5 research outputs found

    Energy Efficient Multipath TCP for Mobile Devices

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    Most mobile devices today come with multiple access interfaces, e.g., 4G and WiFi. Multipath TCP (MP-TCP) can greatly improve network performance by exploiting the connection diversity of multiple access interfaces, at the expense of higher energy consumption. In this paper, we design MP-TCP algorithms for mobile devices by jointly considering the performance and energy consumption. We consider two main types of mobile applications: realtime applications that have a fixed duration and file transfer applications that have a fixed data size. For each type of applications, we propose a two-timescale algorithm with theoretical guarantee on the performance. We present simulation results that show that our algorithms can reduce energy consumption by up to 22% without sacrificing throughput compared to a baseline MP-TCP algorithm

    Systems and Methods for Measuring and Improving End-User Application Performance on Mobile Devices

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    In today's rapidly growing smartphone society, the time users are spending on their smartphones is continuing to grow and mobile applications are becoming the primary medium for providing services and content to users. With such fast paced growth in smart-phone usage, cellular carriers and internet service providers continuously upgrade their infrastructure to the latest technologies and expand their capacities to improve the performance and reliability of their network and to satisfy exploding user demand for mobile data. On the other side of the spectrum, content providers and e-commerce companies adopt the latest protocols and techniques to provide smooth and feature-rich user experiences on their applications. To ensure a good quality of experience, monitoring how applications perform on users' devices is necessary. Often, network and content providers lack such visibility into the end-user application performance. In this dissertation, we demonstrate that having visibility into the end-user perceived performance, through system design for efficient and coordinated active and passive measurements of end-user application and network performance, is crucial for detecting, diagnosing, and addressing performance problems on mobile devices. My dissertation consists of three projects to support this statement. First, to provide such continuous monitoring on smartphones with constrained resources that operate in such a highly dynamic mobile environment, we devise efficient, adaptive, and