6 research outputs found

    ๊ฐ€์ƒํ™” ํ™˜๊ฒฝ์„ ์œ„ํ•œ ์›๊ฒฉ ๋ฉ”๋ชจ๋ฆฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2021.8. Bernhard Egger.ํด๋ผ์šฐ๋“œ ํ™˜๊ฒฝ์€ ๊ฑฐ๋Œ€ํ•œ ์—ฐ์‚ฐ ์ž์›์„ ์ƒ์‹œ ๊ฐ€๋™ํ•  ํ•„์š” ์—†๊ณ  ์›ํ•˜๋Š” ์ˆœ๊ฐ„ ์›ํ•˜๋Š” ์–‘์˜ ๋Œ€ํ•œ ์—ฐ์‚ฐ ๋น„์šฉ๋งŒ์„ ์ง€๋ถˆํ•˜๋ฉด ๋˜๊ธฐ ๋•Œ๋ฌธ์—, ์ตœ๊ทผ ์ธ๊ณต์ง€๋Šฅ ๋ฐ ๋น…๋ฐ์ดํ„ฐ ์—ฐ์‚ฐ์˜ ์œ ํ–‰์œผ๋กœ ์ธํ•ด ๊ทธ ์ˆ˜์š”๊ฐ€ ํฌ๊ฒŒ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ํด๋ผ์šฐ๋“œ ์ปดํ“จํŒ…์˜ ๋„์ž…์œผ๋กœ์ธํ•ด ๊ณ ๊ฐ์€ ์„œ๋ฒ„ ์œ ์ง€์— ๋Œ€ํ•œ ๋น„์šฉ์„ ํฌ๊ฒŒ ์ ˆ๊ฐํ•  ์ˆ˜ ์žˆ๊ณ  ์„œ๋น„์Šค ์ œ๊ณต์ž๋Š” ์—ฐ์‚ฐ ์ž์›์˜ ์ด์šฉ ํšจ์œจ์„ ๊ทน๋Œ€ํ™” ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์‹œ๋‚˜๋ฆฌ์˜ค์—์„œ ๋ฐ์ดํ„ฐ์„ผํ„ฐ ์ž…์žฅ์—์„œ๋Š” ์—ฐ์‚ฐ ์ž์› ํ™œ์šฉ ํšจ์œจ์„ ๊ฐœ์„ ํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•œ ๋ชฉํ‘œ๊ฐ€ ๋œ๋‹ค. ํŠนํžˆ ์ตœ๊ทผ ํญ์ฆํ•˜๊ณ  ์žˆ๋Š” ๋ฐ์ดํ„ฐ ์„ผํ„ฐ์˜ ๊ทœ๋ชจ๋ฅผ ๊ณ ๋ คํ•˜๋ฉด ์ž‘์€ ํšจ์œจ ๊ฐœ์„ ์œผ๋กœ๋„ ๋ง‰๋Œ€ํ•œ ๊ฒฝ์ œ์  ๊ฐ€์น˜๋ฅผ ์ฐฝ์ถœ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ฐ์ดํ„ฐ ์„ผํ„ฐ์˜ ํšจ์œจ์€ ์œ„์น˜ ์„ ์ •, ๊ตฌ์กฐ ์„ค๊ณ„, ๋ƒ‰๊ฐ ์‹œ์Šคํ…œ, ํ•˜๋“œ์›จ์–ด ๊ตฌ์„ฑ ๋“ฑ๋“ฑ ๋‹ค์–‘ํ•œ ์š”์†Œ๋“ค์— ์˜ํ–ฅ์„ ๋ฐ›์ง€๋งŒ, ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ํŠนํžˆ ์—ฐ์‚ฐ ๋ฐ ๋ฉ”๋ชจ๋ฆฌ ์ž์›์„ ๊ด€๋ฆฌํ•˜๋Š” ์†Œํ”„ํŠธ์›จ์–ด ์„ค๊ณ„ ๋ฐ ๊ตฌํ˜„์„ ๋‹ค๋ฃฌ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ฐ์ดํ„ฐ ์„ผํ„ฐ ํšจ์œจ ๊ฐœ์„ ์„ ํš๊ธฐ์ ์œผ๋กœ ๊ฐœ์„ ํ•˜๋Š” ๋‘๊ฐ€์ง€ ์†Œํ”„ํŠธ์›จ์–ด ๊ธฐ๋ฐ˜ ๊ธฐ์ˆ ์„ ์ œ์•ˆํ•œ๋‹ค. ์ฒซ ์งธ๋กœ ๊ฐ€์ƒํ™” ํ™˜๊ฒฝ์„ ์œ„ํ•œ ์†Œํ”„ํŠธ์›จ์–ด ๊ธฐ๋ฐ˜ ๋ฉ”๋ชจ๋ฆฌ ๋ถ„๋ฆฌ ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•œ๋‹ค. ์ตœ๊ทผ ๊ณ ์† ๋„คํŠธ์›Œํฌ์˜ ๋ฐœ์ „์œผ๋กœ ์ธํ•ด ์›๊ฒฉ ๋ฉ”๋ชจ๋ฆฌ ์ ‘๊ทผ ๋น„์šฉ์ด ํš๊ธฐ์ ์œผ๋กœ ์ค„์–ด ๋“ค์—ˆ๊ณ , ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ๊ณ ์„ฑ๋Šฅ ๋„คํŠธ์›Œํ‚น ํ•˜๋“œ์›จ์–ด๋ฅผ ์ด์šฉํ•˜์—ฌ ์›๊ฒฉ ๋ฉ”๋ชจ๋ฆฌ ์œ„์—์„œ ์‹คํ–‰๋˜๋Š” ๊ฐ€์ƒ ๋จธ์‹ ์˜ ํฐ ์„ฑ๋Šฅ ์ €ํ•˜ ์—†์ด ์‹คํ–‰ํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ธ๋‹ค. ์ œ์•ˆ๋œ ๊ธฐ์ˆ ์„ QEMU/KVM ๊ฐ€์ƒ๋จธ์‹  ํ•˜์ดํผ๋ฐ”์ด์ €๋ฅผ ํ†ตํ•ด ํ‰๊ฐ€ํ•œ ๊ฒฐ๊ณผ, ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ ๊ธฐ๋ฒ•์€ ๊ธฐ์กด ์‹œ์Šคํ…œ ๋Œ€๋น„ ์›๊ฒฉ ํŽ˜์ด์ง•์— ๋Œ€ํ•œ ๊ผฌ๋ฆฌ ์ง€์—ฐ์‹œ๊ฐ„์„ 98.2% ๊ฐœ์„ ํ•จ์„ ๋ณด์ธ๋‹ค. ๋˜ํ•œ ๋ž™ ๊ทœ๋ชจ์˜ ์ž‘์—…์ฒ˜๋ฆฌ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•œ ์‹คํ—˜์—์„œ, ์ œ์•ˆ๋œ ์‹œ์Šคํ…œ์€ ์ „์ฒด ์ž‘์—… ์ฒ˜๋ฆฌ ์‹œ๊ฐ„์„ ๊ธฐ์กด ์‹œ์Šคํ…œ ๋Œ€๋น„ 40.9% ์ค„์ผ ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ธ๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ ์›๊ฒฉ ๋ฉ”๋ชจ๋ฆฌ๋ฅผ ์ด์šฉํ•˜๋Š” ์ฆ‰๊ฐ์ ์ธ ๊ฐ€์ƒ๋จธ์‹  ์ด์ฃผ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜๋‹ค. ๊ฐ€์ƒํ™” ํ™˜๊ฒฝ์˜ ์›๊ฒฉ ๋ฉ”๋ชจ๋ฆฌ ํ™œ์šฉ์— ๋Œ€ํ•œ ํ™•์žฅ์€ ๊ทธ๊ฒƒ๋งŒ์œผ๋กœ ์ž์› ์ด์šฉ๋ฅ  ํ–ฅ์ƒ์— ๋Œ€ํ•ด ํฐ ๊ธฐ์—ฌ๋ฅผ ํ•˜์ง€๋งŒ, ์—ฌ์ „ํžˆ ํ•œ ์„œ๋ฒ„์—์„œ ์—ฌ๋Ÿฌ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์ด ๊ฒฝ์Ÿ์ ์œผ๋กœ ์ž์›์„ ์ด์šฉํ•˜๋Š” ๊ฒฝ์šฐ ์„ฑ๋Šฅ์ด ํฌ๊ฒŒ ์ €ํ•˜ ๋  ์ˆ˜ ์žˆ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•˜๋Š” ์ฆ‰๊ฐ์ ์ธ ๊ฐ€์ƒ๋จธ์‹  ์ด์ฃผ ๊ธฐ๋ฒ•์€ ์›๊ฒฉ ๋ฉ”๋ชจ๋ฆฌ ์ƒ์—์„œ ์•„์ฃผ ์ž‘์€ ๋ฉ”ํƒ€๋ฐ์ดํ„ฐ์˜ ์ „์†ก๋งŒ์œผ๋กœ ๊ฐ€์ƒ๋จธ์‹ ์˜ ์ด์ฃผ๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋ฉฐ, ๋ฉ”๋ชจ๋ฆฌ ์ƒ์— ํ‚ค์™€ ๊ฐ’์„ ์ €์žฅํ•˜๋Š” ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๋ฒค์น˜๋งˆํฌ๋ฅผ ์‹คํ–‰ํ•˜๋Š” ๊ฐ€์ƒ๋จธ์‹ ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ํ‰๊ฐ€์—์„œ ๊ธฐ์กด ๊ธฐ๋ฒ•๋Œ€๋น„ ์‹ค์งˆ์ ์ธ ์„œ๋น„์Šค ์ค‘๋‹จ์‹œ๊ฐ„์„ ์ตœ๋Œ€ 92.6% ๊ฐœ์„ ํ•จ์„ ๋ณด์ธ๋‹ค.The raising importance of big data and artificial intelligence (AI) has led to an unprecedented shift in moving local computation into the cloud. One of the key drivers behind this transformation was the exploding cost of owning and maintaining large computing systems powerful enough to process these new workloads. Customers experience a reduced cost by renting only the required resources and only when needed, while data center operators benefit from efficiency at scale. A key factor in operating a profitable data center is a high overall utilization of its resources. Due to the scale of modern data centers, small improvements in efficiency translate to significant savings in the total cost of ownership (TCO). There are many important elements that constitute an efficient data center such as its location, architecture, cooling system, or the employed hardware. In this thesis, we focus on software-related aspects, namely the utilization of computational and memory resources. Reports from data centers operated by Alibaba and Google show that the overall resource utilization has stagnated at a level of around 50 to 60 percent over the past decade. This low average utilization is mostly attributable