58 research outputs found

    ๋‚ธ๋“œ ํ”Œ๋ž˜์‹œ ์ €์žฅ์žฅ์น˜์˜ ์„ฑ๋Šฅ ๋ฐ ์ˆ˜๋ช… ํ–ฅ์ƒ์„ ์œ„ํ•œ ํ”„๋กœ๊ทธ๋žจ ์ปจํ…์ŠคํŠธ ๊ธฐ๋ฐ˜ ์ตœ์ ํ™” ๊ธฐ๋ฒ•

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2019. 2. ๊น€์ง€ํ™.์ปดํ“จํŒ… ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ ํ–ฅ์ƒ์„ ์œ„ํ•ด, ๊ธฐ์กด์˜ ๋Š๋ฆฐ ํ•˜๋“œ๋””์Šคํฌ(HDD)๋ฅผ ๋น ๋ฅธ ๋‚ธ๋“œ ํ”Œ๋ž˜์‹œ ๋ฉ”๋ชจ๋ฆฌ ๊ธฐ๋ฐ˜ ์ €์žฅ์žฅ์น˜(SSD)๋กœ ๋Œ€์ฒดํ•˜๊ณ ์ž ํ•˜๋Š” ์—ฐ๊ตฌ๊ฐ€ ์ตœ๊ทผ ํ™œ๋ฐœํžˆ ์ง„ํ–‰ ๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ง€์†์ ์ธ ๋ฐ˜๋„์ฒด ๊ณต์ • ์Šค์ผ€์ผ๋ง ๋ฐ ๋ฉ€ํ‹ฐ ๋ ˆ๋ฒจ๋ง ๊ธฐ์ˆ ๋กœ SSD ๊ฐ€๊ฒฉ์„ ๋™๊ธ‰ HDD ์ˆ˜์ค€์œผ๋กœ ๋‚ฎ์•„์กŒ์ง€๋งŒ, ์ตœ๊ทผ์˜ ์ฒจ๋‹จ ๋””๋ฐ”์ด์Šค ๊ธฐ์ˆ ์˜ ๋ถ€์ž‘์šฉ์œผ ๋กœ NAND ํ”Œ๋ž˜์‹œ ๋ฉ”๋ชจ๋ฆฌ์˜ ์ˆ˜๋ช…์ด ์งง์•„์ง€๋Š” ๊ฒƒ์€ ๊ณ ์„ฑ๋Šฅ ์ปดํ“จํŒ… ์‹œ์Šคํ…œ์—์„œ์˜ SSD์˜ ๊ด‘๋ฒ”์œ„ํ•œ ์ฑ„ํƒ์„ ๋ง‰๋Š” ์ฃผ์š” ์žฅ๋ฒฝ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ตœ๊ทผ์˜ ๊ณ ๋ฐ€๋„ ๋‚ธ๋“œ ํ”Œ๋ž˜์‹œ ๋ฉ”๋ชจ๋ฆฌ์˜ ์ˆ˜๋ช… ๋ฐ ์„ฑ๋Šฅ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ์‹œ์Šคํ…œ ๋ ˆ๋ฒจ์˜ ๊ฐœ์„  ๊ธฐ์ˆ ์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ ๋œ ๊ธฐ๋ฒ•์€ ์‘์šฉ ํ”„๋กœ ๊ทธ๋žจ์˜ ์“ฐ๊ธฐ ๋ฌธ๋งฅ์„ ํ™œ์šฉํ•˜์—ฌ ๊ธฐ์กด์—๋Š” ์–ป์„ ์ˆ˜ ์—†์—ˆ๋˜ ๋ฐ์ดํ„ฐ ์ˆ˜๋ช… ํŒจํ„ด ๋ฐ ์ค‘๋ณต ๋ฐ์ดํ„ฐ ํŒจํ„ด์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ด์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ, ๋‹จ์ผ ๊ณ„์ธต์˜ ๋‹จ์ˆœํ•œ ์ •๋ณด๋งŒ์„ ํ™œ์šฉํ–ˆ ๋˜ ๊ธฐ์กด ๊ธฐ๋ฒ•์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•จ์œผ๋กœ์จ ํšจ๊ณผ์ ์œผ๋กœ NAND ํ”Œ๋ž˜์‹œ ๋ฉ”๋ชจ๋ฆฌ์˜ ์„ฑ๋Šฅ ๋ฐ ์ˆ˜๋ช…์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ์ตœ์ ํ™” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•œ๋‹ค. ๋จผ์ €, ์‘์šฉ ํ”„๋กœ๊ทธ๋žจ์˜ I/O ์ž‘์—…์—๋Š” ๋ฌธ๋งฅ์— ๋”ฐ๋ผ ๊ณ ์œ ํ•œ ๋ฐ์ดํ„ฐ ์ˆ˜๋ช…๊ณผ ์ค‘ ๋ณต ๋ฐ์ดํ„ฐ์˜ ํŒจํ„ด์ด ์กด์žฌํ•œ๋‹ค๋Š” ์ ์„ ๋ถ„์„์„ ํ†ตํ•ด ํ™•์ธํ•˜์˜€๋‹ค. ๋ฌธ๋งฅ ์ •๋ณด๋ฅผ ํšจ๊ณผ ์ ์œผ๋กœ ํ™œ์šฉํ•˜๊ธฐ ์œ„ํ•ด ํ”„๋กœ๊ทธ๋žจ ์ปจํ…์ŠคํŠธ (์“ฐ๊ธฐ ๋ฌธ๋งฅ) ์ถ”์ถœ ๋ฐฉ๋ฒ•์„ ๊ตฌํ˜„ ํ•˜์˜€๋‹ค. ํ”„๋กœ๊ทธ๋žจ ์ปจํ…์ŠคํŠธ ์ •๋ณด๋ฅผ ํ†ตํ•ด ๊ฐ€๋น„์ง€ ์ปฌ๋ ‰์…˜ ๋ถ€ํ•˜์™€ ์ œํ•œ๋œ ์ˆ˜๋ช…์˜ NAND ํ”Œ ๋ž˜์‹œ ๋ฉ”๋ชจ๋ฆฌ ๊ฐœ์„ ์„ ์œ„ํ•œ ๊ธฐ์กด ๊ธฐ์ˆ ์˜ ํ•œ๊ณ„๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋‹ค. ๋‘˜์งธ, ๋ฉ€ํ‹ฐ ์ŠคํŠธ๋ฆผ SSD์—์„œ WAF๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•ด ๋ฐ์ดํ„ฐ ์ˆ˜๋ช… ์˜ˆ์ธก์˜ ์ •ํ™• ์„ฑ์„ ๋†’์ด๋Š” ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์˜ I/O ์ปจํ…์ŠคํŠธ๋ฅผ ํ™œ์šฉ ํ•˜๋Š” ์‹œ์Šคํ…œ ์ˆ˜์ค€์˜ ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•์˜ ํ•ต์‹ฌ ๋™๊ธฐ๋Š” ๋ฐ์ดํ„ฐ ์ˆ˜๋ช…์ด LBA๋ณด๋‹ค ๋†’์€ ์ถ”์ƒํ™” ์ˆ˜์ค€์—์„œ ํ‰๊ฐ€ ๋˜์–ด์•ผ ํ•œ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๋”ฐ๋ผ์„œ ํ”„ ๋กœ๊ทธ๋žจ ์ปจํ…์ŠคํŠธ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋ฐ์ดํ„ฐ์˜ ์ˆ˜๋ช…์„ ๋ณด๋‹ค ์ •ํ™•ํžˆ ์˜ˆ์ธกํ•จ์œผ๋กœ์จ, ๊ธฐ์กด ๊ธฐ๋ฒ•์—์„œ LBA๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋ฐ์ดํ„ฐ ์ˆ˜๋ช…์„ ๊ด€๋ฆฌํ•˜๋Š” ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•œ๋‹ค. ๊ฒฐ๋ก ์ ์œผ ๋กœ ๋”ฐ๋ผ์„œ ๊ฐ€๋น„์ง€ ์ปฌ๋ ‰์…˜์˜ ํšจ์œจ์„ ๋†’์ด๊ธฐ ์œ„ํ•ด ์ˆ˜๋ช…์ด ์งง์€ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜๋ช…์ด ๊ธด ๋ฐ์ดํ„ฐ์™€ ํšจ๊ณผ์ ์œผ๋กœ ๋ถ„๋ฆฌ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์“ฐ๊ธฐ ํ”„๋กœ๊ทธ๋žจ ์ปจํ…์ŠคํŠธ์˜ ์ค‘๋ณต ๋ฐ์ดํ„ฐ ํŒจํ„ด ๋ถ„์„์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๋ถˆํ•„์š”ํ•œ ์ค‘๋ณต ์ œ๊ฑฐ ์ž‘์—…์„ ํ”ผํ•  ์ˆ˜์žˆ๋Š” ์„ ํƒ์  ์ค‘๋ณต ์ œ๊ฑฐ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ค‘๋ณต ๋ฐ ์ดํ„ฐ๋ฅผ ์ƒ์„ฑํ•˜์ง€ ์•Š๋Š” ํ”„๋กœ๊ทธ๋žจ ์ปจํ…์ŠคํŠธ๊ฐ€ ์กด์žฌํ•จ์„ ๋ถ„์„์ ์œผ๋กœ ๋ณด์ด๊ณ  ์ด๋“ค์„ ์ œ์™ธํ•จ์œผ๋กœ์จ, ์ค‘๋ณต์ œ๊ฑฐ ๋™์ž‘์˜ ํšจ์œจ์„ฑ์„ ๋†’์ผ ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์ค‘๋ณต ๋ฐ์ดํ„ฐ๊ฐ€ ๋ฐœ์ƒ ํ•˜๋Š” ํŒจํ„ด์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ๊ธฐ๋ก๋œ ๋ฐ์ดํ„ฐ๋ฅผ ๊ด€๋ฆฌํ•˜๋Š” ์ž๋ฃŒ๊ตฌ์กฐ ์œ ์ง€ ์ •์ฑ…์„ ์ƒˆ๋กญ๊ฒŒ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ, ์„œ๋ธŒ ํŽ˜์ด์ง€ ์ฒญํฌ๋ฅผ ๋„์ž…ํ•˜์—ฌ ์ค‘๋ณต ๋ฐ์ดํ„ฐ๋ฅผ ์ œ๊ฑฐ ํ•  ๊ฐ€๋Šฅ์„ฑ์„ ๋†’์ด๋Š” ์„ธ๋ถ„ํ™” ๋œ ์ค‘๋ณต ์ œ๊ฑฐ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ ๋œ ๊ธฐ์ˆ ์˜ ํšจ๊ณผ๋ฅผ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ์‹ค์ œ ์‹œ์Šคํ…œ์—์„œ ์ˆ˜์ง‘ ๋œ I/O ํŠธ๋ ˆ์ด์Šค์— ๊ธฐ๋ฐ˜ํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ํ‰๊ฐ€ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์—๋ฎฌ๋ ˆ์ดํ„ฐ ๊ตฌํ˜„์„ ํ†ตํ•ด ์‹ค์ œ ์‘์šฉ์„ ๋™์ž‘ํ•˜๋ฉด์„œ ์ผ๋ จ์˜ ํ‰๊ฐ€๋ฅผ ์ˆ˜ํ–‰ํ–ˆ๋‹ค. ๋” ๋‚˜์•„๊ฐ€ ๋ฉ€ํ‹ฐ ์ŠคํŠธ๋ฆผ ๋””๋ฐ”์ด์Šค์˜ ๋‚ด๋ถ€ ํŽŒ์›จ์–ด๋ฅผ ์ˆ˜์ •ํ•˜์—ฌ ์‹ค์ œ์™€ ๊ฐ€์žฅ ๋น„์Šทํ•˜๊ฒŒ ์„ค์ •๋œ ํ™˜๊ฒฝ์—์„œ ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•˜ ์˜€๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์ œ์•ˆ๋œ ์‹œ์Šคํ…œ ์ˆ˜์ค€ ์ตœ์ ํ™” ๊ธฐ๋ฒ•์ด ์„ฑ๋Šฅ ๋ฐ ์ˆ˜๋ช… ๊ฐœ์„  ์ธก๋ฉด์—์„œ ๊ธฐ์กด ์ตœ์ ํ™” ๊ธฐ๋ฒ•๋ณด๋‹ค ๋” ํšจ๊ณผ์ ์ด์—ˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ํ–ฅํ›„ ์ œ์•ˆ๋œ ๊ธฐ ๋ฒ•๋“ค์ด ๋ณด๋‹ค ๋” ๋ฐœ์ „๋œ๋‹ค๋ฉด, ๋‚ธ๋“œ ํ”Œ๋ž˜์‹œ ๋ฉ”๋ชจ๋ฆฌ๊ฐ€ ์ดˆ๊ณ ์† ์ปดํ“จํŒ… ์‹œ์Šคํ…œ์˜ ์ฃผ ์ €์žฅ์žฅ์น˜๋กœ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๋Š” ๋ฐ์— ๊ธ์ •์ ์ธ ๊ธฐ์—ฌ๋ฅผ ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.Replacing HDDs with NAND flash-based storage devices (SSDs) has been one of the major challenges in modern computing systems especially in regards to better performance and higher mobility. Although the continuous semiconductor process scaling and multi-leveling techniques lower the price of SSDs to the comparable level of HDDs, the decreasing lifetime of NAND flash memory, as a side effect of recent advanced device technologies, is emerging as one of the major barriers to the wide adoption of SSDs in highperformance computing systems. In this dissertation, system-level lifetime improvement techniques for recent high-density NAND flash memory are proposed. Unlike existing techniques, the proposed techniques resolve the problems of decreasing performance and lifetime of NAND flash memory by exploiting the