coordinated systems, as a platform, for active and passive measurements of end-user performance. Second, using this platform and other passive data collection techniques, we conduct an in-depth user trial of mobile multipath to understand how Multipath TCP (MPTCP) performs in practice. Our measurement study reveals several limitations of MPTCP. Based on the insights gained from our measurement study, we propose two different schemes to address the identified limitations of MPTCP. Last, we show how to provide visibility into the end- user application performance for internet providers and in particular home WiFi routers by passively monitoring users' traffic and utilizing per-app models mapping various network quality of service (QoS) metrics to the application performance.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146014/1/ashnik_1.pd

    Energy Awareness for Multiple Network Interface-Activated Smartphone

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2017. 8. ์ตœ์„ฑํ˜„.์ตœ์‹  ์Šค๋งˆํŠธํฐ์€ LTE์™€ Wi-Fi์™€ ๊ฐ™์€ ๋„คํŠธ์›Œํฌ ์ธํ„ฐํŽ˜์ด์Šค ์—ฌ๋Ÿฌ ๊ฐœ๋ฅผ ๋™์‹œ์— ์‚ฌ์šฉํ•˜์—ฌ ์ „์†ก์œจ์„ ์ฆ๊ฐ€์‹œํ‚ค๊ฑฐ๋‚˜ ๋„คํŠธ์›Œํฌ ์ ‘์†์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฐ ๊ฒฝ์šฐ์—, ์Šค๋งˆํŠธํฐ์˜ ์—๋„ˆ์ง€ ์†Œ๋ชจ๋Š” LTE์™€ Wi-Fi๋ฅผ ๋™์‹œ์— ์‚ฌ์šฉํ•จ์— ๋”ฐ๋ผ ์ฆ๊ฐ€ํ•  ์ˆ˜ ์žˆ๋‹ค. ํŠนํžˆ, ์ œํ•œ๋œ ์šฉ๋Ÿ‰์„ ๊ฐ€์ง€๋Š” ๋ฐฐํ„ฐ๋ฆฌ๋กœ ๋™์ž‘์„ ํ•˜๋Š” ์Šค๋งˆํŠธํฐ์—์„œ ์—๋„ˆ์ง€ ์†Œ๋ชจ๋Š” ์ค‘์š”ํ•œ ์ด์Šˆ์ด๊ธฐ ๋•Œ๋ฌธ์—, ์—๋„ˆ์ง€ ์ฆ๊ฐ€์™€ ์„ฑ๋Šฅ ํ–ฅ์ƒ ์‚ฌ์ด์˜ trade-off๋ฅผ ๊ณ ๋ คํ•˜๋Š” ๊ฒƒ์ด ์š”๊ตฌ๋œ๋‹ค. ์—๋„ˆ์ง€ ์ธ์ง€ ๊ธฐ์ˆ ๊ณผ ํ•จ๊ป˜ ์Šค๋งˆํŠธํฐ์˜ LTE์™€ Wi-Fi ์ธํ„ฐํŽ˜์ด์Šค๋ฅผ ์ „๋žต์ ์œผ๋กœ ์ž˜ ์‚ฌ์šฉํ•จ์œผ๋กœ์จ, ๋ฐฐํ„ฐ๋ฆฌ ์—๋„ˆ์ง€ ์†Œ๋ชจ๋ฅผ ์ค„์ž„๊ณผ ๋™์‹œ์— ์Šค๋งˆํŠธํฐ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์˜ ์„ฑ๋Šฅ์„ ์ตœ์ ํ™” ํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•ด์ง„๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” LTE์™€ Wi-Fi ๋งํฌ๋ฅผ ๋™์‹œ์— ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์Šค๋งˆํŠธํฐ์—์„œ ์—๋„ˆ์ง€ ์ธ์ง€๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋Š” ๋‹ค์Œ ์„ธ ๊ฐ€์ง€ ์ „๋žต์„ ๊ณ ๋ คํ•˜์˜€๋‹ค. (i) LTE์™€ Wi-Fi๋ฅผ ๋™์‹œ์— ์‚ฌ์šฉํ•˜๋Š” ์Šค๋งˆํŠธํฐ์˜ ์ „๋ ฅ ์†Œ๋ชจ ๋ชจ๋ธ๋ง, (ii) ์Šค๋งˆํŠธํฐ์˜ ๋ฐฐํ„ฐ๋ฆฌ ์†Œ๋ชจ์œจ์˜ ์‹ค์‹œ๊ฐ„ ์˜ˆ์ธก ๊ธฐ๋ฒ•, (iii) dynamic adaptive streaming over HTTP (DASH) ๊ธฐ๋ฐ˜์˜ ๋น„๋””์˜ค ์ŠคํŠธ๋ฆฌ๋ฐ์˜ ์„ฑ๋Šฅ ์ตœ์ ํ™”. ๋จผ์ €, ๋™์‹œ์— ์—ฌ๋Ÿฌ ๋„คํŠธ์›Œํฌ๋ฅผ ํ™œ์„ฑํ™”ํ•˜์—ฌ ์‚ฌ์šฉํ•˜๋Š” ์Šค๋งˆํŠธํฐ์˜ ์ •๋ฐ€ํ•œ ์ „๋ ฅ ์†Œ๋ชจ ๋ชจ๋ธ๋ง์„ ์ œ์‹œํ•œ๋‹ค. ํŒจํ‚ท ์ฒ˜๋ฆฌ์— ์˜ํ•ด ์†Œ๋ชจ๋˜๋Š” ์ „๋ ฅ๊ณผ ๋„คํŠธ์›Œํฌ ์ธํ„ฐํŽ˜์ด์Šค์—์„œ ์†Œ๋ชจ๋˜๋Š” ์ „๋ ฅ์„ ๋ถ„ํ•ดํ•˜์—ฌ, ๋‹ค์ค‘ ๋„คํŠธ์›Œํฌ ์ธํ„ฐํŽ˜์ด์Šค๋ฅผ ํ™œ์„ฑํ™”์‹œํ‚ค๋Š” ๊ฒฝ์šฐ์˜ ์ •๋ฐ€ํ•œ ์ „๋ ฅ ์†Œ๋ชจ ๋ชจ๋ธ์„ ๊ตฌ์„ฑํ•œ๋‹ค. ๋‹ค์–‘ํ•œ ์‹œ๋‚˜๋ฆฌ์˜ค์—์„œ ์ธก์ •๋œ ์ „๋ ฅ๊ณผ ๋ชจ๋ธ์„ ํ†ตํ•ด ์˜ˆ์ธก๋œ ์ „๋ ฅ์„ ๋น„๊ตํ•˜๋ฉฐ ์ œ์•ˆํ•˜๋Š” ์ „๋ ฅ ์†Œ๋ชจ ๋ชจ๋ธ์˜ ์ •ํ™•์„ฑ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๊ธฐ์กด ์ „๋ ฅ ์†Œ๋ชจ ๋ชจ๋ธ์— ๋น„ํ•ด ๋‹จ์ผ ๋„คํŠธ์›Œํฌ ํ†ต์‹ ์˜ ๊ฒฝ์šฐ์—๋„ 7%-35% ๋งŒํผ ์ถ”์ • ์˜ค์ฐจ๋ฅผ ์ค„์˜€์œผ๋ฉฐ, ๋‹ค์ค‘ ๋„คํŠธ์›Œํฌ ํ†ต์‹ ์˜ ๊ฒฝ์šฐ ์ถ”์ • ์˜ค์ฐจ๋ฅผ 25% ์ค„์ž„์„ ๋ณด์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ, ์Šค๋งˆํŠธํฐ์˜ ์‹ค์‹œ๊ฐ„ ์—๋„ˆ์ง€ ์ธ์ง€๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๊ธฐ ์œ„ํ•ด์„œ, ์Šค๋งˆํŠธํฐ์—์„œ ์‚ฌ์šฉํ•˜๊ณ  ์žˆ๋Š” Li-ion ๋ฐฐํ„ฐ๋ฆฌ์˜ ํŠน์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ๋ฐฐํ„ฐ๋ฆฌ ์†Œ๋ชจ์œจ์„ ์ถ”์ •ํ•˜๋Š” ๊ธฐ๋ฒ•์„ ๊ณ ์•ˆํ•œ๋‹ค. Li-ion ๋ฐฐํ„ฐ๋ฆฌ๋Š” ์˜จ๋„์™€ ๋…ธํ™” ์ƒํƒœ์— ๋”ฐ๋ผ์„œ ๊ฐ€์šฉ ์šฉ๋Ÿ‰๊ณผ ๋‚ด๋ถ€ ์ €ํ•ญ์ด ๋‹ฌ๋ผ์ง€๊ธฐ ๋•Œ๋ฌธ์—, ์˜จ๋„์™€ ๋…ธํ™” ์ƒํƒœ์— ๋”ฐ๋ผ์„œ ๋ฐฐํ„ฐ๋ฆฌ ์†Œ๋ชจ์œจ์ด ๋‹ฌ๋ผ์งˆ ์ˆ˜ ์žˆ๋‹ค. ๋ฐฐํ„ฐ๋ฆฌ ํŠน์„ฑ์„ ๋ชจ๋ธ๋งํ•˜๋Š” ๊ฒƒ์€ ์–ด๋ ค์šด ์ผ์ด๊ธฐ ๋•Œ๋ฌธ์—, effective resistance ๊ฐœ๋…์„ ๋„์ž…ํ•˜์—ฌ ์šฉ๋Ÿ‰๊ณผ ๋‚ด๋ถ€ ์ €ํ•ญ์„ ๋ชจ๋ฅด๊ณ ๋„ ๋ฐฐํ„ฐ๋ฆฌ ์†Œ๋ชจ์œจ์„ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋Š” BattTracker๋ฅผ ๊ณ ์•ˆํ•œ๋‹ค. BattTracker๋Š” ์‹ค์‹œ๊ฐ„ ๋ฐฐํ„ฐ๋ฆฌ ์†Œ๋ชจ์œจ์„ ์ตœ๋Œ€ 0.5์ดˆ ๋งˆ๋‹ค ์ถ”์ •ํ•  ์ˆ˜ ์žˆ๋‹ค. ์‹ค์ œ ์Šค๋งˆํŠธํฐ์œผ๋กœ ๋‹ค์–‘ํ•œ ์‹คํ—˜์„ ํ†ตํ•˜์—ฌ BattTracker๊ฐ€ ๋ฐฐํ„ฐ๋ฆฌ ์†Œ๋ชจ ์˜ˆ์ธก์„ 5% ์˜ค์ฐจ์œจ ์ด๋‚ด๋กœ ์˜ˆ์ธกํ•จ์„ ๋ณด์˜€์œผ๋ฉฐ, ์ด๋ฅผ ํ™œ์šฉํ•˜๋ฉด ๋†’์€ ์‹œ๊ฐ„ ํ•ด์ƒ๋„๋กœ ์Šค๋งˆํŠธํฐ์˜ ์—๋„ˆ์ง€ ์ธ์ง€ ๋™์ž‘์ด ๊ฐ€๋Šฅํ•ด์ง„๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์• ๋„ˆ์ง€ ์ธ์ง€ ๊ธฐ๋ฒ•๊ณผ LTE์™€ Wi-Fi ๋งํฌ๋ฅผ ๋™์‹œ์— ํ™œ์šฉํ•˜๋Š” ๊ฒƒ์„ dynamic adaptive streaming over HTTP (DASH) ๊ธฐ๋ฐ˜ ๋น„๋””์˜ค ์ŠคํŠธ๋ฆฌ๋ฐ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์— ์ ์šฉํ•œ๋‹ค. ์Šค๋งˆํŠธํฐ์—์„œ LTE์™€ Wi-Fi ๋งํฌ๋ฅผ ๋™์‹œ์— ํ™œ์šฉํ•˜๋Š” ๊ฒƒ์€ ๋‹ค์–‘ํ•œ ์ธก๋ฉด์—์„œ DASH ๊ธฐ๋ฐ˜์˜ ๋น„๋””์˜ค ์ŠคํŠธ๋ฆฌ๋ฐ์˜ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ๋ฐฐํ„ฐ๋ฆฌ ์—๋„ˆ์ง€์™€ LTE ๋ฐ์ดํ„ฐ ์‚ฌ์šฉ๋Ÿ‰์„ ์ ˆ์•ฝํ•˜๋ฉด์„œ ๋Š๊น€์—†๋Š” ๊ณ ํ™”์งˆ์˜ ๋น„๋””์˜ค๋ฅผ ์ŠคํŠธ๋ฆฌ๋ฐํ•˜๋Š” ๊ฒƒ์€ ๋„์ „์ ์ธ ์ผ์ด๋‹ค. ๋”ฐ๋ผ์„œ, DASH ๋น„๋””์˜ค๋ฅผ ์‹œ์ฒญํ•˜๋Š” ์‚ฌ์šฉ์ž์˜ Quality of Experience (QoE)๋ฅผ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ LTE์™€ Wi-Fi๋ฅผ ๋™์‹œ์— ํ™œ์šฉํ•˜๋Š” DASH video chunk ์š”์ฒญ ๊ธฐ๋ฒ•์ธ REQUEST๋ฅผ ์ œ์•ˆํ•œ๋‹ค. REQUEST๋Š” ์ฃผ์–ด์ง„ ๋ฐฐํ„ฐ๋ฆฌ ์—๋„ˆ์ง€์™€ LTE ์‚ฌ์šฉ๋Ÿ‰ ์˜ˆ์‚ฐ ๋‚ด์—์„œ ์ตœ์ ์— ๊ฐ€๊นŒ์šด ํ’ˆ์งˆ์˜ ๋น„๋””์˜ค๋ฅผ ๋Š๊น€์—†์ด ์ŠคํŠธ๋ฆฌ๋ฐํ•ด ์ฃผ๋Š” ๊ฒƒ์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•œ๋‹ค. ๋‹ค์–‘ํ•œ ํ™˜๊ฒฝ์—์„œ์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ณผ ์‹ค