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    ์—ฃ์ง€ ํด๋ผ์šฐ๋“œ ํ™˜๊ฒฝ์„ ์œ„ํ•œ ์—ฐ์‚ฐ ์˜คํ”„๋กœ๋”ฉ ์‹œ์Šคํ…œ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€,2020. 2. ๋ฌธ์ˆ˜๋ฌต.The purpose of my dissertation is to build lightweight edge computing systems which provide seamless offloading services even when users move across multiple edge servers. I focused on two specific application domains: 1) web applications and 2) DNN applications. I propose an edge computing system which offload computations from web-supported devices to edge servers. The proposed system exploits the portability of web apps, i.e., distributed as source code and runnable without installation, when migrating the execution state of web apps. This significantly reduces the complexity of state migration, allowing a web app to migrate within a few seconds. Also, the proposed system supports offloading of webassembly, a standard low-level instruction format for web apps, having achieved up to 8.4x speedup compared to offloading of pure JavaScript codes. I also propose incremental offloading of neural network (IONN), which simultaneously offloads DNN execution while deploying a DNN model, thus reducing the overhead of DNN model deployment. Also, I extended IONN to support large-scale edge server environments by proactively migrating DNN layers to edge servers where mobile users are predicted to visit. Simulation with open-source mobility dataset showed that the proposed system could significantly reduce the overhead of deploying a DNN model.๋ณธ ๋…ผ๋ฌธ์˜ ๋ชฉ์ ์€ ์‚ฌ์šฉ์ž๊ฐ€ ์ด๋™ํ•˜๋Š” ๋™์•ˆ์—๋„ ์›ํ™œํ•œ ์—ฐ์‚ฐ ์˜คํ”„๋กœ๋”ฉ ์„œ๋น„์Šค๋ฅผ ์ œ๊ณตํ•˜๋Š” ๊ฒฝ๋Ÿ‰ ์—ฃ์ง€ ์ปดํ“จํŒ… ์‹œ์Šคํ…œ์„ ๊ตฌ์ถ•ํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์›น ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜๊ณผ ์ธ๊ณต์‹ ๊ฒฝ๋ง (DNN: Deep Neural Network) ์ด๋ผ๋Š” ๋‘ ๊ฐ€์ง€ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜ ๋„๋ฉ”์ธ์—์„œ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ–ˆ์Šต๋‹ˆ๋‹ค. ์ฒซ์งธ, ์›น ์ง€์› ์žฅ์น˜์—์„œ ์—ฃ์ง€ ์„œ๋ฒ„๋กœ ์—ฐ์‚ฐ์„ ์˜คํ”„๋กœ๋“œํ•˜๋Š” ์—ฃ์ง€ ์ปดํ“จํŒ… ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ์ œ์•ˆ๋œ ์‹œ์Šคํ…œ์€ ์›น ์•ฑ์˜ ์‹คํ–‰ ์ƒํƒœ๋ฅผ ๋งˆ์ด๊ทธ๋ ˆ์ด์…˜ ํ•  ๋•Œ ์›น ์•ฑ์˜ ๋†’์€ ์ด์‹์„ฑ(์†Œ์Šค ์ฝ”๋“œ๋กœ ๋ฐฐํฌ๋˜๊ณ  ์„ค์น˜ํ•˜์ง€ ์•Š๊ณ  ์‹คํ–‰ํ•  ์ˆ˜ ์žˆ์Œ)์„ ํ™œ์šฉํ•ฉ๋‹ˆ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์ƒํƒœ ๋งˆ์ด๊ทธ๋ ˆ์ด์…˜์˜ ๋ณต์žก์„ฑ์ด ํฌ๊ฒŒ ์ค„์—ฌ์„œ ์›น ์•ฑ์ด ๋ช‡ ์ดˆ ๋‚ด์— ๋งˆ์ด๊ทธ๋ ˆ์ด์…˜ ๋  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๋˜ํ•œ, ์ œ์•ˆ๋œ ์‹œ์Šคํ…œ์€ ์›น ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ์œ„ํ•œ ํ‘œ์ค€ ์ €์ˆ˜์ค€ ์ธ์ŠคํŠธ๋Ÿญ์…˜์ธ ์›น ์–ด์…ˆ๋ธ”๋ฆฌ ์˜คํ”„๋กœ๋“œ๋ฅผ ์ง€์›ํ•˜์—ฌ ์ˆœ์ˆ˜ํ•œ JavaScript ์ฝ”๋“œ ์˜คํ”„๋กœ๋“œ์™€ ๋น„๊ตํ•˜์—ฌ ์ตœ๋Œ€ 8.4 ๋ฐฐ์˜ ์†๋„ ํ–ฅ์ƒ์„ ๋‹ฌ์„ฑํ–ˆ์Šต๋‹ˆ๋‹ค. ๋‘˜์งธ, DNN ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ์—ฃ์ง€ ์„œ๋ฒ„์— ๋ฐฐํฌํ•  ๋•Œ, DNN ๋ชจ๋ธ์„ ์ „์†กํ•˜๋Š” ๋™์•ˆ DNN ์—ฐ์‚ฐ์„ ์˜คํ”„๋กœ๋“œ ํ•˜์—ฌ ๋น ๋ฅด๊ฒŒ ์„ฑ๋Šฅํ–ฅ์ƒ์„ ๋‹ฌ์„ฑํ•  ์ˆ˜ ์žˆ๋Š” ์ ์ง„์  ์˜คํ”„๋กœ๋“œ ๋ฐฉ์‹์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ๋˜ํ•œ, ๋ชจ๋ฐ”์ผ ์‚ฌ์šฉ์ž๊ฐ€ ๋ฐฉ๋ฌธ ํ•  ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋˜๋Š” ์—ฃ์ง€ ์„œ๋ฒ„๋กœ DNN ๋ ˆ์ด์–ด๋ฅผ ์‚ฌ์ „์— ๋งˆ์ด๊ทธ๋ ˆ์ด์…˜ํ•˜์—ฌ ์ฝœ๋“œ ์Šคํƒ€ํŠธ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๋ฐฉ์‹์„ ์ œ์•ˆ ํ•ฉ๋‹ˆ๋‹ค. ์˜คํ”ˆ ์†Œ์Šค ๋ชจ๋นŒ๋ฆฌํ‹ฐ ๋ฐ์ดํ„ฐ์…‹์„ ์ด์šฉํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์—์„œ, DNN ๋ชจ๋ธ์„ ๋ฐฐํฌํ•˜๋ฉด์„œ ๋ฐœ์ƒํ•˜๋Š” ์„ฑ๋Šฅ ์ €ํ•˜๋ฅผ ์ œ์•ˆ ํ•˜๋Š” ๋ฐฉ์‹์ด ํฌ๊ฒŒ ์ค„์ผ ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€์Šต๋‹ˆ๋‹ค.Chapter 1. Introduction 1 1.1 Offloading Web App Computations to Edge Servers 1 1.2 Offloading DNN Computations to Edge Servers 3 Chapter 2. Seamless Offloading of Web App Computations 7 2.1 Motivation: Computation-Intensive Web Apps 7 2.2 Mobile Web Worker System 10 2.2.1 Review of HTML5 Web Worker 10 2.2.2 Mobile Web Worker System 11 2.3 Migrating Web Worker 14 2.3.1 Runtime State of Web Worker 15 2.3.2 Snapshot of Mobile Web Worker 16 2.3.3 End-to-End Migration Process 21 2.4 Evaluation 22 2.4.1 Experimental Environment 22 2.4.2 Migration Performance 24 2.4.3 Application Execution Performance 27 Chapter 3. IONN: Incremental Offloading of Neural Network Computations 30 3.1 Motivation: Overhead of Deploying DNN Model 30 3.2 Background 32 3.2.1 Deep Neural Network 33 3.2.2 Offloading of DNN Computations 33 3.3 IONN For DNN Edge Computing 35 3.4 DNN Partitioning 37 3.4.1 Neural Network (NN) Execution Graph 38 3.4.2 Partitioning Algorithm 40 3.4.3 Handling DNNs with Multiple Paths. 43 3.5 Evaluation 45 3.5.1 Experimental Environment 45 3.5.2 DNN Query Performance 46 3.5.3 Accuracy of Prediction Functions 48 3.5.4 Energy Consumption. 49 Chapter 4. PerDNN: Offloading DNN Computations to Pervasive Edge Servers 51 4.1 Motivation: Cold Start Issue 51 4.2 Proposed Offloading System: PerDNN 52 4.2.1 Edge Server Environment 53 4.2.2 Overall Architecture 54 4.2.3 GPU-aware DNN Partitioning 56 4.2.4 Mobility Prediction 59 4.3 Evaluation 63 4.3.1 Performance Gain of Single Client 64 4.3.2 Large-Scale Simulation 65 Chapter 5. RelatedWorks 73 Chapter 6. Conclusion. 78 Chapter 5. RelatedWorks 73 Chapter 6. Conclusion 78 Bibliography 80Docto