I/O context of an application to analyze data lifetime patterns or duplicate data contents patterns. We first present that I/O activities of an application have distinct data lifetime and duplicate data patterns. In order to effectively utilize the context information, we implemented the program context extraction method. With the program context, we can overcome the limitations of existing techniques for improving the garbage collection overhead and limited lifetime of NAND flash memory. Second, we propose a system-level approach to reduce WAF that exploits the I/O context of an application to increase the data lifetime prediction for the multi-streamed SSDs. The key motivation behind the proposed technique was that data lifetimes should be estimated at a higher abstraction level than LBAs, so we employ a write program context as a stream management unit. Thus, it can effectively separate data with short lifetimes from data with long lifetimes to improve the efficiency of garbage collection. Lastly, we propose a selective deduplication that can avoid unnecessary deduplication work based on the duplicate data pattern analysis of write program context. With the help of selective deduplication, we also propose fine-grained deduplication which improves the likelihood of eliminating redundant data by introducing sub-page chunk. It also resolves technical difficulties caused by its finer granularity, i.e., increased memory requirement and read response time. In order to evaluate the effectiveness of the proposed techniques, we performed a series of evaluations using both a trace-driven simulator and emulator with I/O traces which were collected from various real-world systems. To understand the feasibility of the proposed techniques, we also implemented them in Linux kernel on top of our in-house flash storage prototype and then evaluated their effects on the lifetime while running real-world applications. Our experimental results show that system-level optimization techniques are more effective over existing optimization techniques.I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Garbage Collection Problem . . . . . . . . . . . . . 2 1.1.2 Limited Endurance Problem . . . . . . . . . . . . . 4 1.2 Dissertation Goals . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4 Dissertation Structure . . . . . . . . . . . . . . . . . . . . . 7 II. Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1 NAND Flash Memory System Software . . . . . . . . . . . 9 2.2 NAND Flash-Based Storage Devices . . . . . . . . . . . . . 10 2.3 Multi-stream Interface . . . . . . . . . . . . . . . . . . . . 11 2.4 Inline Data Deduplication Technique . . . . . . . . . . . . . 12 2.5 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.5.1 Data Separation Techniques for Multi-streamed SSDs 13 2.5.2 Write Traffic Reduction Techniques . . . . . . . . . 15 2.5.3 Program Context based Optimization Techniques for Operating Systems . . . . . . . . 18 III. Program Context-based Analysis . . . . . . . . . . . . . . . . 21 3.1 Definition and Extraction of Program Context . . . . . . . . 21 3.2 Data Lifetime Patterns of I/O Activities . . . . . . . . . . . 24 3.3 Duplicate Data Patterns of I/O Activities . . . . . . . . . . . 26 IV. Fully Automatic Stream Management For Multi-Streamed SSDs Using Program Contexts . . 29 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.2.1 No Automatic Stream Management for General I/O Workloads . . . . . . . . . 33 4.2.2 Limited Number of Supported Streams . . . . . . . 36 4.3 Automatic I/O Activity Management . . . . . . . . . . . . . 38 4.3.1 PC as a Unit of Lifetime Classification for General I/O Workloads . . . . . . . . . . . 39 4.4 Support for Large Number of Streams . . . . . . . . . . . . 41 4.4.1 PCs with Large Lifetime Variances . . . . . . . . . 42 4.4.2 Implementation of Internal Streams . . . . . . . . . 44 4.5 Design and Implementation of PCStream . . . . . . . . . . 46 4.5.1 PC Lifetime Management . . . . . . . . . . . . . . 46 4.5.2 Mapping PCs to SSD streams . . . . . . . . . . . . 49 4.5.3 Internal Stream Management . . . . . . . . . . . . . 50 4.5.4 PC Extraction for Indirect Writes . . . . . . . . . . 51 4.6 Experimental Results . . . . . . . . . . . . . . . . . . . . . 53 4.6.1 Experimental Settings . . . . . . . . . . . . . . . . 53 4.6.2 Performance Evaluation . . . . . . . . . . . . . . . 55 4.6.3 WAF Comparison . . . . . . . . . . . . . . . . . . . 56 4.6.4 Per-stream Lifetime Distribution Analysis . . . . . . 57 4.6.5 Impact of Internal Streams . . . . . . . . . . . . . . 58 4.6.6 Impact of the PC Attribute Table . . . . . . . . . . . 60 V. Deduplication Technique using Program Contexts . . . . . . 62 5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.2 Selective Deduplication using Program Contexts . . . . . . . 63 5.2.1 PCDedup: Improving SSD Deduplication Efficiency using Selective Hash Cache Management . . . . . . 63 5.2.2 2-level LRU Eviction Policy . . . . . . . . . . . . . 68 5.3 Exploiting Small Chunk Size . . . . . . . . . . . . . . . . . 70 5.3.1 Fine-Grained Deduplication . . . . . . . . . . . . . 70 5.3.2 Read Overhead Management . . . . . . . . . . . . . 76 5.3.3 Memory Overhead Management . . . . . . . . . . . 80 5.3.4 Experimental Results . . . . . . . . . . . . . . . . . 82 VI. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 6.1 Summary and Conclusions . . . . . . . . . . . . . . . . . . 88 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . 89 6.2.1 Supporting applications that have unusal program contexts . . . . . . . . . . . . . 89 6.2.2 Optimizing read request based on the I/O context . . 90 6.2.3 Exploiting context information to improve fingerprint lookups . . . . .. . . . . . 91 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92Docto