to peak demand-driven resource allocation despite the high variability of modern workloads in their resource usage. In other words, data centers today lack an efficient way to put idle resources that are reserved but not used to work. In this dissertation we present RackMem, a software-based solution to address the problem of low resource utilization through two main contributions. First, we introduce a disaggregated memory system tailored for virtual environments. We observe that virtual machines can use remote memory without noticeable performance degradation under moderate memory pressure on modern networking infrastructure. We implement a specialized remote paging system for QEMU/KVM that reduces the remote paging tail-latency by 98.2% in comparison to the state of the art. A job processing simulation at rack-scale shows that the total makespan can be reduced by 40.9% under our memory system. While seamless disaggregated memory helps to balance memory usage across nodes, individual nodes can still suffer overloaded resources if co-located workloads exhibit high resource usage at the same time. In a second contribution, we present a novel live migration technique for machines running on top of our remote paging system. Under this instant live migration technique, entire virtual machines can be migrated in as little as 100 milliseconds. An evaluation with in-memory key-value database workloads shows that the presented migration technique improves the state of the art by a wide margin in all key performance metrics. The presented software-based solutions lay the technical foundations that allow data center operators to significantly improve the utilization of their computational and memory resources. As future work, we propose new job schedulers and load balancers to make full use of these new technical foundations.Chapter 1. Introduction 1 1.1 Contributions of the Dissertation 3 Chapter 2. Background 5 2.1 Resource Disaggregation 5 2.2 Transparent Remote Paging 7 2.3 Remote Direct Memory Access (RDMA) 9 2.4 Live Migration of Virtual Machines 10 Chapter 3. RackMem Overview 13 3.1 RackMem Virtual Memory 13 3.2 RackMem Distributed Virtual Storage 14 3.3 RackMem Networking 15 3.4 Instant VM Live Migration 16 Chapter 4. Virtual Memory 17 4.1 Design Considerations for Achieving