ํ™˜๊ฒฝ์—์„œ์˜ ์ธก์ •์„ ํ†ตํ•˜์—ฌ, REQUEST๊ฐ€ ๊ธฐ์กด ๋น„๋””์˜ค ์ŠคํŠธ๋ฆฌ๋ฐ ๊ธฐ๋ฒ•์— ๋น„ํ•ด ํ‰๊ท  ๋น„๋””์˜ค ํ’ˆ์งˆ, ์žฌ๋ฒ„ํผ๋ง, ์ž์› ๋‚ญ๋น„๋Ÿ‰ ๊ด€์ ์—์„œ ์ƒ๋‹นํžˆ ์šฐ์ˆ˜ํ•จ์„ ๋ณด์ธ๋‹ค. ์š”์•ฝํ•˜์ž๋ฉด, ์šฐ๋ฆฌ๋Š” LTE์™€ Wi-Fi์˜ ๋™์‹œ ์‚ฌ์šฉํ•˜๋Š” ์Šค๋งˆํŠธํฐ์—์„œ์˜ ์ „๋ ฅ ๋ชจ๋ธ๋ง ๋ฐฉ๋ฒ•๋ก , ์‹ค์‹œ๊ฐ„ ๋ฐฐํ„ฐ๋ฆฌ ์†Œ๋ชจ์œจ ์ถ”์ • ๊ธฐ๋ฒ•, DASH ๊ธฐ๋ฐ˜ ๋น„๋””์˜ค ์ŠคํŠธ๋ฆฌ๋ฐ ์„ฑ๋Šฅ ์ตœ์ ํ™”๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด์„œ, ์šฐ๋ฆฌ๋Š” ํ”„๋กœํ† ํƒ€์ž… ๊ตฌํ˜„๊ณผ ์‹คํ—˜ ์žฅ๋น„๋“ค์„ ํ†ตํ•œ ์‹ค์ธก ๊ธฐ๋ฐ˜์œผ๋กœ LTE์™€ Wi-Fi๋ฅผ ๋™์‹œ์— ํ™œ์šฉํ•˜๋Š” ์Šค๋งˆํŠธํฐ์„ ์œ„ํ•œ ์—๋„ˆ์ง€ ์ธ์ง€ ๊ธฐ์ˆ ์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๊ธฐ๋ฒ•๋“ค์˜ ์„ฑ๋Šฅ์€ ์ƒ์šฉ ์Šค๋งˆํŠธํฐ์— ๊ตฌํ˜„ํ•˜์—ฌ ์‹ค ํ™˜๊ฒฝ์—์„œ์˜ ์„ฑ๋Šฅ ํ‰๊ฐ€๋ฅผ ํ†ตํ•ด ๊ฒ€์ฆํ•˜์˜€๋‹ค.State-of-the-art smartphones can utilize multiple network interfaces simultaneously, e.g., LTE and Wi-Fi, to enhance throughput and network connectivity in various use cases. In this situation, energy consumption of smartphones can increase while using both LTE and Wi-Fi interfaces simultaneously. Since energy consumption is an important issue for smartphones powered by batteries with limited capacity, it is required to consider the trade-off between energy increase and performance enhancement. By judiciously utilizing both LTE and Wi-Fi interfaces of smartphones and energy awareness techniques, it is enabled to optimize the performance of smartphones applications while saving battery energy. In this dissertation, we consider the following three strategies to enable the energy awareness for smartphones which utilize both LTE and Wi-Fi links: (i) Power modeling for smartphone which utilizes both LTE and Wi-Fi links simultaneously, (ii) real-time battery drain rate estimation for smartphones, and (iii) optimizing the performance of dynamic adaptive streaming over HTTP (DASH)-based video streaming for smartphones. First, an accurate power modeling is presented for the smartphones, especially, those capable of activating/utilizing multiple networks simultaneously. By decomposing packet processing power and power consumed by network interfaces, we construct the accurate power model for multiple network interface-activated cases. The accuracy of our model is comparatively evaluated by comparing the estimated power with the measured power in various scenarios. We find that our model reduces estimation error by 7%โ€“35% even for single network transmissions, and by 25% for multiple network transmissions compared with existing power models. Second, in order to enable real-time energy awareness for smartphones, we develop a battery drain rate monitoring