    ํ˜•ํƒœ์˜ ๋ถˆํ™•์ ์„ฑ์„ ๋ฐฐ๊ฒฝ์œผ๋กœ ํ•œ ์ž‘์—…์—ฐ๊ตฌ

    No full text
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์กฐ์†Œ๊ณผ ์กฐ์†Œ์ „๊ณต,1996.Maste

    Effects of excercise and atmospheric oxygen concentration in normobaric environment on the free oxygen radical gener

    No full text
    ์˜ํ•™๊ณผ/๋ฐ•์‚ฌ[ํ•œ๊ธ€]์‚ฐ์†Œ๋Š” ์‚ด์•„๊ฐ€๋Š” ๋ฐ ๊ผญ ํ•„์š”ํ•˜์ง€๋งŒ ์‚ฐ์†Œ๋ฅผ ์ด์šฉํ•œ ๋Œ€์‚ฌ ํ™œ๋™์˜ ๊ฒฐ๊ณผ๋กœ ์ธํ•ด ์œ ํ•ด ์‚ฐ์†Œ์ธ ํ™œ์„ฑ์‚ฐ์†Œ๋„ ๋งŒ๋“ค์–ด์ง€๋ฉฐ, ์ด๋กœ ์ธํ•œ ์‚ฐํ™”์  ์กฐ์ง ์†์ƒ์œผ๋กœ๋ถ€ํ„ฐ ๋™๋ฌผ์„ธํฌ๋Š” ์Šค์Šค๋กœ๋ฅผ ๋ณดํ˜ธํ•˜๊ธฐ ์œ„ํ•ด ํ™œ์„ฑ์‚ฐ์†Œ ๋ฐ ๋ฐ˜์‘์„ฑ ์‚ฐ์†Œํ™”ํ•ฉ๋ฌผ์˜ ์ƒ์„ฑ์„ ๋ฐฉ์ง€, ์ œ๊ฑฐ, ๋ถˆํ™œ์„ฑํ™” ์‹œ์ผœ ์กฐ์ง ์†์ƒ์„ ์ตœ์†Œํ•œ์œผ๋กœ ์–ต์ œํ•˜๋Š” ํ•ญ์‚ฐํ™” ๋Šฅ๋ ฅ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์šด๋™์€ ์ผ์‹œ์ ์œผ๋กœ ์กฐ์ง์— ์ €์‚ฐ์†Œ ์ƒํƒœ๋ฅผ ์œ ๋ฐœ์‹œํ‚ค๊ณ  ํ™œ์„ฑ์‚ฐ์†Œ ์ƒ์„ฑ๋Ÿ‰์„ ์ฆ๊ฐ€ ์‹œํ‚ค์ง€๋งŒ ์‹ ์ฒด๋Š” ์ด์— ๋Œ€ํ•ด ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ํ•ญ์ƒ์„ฑ ์œ ์ง€ ๋ฐ˜์‘์„ ๋ณด์ธ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ง€์†์  ์šด๋™ ๋˜๋Š” ์žฅ๊ธฐ๊ฐ„์— ๊ฑธ์นœ ๊ฐ„ํ—์  ์ €์‚ฐ์†Œ ์ƒํƒœ์—์˜ ๋…ธ์ถœ์ด ๋งˆ์šฐ์Šค์˜ ํ™œ์„ฑ์‚ฐ์†Œ ์ƒ์„ฑ๊ณผ ํ•ญ์‚ฐํ™”๋Šฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๊ณผ ์—๋„ˆ์ง€๋Œ€์‚ฌ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด, Balb/C mouse๋ฅผ ๊ต๋ฐฐํ•˜์—ฌ ์–ป์€ ์‹ ์ƒ ์ƒ์ฅ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์ƒํ›„ 12์‹œ๊ฐ„ ์ด๋‚ด์— ์‚ฐ์†Œ๋†๋„ 13%์˜ ์ €์‚ฐ์†Œ ํ™˜๊ฒฝ์— ๋‘ ์‹œ๊ฐ„๋งˆ๋‹ค 30๋ถ„ ๋™์•ˆ ๊ฐ„ํ—์ , ๋งŒ์„ฑ์ ์œผ๋กœ ๋…ธ์ถœํ•œ ๊ตฐ๊ณผ ๋Œ€์กฐ๊ตฐ์—์„œ ์ถœ์ƒ 10์ฃผ ํ›„๋ถ€ํ„ฐ ์ •์ƒ ์‚ฐ์†Œ์ƒํƒœ์—์„œ 1์ฃผ์ผ์— 3์ผ๊ฐ„ 20 m/min์˜ ์†๋„๋กœ 40๋ถ„๊ฐ„ ํŠธ๋ ˆ๋“œ๋ฐ€ ์šด๋™์„ 10์ฃผ๊ฐ„ ์‹œ์ผฐ๋‹ค. 13 %์˜ ์‚ฐ์†Œ ์กฐ๊ฑด (ํ•ด๋ฐœ 3,800 m ์ƒ๋‹น ๊ณ ๋„)์—์„œ ์ƒํ™œํ•˜๋ฉฐ 21 %์˜ ์‚ฐ์†Œ ์กฐ๊ฑด (ํ•ด์ˆ˜๋ฉด ์ƒ๋‹น ๊ณ ๋„)์—์„œ ์šด๋™ํ•˜๋Š” ์ €์‚ฐ์†Œ ์šด๋™๊ตฐ (hypoxia exercise, HE), 21 % ์‚ฐ์†Œ ์กฐ๊ฑด์—์„œ ์ƒํ™œ๊ณผ ์šด๋™์„ ์ˆ˜ํ–‰ํ•˜๋Š” ์ •์ƒ์‚ฐ์†Œ ์šด๋™๊ตฐ (normoxia exercise, NE), 13 % ์‚ฐ์†Œ ์กฐ๊ฑด์—์„œ ์ƒํ™œํ•˜๋ฉฐ ์šด๋™์€ ์ˆ˜ํ–‰ํ•˜์ง€ ์•Š๋Š” ์ €์‚ฐ์†Œ ๋น„์šด๋™๊ตฐ (hypoxia control, HC) ๋ฐ 21 % ์‚ฐ์†Œ ์กฐ๊ฑด์—์„œ ์ƒํ™œํ•˜๋ฉฐ ์šด๋™์€ ์ˆ˜ํ–‰ํ•˜์ง€ ์•Š๋Š” ์ •์ƒ์‚ฐ์†Œ ๋น„์šด๋™๊ตฐ (normoxia control, NC)์œผ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ, ๊ฐ๊ฐ ๊ทธ๋ฃน๋ณ„ 20๋งˆ๋ฆฌ์”ฉ ํ˜ˆ์•ก๋‚ด ํ™œ์„ฑ์‚ฐ์†Œ๋Ÿ‰๊ณผ ํ•ญ์‚ฐํ™”๋Šฅ์„ ์กฐ์‚ฌํ•˜์˜€๊ณ , ์—๋„ˆ์ง€๋Œ€์‚ฌ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ๊ทผ์œก๋‚ด ๋‹น๋Œ€์‚ฌ ๊ด€๋ จ ์œ ์ „์ž์™€ ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ์ƒ์„ฑ ๋ฐ ํ™œ์„ฑ์— ๊ด€๋ จ๋œ ์œ ์ „์ž๋“ค์˜ ๋ฐœํ˜„ ์ •๋„๋ฅผ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ์‚ฐ์†Œ ๋†๋„์™€ ์šด๋™ ์—ฌ๋ถ€์— ๋”ฐ๋ฅธ ๋ฐœํ˜„ ์ •๋„๋ฅผ ๋น„๊ตํ•˜์—ฌ ์ €์‚ฐ์†Œ ํ™˜๊ฒฝ๊ณผ ์šด๋™์— ๋Œ€ํ•œ ์ƒ์ฒด์˜ ์ ์‘๋ฐ˜์‘์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ํ™œ์„ฑ์‚ฐ์†Œ์ƒ์„ฑ์ด ์šด๋™ ํ›„ ์ฆ๊ฐ€๋˜์ง€ ์•Š์•˜๊ณ , ํ•ญ์‚ฐํ™” ํšจ์†Œ์— ์˜ํ–ฅ์„ ์ฃผ๋Š” CuZnSOD์™€ MNSOD mRNA ๋ฐœํ˜„์€ ์ •์ƒ์‚ฐ์†Œ ์šด๋™๊ตฐ๊ณผ ์ €์‚ฐ์†Œ๊ตฐ์—์„œ ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์˜€์œผ๋ฉฐ ํ˜ˆ์ฒญ๋‚ด ํ•ญ์‚ฐํ™”๋Šฅ์€ ๋‘ ์šด๋™๊ตฐ์—์„œ ์œ ์˜์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ๊ณจ๊ฒฉ๊ทผ์—์„œ ํ•ด๋‹น๊ณผ์ •๊ณผ ๊ด€๋ จ๋œ GLUT4๋Š” ์ •์ƒ์‚ฐ์†Œ ์šด๋™๊ตฐ๊ณผ ์ €์‚ฐ์†Œ ๋น„์šด๋™๊ตฐ์—์„œ ์œ ์˜์ ์œผ๋กœ ๋ฐœํ˜„์ด ์ฆ๊ฐ€๋˜์—ˆ๊ณ  PFKm์€ ์ •์ƒ์‚ฐ์†Œ ์šด๋™๊ตฐ๊ณผ ์ €์‚ฐ์†Œ๊ตฐ๋“ค์—์„œ ์ฆ๊ฐ€๋˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์˜€์œผ๋ฉฐ, ์ –์‚ฐ ๋Œ€์‚ฌ์™€ ๊ด€๋ จ๋œ CA3๋Š” ์šด๋™๊ณผ ์ €์‚ฐ์†Œ ํ™˜๊ฒฝ์—์˜ ๋…ธ์ถœ์— ์˜ํ•ด ๋ชจ๋‘ ๋ฐœํ˜„์ด ์œ ์˜์ ์œผ๋กœ ์ฆ๊ฐ€๋˜์—ˆ๊ณ  MCT-1 ์—ญ์‹œ ์ €์‚ฐ์†Œ ๋น„์šด๋™๊ตฐ์—์„œ mRNA์˜ ๋ฐœํ˜„์ด ํ†ต๊ณ„ํ•™์ ์œผ๋กœ ์œ ์˜ํ•˜๊ฒŒ ์ฆ๊ฐ€๋˜์—ˆ๋‹ค. PGA1ฮฑ์™€ TFAM์˜ mRNA ๋ฐœํ˜„์ด ๋ชจ๋‘ ์ €์‚ฐ์†Œ ๋น„์šด๋™๊ตฐ์—์„œ ์œ ์˜์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜์˜€๊ณ , ์‹ค์งˆ์ ์œผ๋กœ ๊ทผ์œก๋‚ด ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„์˜ ์ˆ˜๋Š” ์ •์ƒ์‚ฐ์†Œ ์šด๋™๊ณผ ์ €์‚ฐ์†Œ ๋น„์šด๋™๊ตฐ์—์„œ ์œ ์˜์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ํ™œ์„ฑ์„ ๋ณด์—ฌ์ฃผ๋Š” COX4์™€ CS mRNA ๋ฐœํ˜„์ด ์ •์ƒ์‚ฐ์†Œ ์šด๋™๊ตฐ๊ณผ ์ €์‚ฐ์†Œ๊ตฐ๋“ค์—์„œ ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์˜€๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ๋ฐ˜๋ณต๋œ ์šด๋™๊ณผ ๊ฐ„ํ—์  ์ €์‚ฐ์†Œ ์ƒํƒœ์— ์žฅ๊ธฐ๊ฐ„ ๋…ธ์ถœ์‹œ์— ํ•ญ์‚ฐํ™”๋Šฅ์ด ์ฆ๊ฐ€๋˜๊ณ , ํ•ด๋‹น๊ณผ์ • ๋˜ํ•œ ์ด‰์ง„ ๋˜๋ฉฐ ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„์˜ ์ˆ˜์ ์ธ ์ฆ๊ฐ€์™€ ํ™œ์„ฑ์˜ ์ฆ๊ฐ€๋กœ ์ €์‚ฐ์†Œ์— ์˜ํ•œ ์กฐ์ง์˜ ์†์ƒ์„ ๊ฐ์†Œ์‹œํ‚ฌ ์ˆ˜ ์žˆ์œผ๋ฆฌ๋ผ๊ณ  ์ƒ๊ฐ๋œ๋‹ค. [์˜๋ฌธ]ope

