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Fast Application Launch on Personal Computing/Communication Devices
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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Ryu, Junhee | - |
| dc.contributor.author | Lee, Dongeun | - |
| dc.contributor.author | Shin, Kang G. | - |
| dc.contributor.author | Kang, Kyungtae | - |
| dc.date.accessioned | 2024-01-12T06:31:11Z | - |
| dc.date.available | 2024-01-12T06:31:11Z | - |
| dc.date.issued | 2023-02 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/117699 | - |
| dc.description.abstract | We present Paralfetch, a novel prefetcher to speed up app launches on personal computing/communication devices by: 1) accurate collection of launch-related disk read requests, 2) pre-scheduling of these requests to improve I/O throughput during prefetching, and 3) overlapping app execution with disk prefetching for hiding disk access time from the app execution. We have implemented Paralfetch under Linux kernels on a desktop/laptop PC, a Raspberry Pi 3 board, and an Android smartphone. Tests with popular apps show that Paralfetch significantly reduces app launch times on flash-based drives, and outperforms GSoC Prefetch and FAST, which are representative app prefetchers available for Linux-based systems. © 2023 by The USENIX Association. All rights reserved. | - |
| dc.format.extent | 15 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | USENIX | - |
| dc.title | Fast Application Launch on Personal Computing/Communication Devices | - |
| dc.type | Article | - |
| dc.publisher.location | 미국 | - |
| dc.identifier.scopusid | 2-s2.0-85159287311 | - |
| dc.identifier.bibliographicCitation | USENIX Conference on File and Storage Technologies, pp 425 - 439 | - |
| dc.citation.title | USENIX Conference on File and Storage Technologies | - |
| dc.citation.startPage | 425 | - |
| dc.citation.endPage | 439 | - |
| dc.type.docType | Proceeding | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.identifier.url | https://www.usenix.org/conference/fast23/presentation/ryu | - |
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Related Researcher
- Kang, Kyung tae
- ERICA 소프트웨어융합대학 (DEPARTMENT OF ARTIFICIAL INTELLIGENCE)