technique by considering characteristics of Li-ion batteries which are used by smartphones. With Li-ion battery, battery drain rate varies with temperature and battery aging, since they affect battery characteristics such as capacity and internal resistance. Since it is difficult to model the battery characteristics, we develop BattTracker, an algorithm to estimate battery drain rate without knowing the exact capacity and internal resistance by incorporating the concept of effective resistance. BattTracker tracks the instantaneous battery drain rate with up to 0.5 second time granularity. Extensive evaluation with smartphones demonstrates that BattTracker accurately estimates the battery drain rate with less than 5% estimation error, thus enabling energy-aware operation of smartphones with fine-grained time granularity. Finally, we adapt an energy awareness and utilize both LTE and Wi-Fi links for a Dynamic Adaptive Streaming over HTTP (DASH)-based video streaming application. Exploiting both LTE and Wi-Fi links simultaneously enhances the performance of DASH-based video streaming in various aspects. However, it is challenging to achieve seamless and high quality video while saving battery energy and LTE data usage to prolong the usage time of a smartphone. Thus, we propose REQUEST, a video chunk request policy for DASH in a smartphone, which can utilize both LTE andWi-Fi to enhance users Quality of Experience (QoE). REQUEST enables seamless DASH video streaming with near optimal video quality under given budgets of battery energy and LTE data usage. Through extensive simulation and measurement in a real environment, we demonstrate that REQUEST significantly outperforms other existing schemes in terms of average video bitrate, rebuffering, and resource waste. In summary, we propose a power modeling methodology, a real-time battery drain rate estimation method, and performance optimization of DASH-based video streaming for a smartphone which utilizes both LTE and Wi-Fi simultaneously. Through this research, we propose several energy-aware techniques for the smartphone, which especially utilizes both LTE and Wi-Fi, based on prototype implementation and the real measurement with experimental equipment. The performance of the proposed methods are validated by implementation on off-the-shelf smartphones and evaluations in real environments.1 Introduction 1 1.1 Motivation 1 1.2 Overview of Existing Approaches 3 1.2.1 Power modeling of smartphone 3 1.2.2 Battery drain rate estimation 4 1.2.3 Optimizing the performance of video streaming 5 1.3 Main Contributions 6 1.3.1 PIMM: Power Modeling of Multiple