4,469 research outputs found
์ฃ์ง ํด๋ผ์ฐ๋ ํ๊ฒฝ์ ์ํ ์ฐ์ฐ ์คํ๋ก๋ฉ ์์คํ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ ๋ณด๊ณตํ๋ถ,2020. 2. ๋ฌธ์๋ฌต.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 ์ ์ฌ์ฉ์๊ฐ ์ด๋ํ๋ ๋์์๋ ์ํํ ์ฐ์ฐ ์คํ๋ก๋ฉ ์๋น์ค๋ฅผ ์ ๊ณตํ๋ ๊ฒฝ๋ ์ฃ์ง ์ปดํจํ
์์คํ
์ ๊ตฌ์ถํ๋ ๊ฒ์
๋๋ค. ์น ์ดํ๋ฆฌ์ผ์ด์
๊ณผ ์ธ๊ณต์ ๊ฒฝ๋ง (DNN: Deep Neural Network) ์ด๋ผ๋ ๋ ๊ฐ์ง ์ดํ๋ฆฌ์ผ์ด์
๋๋ฉ์ธ์์ ์ฐ๊ตฌ๋ฅผ ์งํํ์ต๋๋ค.
์ฒซ์งธ, ์น ์ง์ ์ฅ์น์์ ์ฃ์ง ์๋ฒ๋ก ์ฐ์ฐ์ ์คํ๋ก๋ํ๋ ์ฃ์ง ์ปดํจํ
์์คํ
์ ์ ์ํฉ๋๋ค. ์ ์๋ ์์คํ
์ ์น ์ฑ์ ์คํ ์ํ๋ฅผ ๋ง์ด๊ทธ๋ ์ด์
ํ ๋ ์น ์ฑ์ ๋์ ์ด์์ฑ(์์ค ์ฝ๋๋ก ๋ฐฐํฌ๋๊ณ ์ค์นํ์ง ์๊ณ ์คํํ ์ ์์)์ ํ์ฉํฉ๋๋ค. ์ด๋ฅผ ํตํด ์ํ ๋ง์ด๊ทธ๋ ์ด์
์ ๋ณต์ก์ฑ์ด ํฌ๊ฒ ์ค์ฌ์ ์น ์ฑ์ด ๋ช ์ด ๋ด์ ๋ง์ด๊ทธ๋ ์ด์
๋ ์ ์์ต๋๋ค. ๋ํ, ์ ์๋ ์์คํ
์ ์น ์ดํ๋ฆฌ์ผ์ด์
์ ์ํ ํ์ค ์ ์์ค ์ธ์คํธ๋ญ์
์ธ ์น ์ด์
๋ธ๋ฆฌ ์คํ๋ก๋๋ฅผ ์ง์ํ์ฌ ์์ํ JavaScript ์ฝ๋ ์คํ๋ก๋์ ๋น๊ตํ์ฌ ์ต๋ 8.4 ๋ฐฐ์ ์๋ ํฅ์์ ๋ฌ์ฑํ์ต๋๋ค.
๋์งธ, DNN ์ดํ๋ฆฌ์ผ์ด์
์ ์ฃ์ง ์๋ฒ์ ๋ฐฐํฌํ ๋, DNN ๋ชจ๋ธ์ ์ ์กํ๋ ๋์ DNN ์ฐ์ฐ์ ์คํ๋ก๋ ํ์ฌ ๋น ๋ฅด๊ฒ ์ฑ๋ฅํฅ์์ ๋ฌ์ฑํ ์ ์๋ ์ ์ง์ ์คํ๋ก๋ ๋ฐฉ์์ ์ ์ํฉ๋๋ค. ๋ํ, ๋ชจ๋ฐ์ผ ์ฌ์ฉ์๊ฐ ๋ฐฉ๋ฌธ ํ ๊ฒ์ผ๋ก ์์๋๋ ์ฃ์ง ์๋ฒ๋ก DNN ๋ ์ด์ด๋ฅผ ์ฌ์ ์ ๋ง์ด๊ทธ๋ ์ด์
ํ์ฌ ์ฝ๋ ์คํํธ ์ฑ๋ฅ์ ํฅ์์ํค๋ ๋ฐฉ์์ ์ ์ ํฉ๋๋ค. ์คํ ์์ค ๋ชจ๋น๋ฆฌํฐ ๋ฐ์ดํฐ์
์ ์ด์ฉํ ์๋ฎฌ๋ ์ด์
์์, DNN ๋ชจ๋ธ์ ๋ฐฐํฌํ๋ฉด์ ๋ฐ์ํ๋ ์ฑ๋ฅ ์ ํ๋ฅผ ์ ์ ํ๋ ๋ฐฉ์์ด ํฌ๊ฒ ์ค์ผ ์ ์์์ ํ์ธํ์์ต๋๋ค.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
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
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
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
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
- โฆ