    ํ”Œ๋ž˜์‹œ ๋ฉ”๋ชจ๋ฆฌ ๊ธฐ๋ฐ˜ SSD์˜ ์ˆ˜๋ช… ํ–ฅ์ƒ์„ ์œ„ํ•œ ๋ฐ์ดํ„ฐ Deduplication ๊ธฐ๋ฒ•์˜ ์„ค๊ณ„ ๋ฐ ๊ตฌํ˜„

    No full text
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2012. 2. ๊น€์ง€ํ™.SSD๋Š” ๊ธฐ๊ณ„์ ์ธ ์š”์†Œ๊ฐ€ ์กด์žฌํ•˜์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์— ํ•˜๋“œ๋””์Šคํฌ์— ๋น„ํ•ด ์ž„์˜ ์ฝ๊ธฐ/์“ฐ๊ธฐ์— ๋Œ€ํ•œ ์„ฑ๋Šฅ์ด ์šฐ์ˆ˜ํ•˜๊ณ  ์ „๋ ฅ ์†Œ๋ชจ๋Ÿ‰์ด ๋‚ฎ์œผ๋ฉฐ ์ถฉ๊ฒฉ์— ๊ฐ•ํ•˜๋‹ค๋Š” ์žฅ์ ์ด ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ํ”Œ๋ž˜์‹œ ๋ฉ”๋ชจ๋ฆฌ์˜ ๋ธ”๋ก ๋‹น ์‚ญ์ œ ํšŸ์ˆ˜๊ฐ€ ์ œํ•œ๋œ๋‹ค๋Š” ํŠน์„ฑ์œผ๋กœ ์ธํ•ด SSD์˜ ์ˆ˜๋ช…์ด ๊ฒฐ์ •๋˜์–ด ๋ฒ„๋ฆฌ๋Š” ๋ฌธ์ œ๊ฐ€ ๋Œ€๋‘๋˜๊ณ  ์ด๋Ÿฌํ•œ ํ˜„์ƒ์€ ๊ธฐ์ˆ ์ด ๋ฐœ์ „ํ•˜๋Š” ์ถ”์„ธ์— ๋”ฐ๋ผ ์ ์ฐจ ์‹ฌํ™”๋˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ SSD์˜ ์ˆ˜๋ช… ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃจ๊ธฐ ์œ„ํ•œ ๊ธฐ๋ฒ•์ค‘ ํ•˜๋‚˜์ธ de-duplication ์ฆ‰, ์ค‘๋ณต๋˜๋Š” ๋ฐ์ดํ„ฐ๋ฅผ ์ฐพ์•„ ์ œ๊ฑฐํ•˜๋Š” ๊ธฐ๋ฒ•์ด ์—ฐ๊ตฌ ๋˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ธฐ์กด ๊ธฐ๋ฒ•์—์„œ๋Š” ์†Œํ”„ํŠธ์›จ์–ด์ ์ธ ํ•ด์‹œ ๊ฐ’์˜ ๊ณ„์‚ฐ์ด ํฐ ๋ถ€ํ•˜๋ฅผ ๋ฐœ์ƒ์‹œ์ผœ, ์ผ๋ถ€ ๋ฐ์ดํ„ฐ๋งŒ์„ ์ฒ˜๋ฆฌํ•˜๋Š” sampling ๊ธฐ๋ฒ• ๋“ฑ์˜ ์‚ฌ์šฉ์ด ๋ถˆ๊ฐ€ํ”ผ ํ•˜๋‹ค. ์ด๋Ÿฌํ•œ ๊ธฐ๋ฒ•๋“ค์€ ํ•ด์‹œ ๊ฐ’ ๊ณ„์‚ฐ์˜ ๋ถ€ํ•˜๋ฅผ ๊ฐ์†Œ์‹œํ‚ค์ง€๋งŒ SSD์˜ ์ˆ˜๋ช… ํ–ฅ์ƒ์˜ ํšจ๊ณผ ์—ญ์‹œ ๊ฒฝ๊ฐ์‹œํ‚จ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ค‘๋ณต ํ™•์ธ์˜ ๋‹จ์œ„๋ฅผ ๋ฏธ์„ธํ™” ํ•จ์œผ๋กœ์จ ๊ธฐ์กด ๊ธฐ๋ฒ• ๋Œ€๋น„ ์ค‘๋ณต๋˜๋Š” ๋ฐ์ดํ„ฐ์˜ ์ œ๊ฑฐ๋Ÿ‰์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ด์™€ ๋™์‹œ์— ํ•ด์‹œ ๊ฐ’ ๊ณ„์‚ฐ์˜ ๋น„์šฉ์„ ํ•˜๋“œ์›จ์–ด ๊ฐ€์†๊ธฐ๋ฅผ ๋„์ž…ํ•จ์œผ๋กœ์จ ํšจ๊ณผ์ ์œผ๋กœ ์ค„์ด๋Š” De-duplication ๊ธฐ๋ฒ•์„ ์„ค๊ณ„ํ•˜์˜€๊ณ , ์ด๋ฅผ SSD ํ”„๋กœํ† ํƒ€์ž…์— ๊ตฌํ˜„ํ•จ์œผ๋กœ์จ ๊ธฐ๋ฒ•์˜ ํšจ๊ณผ๋ฅผ ๊ฒ€์ฆํ•œ ๊ฒฐ๊ณผ ๊ธฐ์กด ๊ธฐ๋ฒ• ๋Œ€๋น„ ์•ฝ 41%์˜ ์ˆ˜๋ช… ํ–ฅ์ƒ ํšจ๊ณผ๋ฅผ ๋ณด์˜€๊ณ  ์ด์— ๋”ฐ๋ผ ์ฆ๊ฐ€ํ•œ ๋ถ€ํ•˜ ์—ญ์‹œ ์ตœ๋Œ€ 65% ๊ฐ์†Œ์‹œ์ผฐ๋‹ค.Recently, SSDs have gained wide popularity in comparison with hard disk drives due to its distinctive own merits such as high random access performance and low power consumptions. However, flash memories which have a limited number of erase operation per block, limit the lifespan of SSDs. In order to deal with this lifespan problem, a technique called de-duplication have been introduced which removes duplicated data from workload. The existing de-duplication scheme computes a hash value of incoming data with the software which incurs a huge time overhead. The scheme needs to exploit pre-hashing or sampling techniques to avoid the hash computation. The sampling technique, however, can miss potentially duplicated data, and thus the amount of the increased lifespan of the SSD cannot be maximized. In this paper, we propose a fine-grained chunking module for fingerprinting so that the amount of duplicated data can be larger than the existing scheme. At the same time, we introduce a hardware accelerator for hashing which can reduce the time overhead effectively. Eventually, we design a new de-duplication technique by combining these two modules and implement this technique on the SSD prototype to evaluate its benefit. The proposed technique reduces the amount of written data to the SSD by 41% compared to the existing scheme whose chunk size is a flash page. The write buffer and the read cache also reduce additional page reads incurred by this technique by 27% and 65%, respectively.1. ์„œ๋ก  1_x000D_ 1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1_x000D_ 1.2 ์—ฐ๊ตฌ ๋™๊ธฐ์™€ ๊ธฐ์—ฌ 4_x000D_ 1.3 ๋…ผ๋ฌธ ๊ตฌ์„ฑ 8_x000D_ 2. ๊ด€๋ จ ์—ฐ๊ตฌ 9_x000D_ 2.1 Deduplication ๊ธฐ๋ฒ• 9_x000D_ 3. SSD ํ”„๋กœํ† ํƒ€์ž…์˜ ๊ตฌ์กฐ 11_x000D_ 3.1 SSD ํ”„๋กœํ† ํƒ€์ž…์˜ ์ „์ฒด ๊ตฌ์กฐ 11_x000D_ 3.2 SSD ํ”„๋กœํ† ํƒ€์ž…์˜ ์†Œํ”„ํŠธ์›จ์–ด ๊ตฌ์กฐ 12_x000D_ 3.3 SSD ํ”„๋กœํ† ํƒ€์ž…์˜ ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ 14_x000D_ 4. ๋ฏธ์„ธ Chunk ๋‹จ์œ„ De-duplication 16_x000D_ 4.1 ๋ฏธ์„ธ Chunk ๋‹จ์œ„ ์ค‘๋ณต ์ œ๊ฑฐ ๊ธฐ๋ฒ• 16_x000D_ 4.2 ์“ฐ๊ธฐ ๋ฒ„ํผ ๊ด€๋ฆฌ ๊ธฐ๋ฒ• 20_x000D_ 4.3 ์ฝ๊ธฐ ์บ์‹œ์˜ ๋„์ž… 23_x000D_ 5. ํ•˜๋“œ์›จ์–ด ๊ฐ€์†๊ธฐ์˜ ๋„์ž… 25_x000D_ 5.1 ํ•ด์‹œ ํ•จ์ˆ˜์˜ ๊ฒฐ์ • 25_x000D_ 5.2 ํ•˜๋“œ์›จ์–ด ํ•ด์‹œ ํ•จ์ˆ˜ 27_x000D_ 5.3 ํ•˜๋“œ์›จ์–ด ๊ฐ€์†๊ธฐ์˜ ์„ฑ๋Šฅ 29_x000D_ 5.4 ํ•˜๋“œ์›จ์–ด ๊ฐ€์†๊ธฐ์˜ ๋„์ž… 31_x000D_ 6. ์‹คํ—˜ ๊ฒฐ๊ณผ 32_x000D_ 6.1 ์‹คํ—˜ ํ™˜๊ฒฝ 32_x000D_ 6.2 ์‹คํ—˜ ๊ฒฐ๊ณผ 34_x000D_ 7. ๊ฒฐ๋ก  38_x000D_ 7.1 ๊ฒฐ๋ก  38_x000D_ 7.2 ํ–ฅํ›„ ์—ฐ๊ตฌ 39_x000D_ _x000D_Maste