Network Interface-Activated Smartphone 6 1.3.2 BattTracker: Real-Time Battery Drain Rate Estimation 7 1.3.3 REQUEST: Performance Optimization of DASH Video Streaming 8 1.4 Organization of the Dissertation 8 2 PIMM: Packet Interval-Based Power Modeling of Multiple Network Interface-Activated Smartphones 10 2.1 Introduction 10 2.2 Background 12 2.2.1 Power saving operations of Wi-Fi and LTE 12 2.2.2 Related work 13 2.3 Power Consumption Modeling 14 2.3.1 Modeling Methodology 15 2.3.2 Packet interval-based power modeling 21 2.4 Practical issues 30 2.4.1 Impact of packet length 30 2.4.2 Impact of channel quality 33 2.5 Performance Evaluation 34 2.5.1 On-line power estimation 35 2.5.2 Single network data connections 37 2.5.3 Multiple network data connections 42 2.5.4 Model generation complexity 45 2.6 Summary 46 3 BattTracker: Enabling Energy Awareness for Smartphone Using Li-ion Battery Characteristics 47 3.1 Introduction 47 3.2 Background 50 3.2.1 Smartphones battery interface 50 3.2.2 State of Charge (SoC) and Open Circuit Voltage (Voc) 50 3.2.3 Batterys internal resistance 53 3.2.4 Related Work 53 3.3 Li-ion Battery Characteristics 54 3.3.1 Impact of temperature and aging on Li-ion battery 54 3.3.2 Measuring battery characteristics 54 3.3.3 Battery characteristics models 57 3.4 Effective Internal Resistance 58 3.4.1 Effective internal resistance (re) 58 3.4.2 Battery drain rate and lifetime derived from re 60 3.5 BattTracker Design 61 3.5.1 Rd estimator 62 3.5.2 Voc estimator 62 3.5.3 re estimator 64 3.6 Performance Evaluation 65 3.6.1 Comparison schemes and performance metrics 66 3.6.2 Convergence time of re 67 3.6.3 Power consumption overhead of BattTracker 67 3.6.4 Comparison with measurement using equipment 68 3.6.5 BattTracker with aged batteries 70 3.6.6 BattTracker with various video applications 70 3.6.7 BattTracker with varying temperature 73 3.7 Summary 74 4 REQUEST: Seamless Dynamic Adaptive Streaming over HTTP for Multi-Homed Smartphone under Resource Constraints 75 4.1 Introduction 75 4.2 Background and Related Work 77 4.2.1 Background 77 4.2.2 Related Work 79 4.3 Motivation 80 4.3.1 Wi-Fi Throughput Fluctuation 80 4.3.2 Optimizing Resource Utilization 82 4.4 Proposed Chunk Request Policy 83 4.5 Problem Formulation 86 4.6 REQUEST Algorithm 90 4.6.1 Request Interval Adaptation 91 4.6.2 Chunk Request Adaptation 94 4.7 Performance Evaluation 96 4.7.1 Prototype-Based Evaluation 98 4.7.2 Trace-Driven Simulation 102 4.8 Summary 104 5 Concluding Remarks 106 5.1 Research Contributions 106 5.2 Future Work 107 Abstract (In Korean) 117Docto
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