Low-latency 19 4.2 Pagefault handling 20 4.2.1 Fast-path and slow-path in the pagefault handler 21 4.2.2 State transition of RackVM page 23 4.3 Latency Hiding Techniques 25 4.4 Implementation 26 4.4.1 RackMem Virtual Memory Module 27 4.4.2 Dynamic Rebalancing of Local Memory 29 4.4.3 RackVM for Virtual Machines 29 4.4.4 Running Unmodified Applications 30 Chapter 5. RackMem Distributed Virtual Storage 31 5.1 The distributed Storage Abstraction 32 5.2 Memory Management 33 5.2.1 Remote memory allocation 33 5.2.2 Remote memory reclamation 33 5.3 Fault Tolerance 34 5.3.1 Fault-tolerance and Write-duplication 34 5.4 Multiple Storage Support in RackMem 36 5.5 Implementation 38 5.5.1 The Remote Memory Backend 38 5.5.2 Linux Demand Paging on RackDVS 39 Chapter 6. Networking 40 6.1 Design of RackNet 40 6.2 Implementation 41 6.2.1 RPC message layout 41 6.2.2 RackNet RPC Implementation 42 Chapter 7. Instant VM Live Migration 44 7.1 Motivation 45 7.1.1 The need for a tailored live migration technique 45 7.1.2 Software Bottlenecks 46 7.1.3 Utilizing workload variability 46 7.2 Design of Instant 47 7.2.1 Instant Region Migration 47 7.3 Implementation 48 7.3.1 Extension of RackVM for Instant 49 7.3.2 Instant region migration 49 7.3.3 Pre-fetch optimizations 51 7.3.4 Downtime optimizations 51 7.3.5 QEMU modification for Instant 52 Chapter 8. Evaluation - RackMem 53 8.1 Execution Environment 54 8.2 Pagefault Handler Latency 56 8.3 Single Application Performance 57 8.3.1 Batch-oriented Applications 58 8.3.2 Internal Pagesize and Performance 59 8.3.3 Write-duplication overhead 60 8.3.4 RackDVS slab size and performance 62 8.3.5 Latency-oriented Applications 63 8.3.6 Network Bandwidth Analysis 64 8.3.7 Dynamic Local Memory Partitioning 66 8.3.8 Rack-scale Job Processing Simulation 67 Chapter 9. Evaluation - Instant VM Live Migration 69 9.1 Experimental setup 69 9.2 Target Applications 70 9.3 Comparison targets 70 9.4 Database and client setups 71 9.5 Memory disaggregation scenarios 71 9.6.1 Time-to-responsiveness 71 9.6.2 Effective Downtime 73 9.6.3 Effect of Instant optimizations 75 Chapter 10. Conclusion 77 10.1 Future Directions 78 ์š”์•ฝ 89๋ฐ•

    Regulation of cellular behaviors via a cross-talk between TM4SF5 and protein tyrosine phosphatase receptor type-F (PTPRF) in liver cancer cells.