    ๊ธฐ๊ณ„ ์‹œ์Šคํ…œ ๊ฑด์ „์„ฑ ํ‰๊ฐ€๋ฅผ ์œ„ํ•œ ์œ ํšจ๋…๋ฆฝ์„ฑ ๊ธฐ๋ฐ˜ ์„ผ์„œ ๋„คํŠธ์›Œํฌ ๋””์ž์ธ ์—ฐ๊ตฌ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2017. 2. ์œค๋ณ‘๋™.The failure of an engineered system not only results in an enormous property loss, but also causes a substantial societal loss. The discipline of prognostics and health management (PHM) recently has received great attention as a solution to prevent unexpected failures of engineered systems. The goal of PHM is to detect anomaly states, to predict potential failures of a system, and to plan an optimal management schedule. PHM is composed of five essential functions: 1) sensing, 2) reasoning, 3) diagnostics, 4) prognostics, and 5) management. The sensing function, in which sensory data is acquired from the system of interest, is a core element needed for cost-effective execution of PHM. The success of the remaining functions in PHM highly depends on the quality of the data obtained by the sensing function. The research described herein describes the investigation of two original ideas of optimal sensor placement (OSP) for the PHM sensing function. These ideas are aimed to enable cost-effective and robust sensor data acquisition from the system. The first idea is a stochastic effective independence (EFI) method, referred to as an energy-based stochastic EFI methodthe proposed method overcomes the drawbacks of existing OSP methods in the sensing function. In Research Thrust 1, the stochastic sensor network design is proposed. It takes the uncertainty of the system into consideration to give more accurate representation of the system than the deterministic sensor network design in the mean sense. Also, the explicit form of the proposed method has the benefit of lower computational requirements, as compared to the sampling-based stochastic approach. In Research Thrust 2, a robust sensor network design that considers the latent failure of the sensor is introduced. The proposed robust sensor network is designed to tolerate the partial failure of the sensorthus, it contributes to the safety of the sensor network. The proposed method is validated to have accuracy that is comparable to the optimal sensor network design in normal conditions, and higher accuracy for situations in which there is a partial failure of the given sensor network.Chapter 1. Introduction 1 1.1 Background and Motivation 1 1.2 Research Objectives and Scopes 3 1.3 Dissertation Overview 5 Chapter 2. Literature Review 7 2.1 Linear Independence of a System 7 2.2 Model-based Sensor Placement Method: Effective Independence Method 10 2.3 Energy-Based Sensor Placement Method 14 2.4 Data-Based Sensor Placement Method: EigenMap Method 15 Chapter 3. Stochastic Sensor Network Design 18 3.1 Stochastic Finite Element Method 18 3.1.1 Principle of Stochastic Perturbation 18 3.1.2 Stochastic Eigenvalue Problem 19 3.2 Stochastic Effective Independence Method 21 3.3 Energy-Based Stochastic EFI Method 30 3.4 Case Study 31 3.4.1 Truss Bridge Structure 32 3.4.2 Sensor Placement Under Uncertainty 34 3.4.2.1 Monte Carlo Simulation 34 3.4.2.2 SEFI Method 38 3.5 Conclusion 53 Chapter 4. Robust Sensor Network Design 55 4.1 Battery System 55 4.1.1 Battery Pack Overview 55 4.1.2 Heat Generation Model 58 4.1.3 Model Calibration and Validation 62 4.2 Robust Sensor Network Design 65 4.3 Case Study 72 4.3.1 Case 1: Different Heat Generation for the Cells 72 4.3.2 Case 2: Forced Convection 76 4.4 Conclusion 83 Chapter 5. Contributions and Future Work 86 5.1 Contributions and Impacts 86 5.2 Suggestions for Future Research 88 References 90 Abstract (Korean) 94Docto

