE Trans. Inf. & Syst.

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File systems based on persistent memory deploy Copyon-Write (COW) or logging to guarantee data consistency. However, COW has a write amplification problem and logging has a double write problem. Both COW and logging increase write traffic on persistent memory. In this work, we present adaptive differential logging and zero-copy logging for persistent memory. Adaptive differential logging applies COW or logging selectively to each block. If the updated size of a block is smaller than or equal to half of the block size, we apply logging to the block. If the updated size of a block is larger than half of the block size, we apply COW to the block. Zero-copy logging treats an user buffer on persistent memory as a redo log. Zero-copy logging does not incur any additional data copy. We implement adaptive differential logging and zero-copy logging on both NOVA and PMFS file systems. Our measurement on real workloads shows that adaptive differential logging and zero-copy logging get 150.6% and 149.2% performance improvement over COW, respectively.

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“…The IO traces [30] were extracted while the user approached facebook and twitter. We replayed the trace through mobibench [31]. Figure 4 (c) shows the normalized throughput to COW.…”

Section: Macrobenchmarkmentioning

confidence: 99%