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ์•ฝํ•™๋Œ€ํ•™ ์•ฝํ•™๊ณผ, 2017. 8. ์ด์ •์›.Transmembrane 4 L six family member 5 (TM4SF5) is a glycoprotein on cell surface and highly expressed in various cancers including hepatocarcinoma. In previous studies, it is shown that TM4SF5 induces Epithelial-Mesenchymal Transition (EMT), as a regulation factor of cell morphological change, cell migration, invasion, and proliferation. In this study, I found protein tyrosine phosphatase receptor type-F (PTPRF) as a new binding partner of TM4SF5 through mass spectrometry. PTPRF is a transmembrane tyrosine phosphatase with one transmembrane domain. PTPRF is known to regulate cell proliferation, differentiation, and mitotic cell cycle, being associated with cell death-related protein activation. Here, I investigated how this interaction affected cellular behaviors using cell lines with overexpression or suppression of TM4SF5 and/or PTPRF. First, I could confirm their interaction by co-immunoprecipitation and immunofluorescence. Overexpression or down-regulation of either TM4SF5 or PTPRF did not affect their mRNA or protein levels. Further, the interaction between TM4SF5 and PTPRF decreased in the suspension state of the cells and recovered significantly when the cells became reattached to the extracellular matrix (ECM). I could thus rationalize that TM4SF5 and PTPRF interaction was correlated with and affect PTPRFs tyrosine-phosphatase activity. In a previous study, it was reported that TM4SF5 regulates activities of focal adhesion (FA) molecules and formation of FA enriched with paxillin. So, I focused on the influence of PTPRF expression on activities of FA molecules in the presence or absence of TM4SF5 expression. PTPRF co-localized with Paxillin during immunofluorescence studies, and overexpression of PTPRF led to dephosphorylation of FAK, Src, and Paxillin. When TM4SF5 and PTPRF were co-overexpressed, PTPRFs effects against phosphorylation of FAK, Src, and Paxillin were inhibited, indicating that TM4SF5 could antagonize PTPRF. To investigate such effects of PTPRF on cellular behaviors, I performed cell adhesion assay, cell migration assay, and sphere formation assay for the purposes of understanding the roles of the both in cancer metastasis. During the cell adhesion assay, cells expressing PTPRF inhibited tyrosine phosphorylation of paxillin in the suspended state, whereas cells lacking PTPRF retained tyrosine phosphorylation of paxillin in the suspended state. When those cells were attached to ECM fibronectin, PTPRF-lacking cells formed FAs faster than control PTPRF-expressing cells. However, cells lacking both PTPRF and TM4SF5 showed paxillin phosphorylation higher than TM4SF5 alone-lacking cells. In the cell migration assay, migration ability of PTPRF-lacking cells increased but cells lacking both PTPRF and TM4SF5 showed reduced migration ability than PTPRF alone-lacking cells. In case of the spheroid formation assay, PTPRF-lacking cells increased sphere formation but cells lacking both PTPRF and TM4SF5 showed a lower sphere formation capacity than PTPRF-alone lacking cells. Altogether, these observations suggest that the role of PTPRF in cells to inhibit molecules at FAs, such as FAK, c-Src, and Paxillin, would be controlled by TM4SF5, especially when the cells were attached to the ECMs. In HCC that TM4SF5 is overexpressed, however, TM4SF5 can promote tyrosine phosphorylation of FA molecules that would be negatively targeted by PTPRF. Thus, the coordinated cross-talks between TM4SF5 and PTPRF can play roles in successful cancer metastasis, which further can be a promising target to deals with TM4SF5-dependent metastasis.INTRODUCTION 1 MATERIALS AND METHODS 4 RESULTS 8 1. A new binding partner of TM4SF5 in hepatocarcinoma (HCC). 8 2. TM4SF5 inhibits the activity of PTPRF for focal adhesion molecules. 11 3. TM4SF5 and PTPRF regulate the adhesion and migration of HCC. 16 4. Sphere formation promoted by PTPRF depletion requires TM4SF5. 20 5. Summary of the present study. 25 DISCUSSION 26 REFERENCES 30 ๊ตญ๋ฌธ์ดˆ๋ก 34Maste
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