    ๋Œ€๋™(ๅคงๅŒ)๊ณผ ๊ฒฝ๊ณ„(ๅขƒ็•Œ) : ์บ‰์œ ์›จ์ด(ๅบทๆœ‰็ˆฒ)์˜ ๋ณดํŽธ์  ๊ณต๋™์ฒด ๊ตฌ์ƒ

    No full text
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์™ธ๊ตํ•™๊ณผ,2008.Maste

    ้ †ๆฌก็š„ ๆ„ๆ€ๆฑบๅฎšๆณ•์„ ๏ง็”จํ•œ ไปฃๆ›ฟ์—๋„ˆ์ง€ ๆŠ•่ณ‡ๆฑบๅฎš์— ้—œํ•œ ็ก็ฉถ : ๆฟŸๅทž้“ ๅœฐ็†ฑ้–‹็™ผ๊ณผ ้—œ่ฏํ•˜์—ฌ

    No full text
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธๅคงๅญธๆ ก ๅคงๅญธ้™ข :่ณ‡ๆบๅทฅๅญธ็ง‘,1996.Maste

    ๊ทผ๋Œ€์ฒœํ™ฉ์ œ๋ฅผ ๋‘˜๋Ÿฌ์‹ผ ์ •์น˜์™€ ์ข…๊ต : ๊ฐ€์ผ€์ด ๊ฐ€์“ฐํžˆ์ฝ” ์‹ ๋„๋ก ์˜ ์ง€์„ฑ์‚ฌ์  ์˜๋ฏธ

    No full text
    ๋ณธ๊ณ ๋Š” ์ผ๋ณธ์˜ ์‹ ๋„๋ก ์„ ์ด๋ก ํ™”ํ–ˆ๋‹ค๊ณ  ํ‰๊ฐ€๋˜๋Š” ๊ฐ€์ผ€์ด ๊ฐ€์“ฐํžˆ์ฝ” ์‹ ๋„๋ก ์˜ ์ง€์„ฑ์‚ฌ์  ์˜๋ฏธ๋ฅผ ์‚ดํŽด๋ณธ๋‹ค. ๊ทธ์—๊ฒŒ ์‹ ์ด๋ž€ ์ ˆ๋Œ€์  ์ดˆ์›”์ž๊ฐ€ ์•„๋‹ˆ๋ผ ๋ชจ๋“  ์ธ๊ฐ„์ด ๋„๋‹ฌ ๊ฐ€๋Šฅํ•œ ๊ฒƒ์ด์—ˆ๋‹ค. ์ด๋•Œ ์‹ ์— ๋„๋‹ฌํ•  ์ˆ˜ ์žˆ๋Š” ์ธ๊ฐ„์€ ๊ตญ๊ฐ€์— ์ ํ•ฉํ•œ ์ธ๋ฌผ์ด ๋˜๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๋‚˜๋ฅผ ์žŠ๊ณ  ์Šค์Šค๋กœ์˜ ์ผ์— ๋ชฐ์ž…ํ•จ์œผ๋กœ์จ ์‹ =์ธ๊ฒฉ์ž๊ฐ€ ๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฐ€๋ฅด์นจ์ด ๊ทธ๊ฐ€ ์‹ ๋„๋ผ๋Š” ์ข…๊ต์—์„œ ์ฐพ์€ ๊ฐ€์น˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ฐ€์ผ€์ด์˜ ์‹ ๋„๋ก ์„ ๊ตญ๊ฐ€์œค๋ฆฌ์˜ ๊ฐ•์กฐ๋กœ๋งŒ ๋ณด๋Š” ๊ฒƒ์€ ์ผ๋ฉด์ ์ผ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ์˜ ๋…ผ๋ฆฌ๋Š” ์„œ์–‘์˜ ๊ธฐ๋…๊ต์™€ ๊ทผ๋ณธ์ ์œผ๋กœ ๋Œ€๋ฆฝํ•˜๋Š” ๊ฒƒ์ฒ˜๋Ÿผ ๋ณด์ด์ง€๋งŒ, ๋‹น์‹œ์˜ ๋…์ผ๋‚ญ๋งŒ์ฃผ์˜์ž๋“ค์—๊ฒŒ ๋ณด์ด๋Š” ์ƒ๊ธฐ๋ก ์  ๋ฒ”์‹ ๋ก ์˜ ์ดํ•ด ์†์—์„œ ๊ธฐ๋…๊ต๋ฅผ ํ•ด์„ํ•˜๋ ค๋Š” ์‹œ๋„์™€ ๊ทธ๋ฆฌ ๋ฉ€์ง€ ์•Š๋‹ค. ๊ฐ€์ผ€์ด์˜ ์‚ฌ์ƒ์€ ์Šˆ๋งˆ์ด์–ด๋งˆํ—ˆ๋‚˜ ๋”œํƒ€์ด์˜ ์ƒ๋ช…์˜ ํ‘œํ˜„์ด๋‚˜ ๋ฒ”์‹ ๋ก ์  ์ƒ๊ธฐ๋ก ์—์„œ ๋ณด์•˜๋˜ ๊ฐ€์น˜๋ฅผ ์‹ ๋„์  ์‚ฌ์œ ๋กœ ํ’€์–ด๋‚ธ ๊ฒƒ์ด์—ˆ๋‹ค. ๋ฌผ๋ก  ๊ฐ€์ผ€์ด์˜ ์‹ ๋„์‹์˜ ์‚ฌ์œ ๊ฐ€ ๊ตญ๊ฐ€์ฃผ์˜319 ๊ตญ๋ฌธ์ดˆ๋ก์™€ ๊ฒฐํ•ฉํ•˜๊ฒŒ ๋œ ๋ฐฐ๊ฒฝ์—๋Š” ์ด๋Ÿฌํ•œ ์ƒ๋ช…์ฃผ์˜์™€ ์‹ ์— ๋Œ€ํ•œ ์ดํ•ด์™€์˜ ๊ฒฐํ•ฉ์ด ์žˆ์—ˆ์Œ์„ ๋ถ€์ •ํ•  ์ˆ˜ ์—†๋‹ค. ํ•˜์ง€๋งŒ ๊ฐ€์ผ€์ด๋Š” ์Šคํ”ผ๋…ธ์ž-์Š๋ผ์ด์–ด๋งˆํ—ˆ-๋”œํƒ€์ด์˜ ๋…ผ์˜ ์†์—์„œ ์ ˆ๋Œ€์ž๋ฅผ ๋Œ€์ƒ๋ช…์œผ๋กœ์„œ ์น˜ํ™˜ํ•˜์—ฌ ๋งŒ๋ฌผ์„ ์ด ๋Œ€์ƒ๋ช…์˜ ํ‘œํ˜„์œผ๋กœ์„œ ๋ฐ”๋ผ๋ด„์œผ๋กœ์จ ์ฒœํ™ฉ์„ ๋‘˜๋Ÿฌ์‹ผ ๊ตญ์ฒด๋…ผ์Ÿ์—์„œ ์ƒˆ๋กœ์šด ์ดํ•ด๋ฅผ ์‹œ๋„ํ•œ ๊ฒƒ์œผ๋กœํŒŒ์•…๋  ์ˆ˜ ์žˆ๋‹ค. ์‹ ๋„, ์ƒ๋ช…, ํ‘œํ˜„์ด๋ผ๋Š” ๊ทธ์˜ ํ•ต์‹ฌ๊ฐœ๋…์ด ๊ฐ–๋Š” ์˜๋ฏธ์™€ ๋ฐฐ๊ฒฝ์„ ์‚ดํŽด๋ด„์œผ๋กœ์จ, ๋ณธ๊ณ ๋Š” ๊ทผ๋Œ€ ์ผ๋ณธ์—์„œ์˜ ์ฒœํ™ฉ์ œ๋ฅผ ๋‘˜๋Ÿฌ์‹ผ ์ •์น˜์™€ ์ข…๊ต์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด๋‹ค ๋ฉด๋ฐ€ํžˆ ๋ฐํžˆ๊ณ ์ž ํ–ˆ๋‹ค.์ด ์—ฐ๊ตฌ๋Š” 2020๋…„๋„ ์„œ์šธ๋Œ€ํ•™๊ต ์ผ๋ณธ์—ฐ๊ตฌ์†Œ ์ผ๋ณธํ•™์—ฐ๊ตฌ์ง€์›์‚ฌ์—…์˜ ์ง€์›์„ ๋ฐ›์•„ ์ˆ˜ํ–‰๋˜์—ˆ์Œ