    Mobile cloud computing for computation offloading: Issues and challenges

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    International audienceDespite the evolution and enhancements that mobile devices have experienced, they are still considered as limited computing devices. Today, users become more demanding and expect to execute computational intensive applications on their smartphone devices. Therefore, Mobile Cloud Computing (MCC) integrates mobile computing and Cloud Computing (CC) in order to extend capabilities of mobile devices using offloading techniques. Computation offloading tackles limitations of Smart Mobile Devices (SMDs) such as limited battery lifetime, limited processing capabilities , and limited storage capacity by offloading the execution and workload to other rich systems with better performance and resources. This paper presents the current offloading frameworks, computation offloading techniques, and analyzes them along with their main critical issues. In addition , it explores different important parameters based on which the frameworks are implemented such as offloading method and level of partitioning. Finally, it summarizes the issues in offloading frameworks in the MCC domain that requires further research

    A Framework for Evaluating Model-Driven Self-adaptive Software Systems

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    In the last few years, Model Driven Development (MDD), Component-based Software Development (CBSD), and context-oriented software have become interesting alternatives for the design and construction of self-adaptive software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the modularity, flexibility, adaptability, and reliability of software systems. An analysis of these technologies shows them all to include the principle of the separation of concerns, and their further integration is a key factor to obtaining high-quality and self-adaptable software systems. Each technology identifies different concerns and deals with them separately in order to specify the design of the self-adaptive applications, and, at the same time, support software with adaptability and context-awareness. This research studies the development methodologies that employ the principles of model-driven development in building self-adaptive software systems. To this aim, this article proposes an evaluation framework for analysing and evaluating the features of model-driven approaches and their ability to support software with self-adaptability and dependability in highly dynamic contextual environment. Such evaluation framework can facilitate the software developers on selecting a development methodology that suits their software requirements and reduces the development effort of building self-adaptive software systems. This study highlights the major drawbacks of the propped model-driven approaches in the related works, and emphasise on considering the volatile aspects of self-adaptive software in the analysis, design and implementation phases of the development methodologies. In addition, we argue that the development methodologies should leave the selection of modelling languages and modelling tools to the software developers.Comment: model-driven architecture, COP, AOP, component composition, self-adaptive application, context oriented software developmen

    Distributed Hybrid Simulation of the Internet of Things and Smart Territories

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    This paper deals with the use of hybrid simulation to build and compose heterogeneous simulation scenarios that can be proficiently exploited to model and represent the Internet of Things (IoT). Hybrid simulation is a methodology that combines multiple modalities of modeling/simulation. Complex scenarios are decomposed into simpler ones, each one being simulated through a specific simulation strategy. All these simulation building blocks are then synchronized and coordinated. This simulation methodology is an ideal one to represent IoT setups, which are usually very demanding, due to the heterogeneity of possible scenarios arising from the massive deployment of an enormous amount of sensors and devices. We present a use case concerned with the distributed simulation of smart territories, a novel view of decentralized geographical spaces that, thanks to the use of IoT, builds ICT services to manage resources in a way that is sustainable and not harmful to the environment. Three different simulation models are combined together, namely, an adaptive agent-based parallel and distributed simulator, an OMNeT++ based discrete event simulator and a script-language simulator based on MATLAB. Results from a performance analysis confirm the viability of using hybrid simulation to model complex IoT scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0487

    A Catalog of Architectural Tactics for Cyber-Foraging

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    Mobile devices have become for many the preferred way of interacting with the Internet, social media and the enterprise. However, mobile devices still do not have the computing power or battery life that will allow them to perform effectively over long periods of time or for executing applications that require extensive communication or computation, or low latency. Cyber-foraging is a technique enabling mobile devices to extend their computing power and storage by offloading computation or data to more powerful servers located in the cloud or in single-hop proximity. This paper presents a catalog of architectural tactics for cyber-foraging that was derived from the results of a systematic literature review on architectures for cyber-foraging systems. Elements of the architectures identified in the primary studies were codified in the form of Architectural Tactics for Cyber-Foraging. These tactics will help architects extend their design reasoning towards cyber-foraging as a way to support the mobile applications of the present and the future
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