    ์ •์  ์ž‘๋™ํ™˜๊ฒฝ์— ๋”ฐ๋ฅธ ํ•ญ๊ณต๊ธฐ์šฉ ์—ด๊ตํ™˜๊ธฐ์˜ ๊ตฌ์กฐ์„ค๊ณ„์— ๊ด€ํ•œ ์—ฐ๊ตฌ

    No full text
    The objective of this study is to predict structural characteristics of a heat exchanger mounted on aircraft engine using a finite element analysis. The plastic fracture and life on the heat exchanger are estimated by thermo-mechanical analysis. The mechanical properties of Inconel 625 refer to SPECIAL METALS which is Inconel manufacturer. Also, the mechanical properties of Inconel tube refer to tensile test result at PNU-RRUTC(Pusan National University- Rolls Royce University Technology Center). The yield strength shows 308 MPa and tensile strength shows 640 MPa at 1000 K. To assess the structural characteristics of heat exchanger, the full and part models for the aircraft engine are employed under static operating conditions given by thermo-mechanical and inertia load. The components of aircraft engine should have high stiffness body with light weight and small volume. Therefore, parametric study is performed to determine the basic dimensions of major parts. Heat exchanger is mounted on the gas turbine engine of aircraft with mounting component which has structural safety. The mounting component design is performed to determine the major dimensions. And it is employed to heat exchanger for structural analysis. Also, heat exchanger is required installation study according to the arrangement of mounting component considering high temperature and locations. When the full model of heat exchanger is performed to the structural analysis under thermo-mechanical and inertia load, the stress result of whole load show a strong dependence to the thermal load which causes large thermal deformation. To perform transient analysis under thermo-mechanical load, heat exchanger is employed ligament efficiency refer to ASME code. When checking stress results of the equivalent model, thermal load has large difference from base model more than 70%. So, it need to consider additional conditions for thermal load in the future work. As a result of static load on the major parts, such as, the case, tubesheet, flange and mounting component have reasonable safety margin to allowable stress assumed the fatigue strength of Inconel 625 at 10000 cycle and 1000 K.Abstract โ…ณ Nomenclature โ…ต List of Tables โ…ถ List of Figures โ…ท 1. ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ ๋™ํ–ฅ 3 1.3 ์—ฐ๊ตฌ ๋‚ด์šฉ ๋ฐ ๋ชฉ์  5 2. ํƒ„์†Œ์„ฑ ์œ ํ•œ์š”์†Œ ํ•ด์„์˜ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 6 2.1 ์œ ํ•œ์š”์†Œ๋ฒ• 6 2.2 ํƒ„์†Œ์„ฑ ์ด๋ก  7 3. ์ •์  ์šด์ „ ํ•˜์ค‘์„ ๊ณ ๋ คํ•œ ์—ด๊ตํ™˜๊ธฐ์˜ ๊ตฌ์กฐ ์„ค๊ณ„ 14 3.1 ์—ด๊ตํ™˜๊ธฐ ๋ถ€ํ’ˆ ์„ค๊ณ„ 14 3.1.1 ํŠœ๋ธŒ์‹œํŠธ ๋งค๊ฐœ๋ณ€์ˆ˜ ํ•ด์„ 19 3.1.2 ๋งค๋‹ˆํด๋“œ ๋งค๊ฐœ๋ณ€์ˆ˜ ํ•ด์„ 23 3.2 ์žฅ์ฐฉ์กฐ๊ฑด์„ ๊ณ ๋ คํ•œ ๋งˆ์šดํŒ… ์„ค๊ณ„ 26 3.2.1 ๋งˆ์šดํŒ… ํ˜•์ƒ ๋””์ž์ธ 26 3.2.2 ๋งˆ์šดํŒ… ์œ„์น˜ ์„ ์ • 27 3.3 ์—ด๊ตํ™˜๊ธฐ ํ•ด์„ ๋ชจ๋ธ 32 3.4 ํ•ญ๊ณต๊ธฐ ์ •์  ์šด์ „ ํ•˜์ค‘์กฐ๊ฑด์— ๋”ฐ๋ฅธ ์„ค๊ณ„ 35 3.4.1 ์—ด-๊ธฐ๊ณ„ ํ•˜์ค‘์— ๋Œ€ํ•œ ์„ค๊ณ„ 35 3.4.2 ๊ด€์„ฑ ํ•˜์ค‘์— ๋Œ€ํ•œ ์„ค๊ณ„ 41 3.4.3 ๋ฆฌ๊ฐ€๋จผํŠธ ํšจ์œจ์„ ์ด์šฉํ•œ ๋“ฑ๊ฐ€๋ชจ๋ธ ์„ ์ • 45 3.4.4 ์—ด๊ตํ™˜๊ธฐ ๋ชจ๋ธ์˜ ๊ฑด์ „์„ฑ ํ‰๊ฐ€ 49 3.4.5 ์—ด-๊ธฐ๊ณ„ ๋ฐ ๊ด€์„ฑ ํ•˜์ค‘ ๋ชจ๋ธ์˜ ๊ฒฐ๊ณผ๋น„๊ต 50 4. ๊ฒฐ๋ก  52 ์ฐธ๊ณ ๋ฌธํ—Œ 5

    ์ธ์ฒด(ไบบ้ซ”)์˜ ์šฐ์ธก ์†์˜ ์†ํ†ฑ๊ณผ ์ง€๋ฌธ์—์„œ ๋ฐฉ์ถœ๋˜๋Š” ์ƒ์ฒด๊ด‘(็”Ÿ้ซ”ๅ…‰, Biophoton)์— ๊ด€ํ•œ ์—ฐ๊ตฌ

    No full text
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณผํ•™๊ต์œก๊ณผ ๋ฌผ๋ฆฌ์ „๊ณต,2003.Maste
    • โ€ฆ
    corecore