6 research outputs found

    Optimizing Software Platforms with Cross-Layer Resource Control and Scheduling

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    ν•™μœ„λ…Όλ¬Έ (박사)-- μ„œμšΈλŒ€ν•™κ΅ μœ΅ν•©κ³Όν•™κΈ°μˆ λŒ€ν•™μ› : μœ΅ν•©κ³Όν•™λΆ€(λ””μ§€ν„Έμ •λ³΄μœ΅ν•©μ „κ³΅), 2015. 2. ν™μ„±μˆ˜.μ†Œν”„νŠΈμ›¨μ–΄ ν”Œλž«νΌμ˜ κ±°λŒ€ν™” 및 λ³΅μž‘ν™” λ¬Έμ œμ— λŒ€μ‘ν•˜κΈ° μœ„ν•΄ ν”Œλž«νΌμ€ ν•„μ—°μ μœΌλ‘œ 계측적인 μ†Œν”„νŠΈμ›¨μ–΄ ꡬ쑰λ₯Ό μ±„νƒν•œλ‹€. μ΄λŸ¬ν•œ 계측 κ΅¬μ‘°μ—μ„œ μ„±λŠ₯ μ΅œμ ν™”λŠ” κ°œλ³„ κ³„μΈ΅μ—μ„œ 타 계측에 λ…λ¦½μ μœΌλ‘œ μˆ˜ν–‰λ˜λ©°, μΈμ ‘ν•œ κ³„μΈ΅λ“€μ˜ μ œν•œλœ μ •λ³΄λ§Œμ„ μ΄μš©ν•  수 있기 λ•Œλ¬Έμ— 전체둠적 μ„±λŠ₯ μ΅œμ ν™”μ˜ μΈ‘λ©΄μ—μ„œ ν•œκ³„κ°€ μžˆλ‹€. 이와 같이 계측적 μ†Œν”„νŠΈμ›¨μ–΄ ꡬ쑰에 κΈ°μΈν•˜λŠ” μ„±λŠ₯ μ΅œμ ν™”μ˜ 문제λ₯Ό ν•΄κ²°ν•˜κΈ° μœ„ν•΄ κ³ μ•ˆλœ 기법이 ꡐ차계측 μ΅œμ ν™”μ΄λ‹€. μ΄λŠ” 전체 μ„±λŠ₯에 영ν–₯을 μ£ΌλŠ” 각 κ³„μΈ΅μ˜ 정보λ₯Ό ν•„μš”ν•œ 계측에 μ„ νƒμ μœΌλ‘œ μ „λ‹¬ν•˜κ³ , 이λ₯Ό 톡해 전체둠적인 μ„±λŠ₯ μ΅œμ ν™”λ₯Ό λ‹¬μ„±ν•˜λŠ” 것에 κ·Έ λͺ©μ μ΄ μžˆλ‹€. λ”°λΌμ„œ μ΅œκ·Όμ—λŠ” ꡐ차계측적인 μ΅œμ ν™”λ₯Ό ν†΅ν•˜μ—¬ ν”Œλž«νΌμ˜ 전체둠적 μ„±λŠ₯을 ν–₯μƒμ‹œν‚€λ €λŠ” λ…Έλ ₯이 경주되고 μžˆλ‹€. 특히, μŠ€μΌ€μ€„λ§μ€ μ†Œν”„νŠΈμ›¨μ–΄ ν”Œλž«νΌμ—μ„œ λ°œμƒν•˜λŠ” λ‹€μ–‘ν•œ μ„±λŠ₯ μ΅œμ ν™” 문제 쀑에 μ‹œμŠ€ν…œ 전체에 영ν–₯을 끼칠 수 μžˆλŠ” μ€‘μš”ν•œ λ¬Έμ œμ΄λ‹€. λ³Έ ν•™μœ„λ…Όλ¬Έμ€ 계측적 μ†Œν”„νŠΈμ›¨μ–΄ ν”Œλž«νΌμ—μ„œ λ°œμƒν•  수 μžˆλŠ” 두 가지 μ„±λŠ₯ μ΅œμ ν™” 문제λ₯Ό ꡐ차계측 μžμ›κ΄€λ¦¬μ™€ μŠ€μΌ€μ€„λ§μ„ 톡해 ν•΄κ²° ν•  수 μžˆλŠ” 기법을 μ œμ•ˆν•œλ‹€. 첫째, λ³Έ μ—°κ΅¬λŠ” λ‚΄μž₯ν˜• μ‚¬μš©μž λ‹¨λ§μ—μ„œ μ‚¬μš©μž λ°˜μ‘μ„± μ΅œμ ν™”λ₯Ό μœ„ν•œ ꡐ차계측 μžμ›κ΄€λ¦¬ 기법을 μ œμ•ˆν•œλ‹€. μ œμ•ˆν•˜λŠ” 기법은 크게 두 가지 ꡬ성 μš”μ†Œλ“€λ‘œ 이루어진닀. 첫째, Framework-assisted Task Characterization(FTC)은 μƒμœ„ ν”„λ ˆμž„μ›Œν¬ 계측과 ν•˜μœ„ Linux kernel 계측 μ‚¬μ΄μ˜ ꡐ차계측적 μžμ›κ΄€λ¦¬ 기법이닀. μƒμœ„ κ³„μΈ΅μ—μ„œλŠ” μ‚¬μš©μž μƒν˜Έμ  νƒœμŠ€ν¬ 체인이 λŸ°νƒ€μž„μ— κ²€μΆœλ˜κ³ , ν•˜μœ„ κ³„μΈ΅μ—μ„œλŠ” 이 쀑 μ‚¬μš©μž λ°˜μ‘μ„±μ— 영ν–₯을 λΌμΉ˜λŠ” νƒœμŠ€ν¬λ“€μ˜ μš°μ„ μˆœμœ„λ₯Ό λ™μ μœΌλ‘œ μ¦μ§„ν•œλ‹€. 결과적으둜, 이 νƒœμŠ€ν¬λ“€μ€ λ‹€λ₯Έ νƒœμŠ€ν¬μ— μ˜ν•΄ 선점 λ‹Ήν•˜μ§€ μ•Šκ³  μ˜€λž«λ™μ•ˆ CPUλ₯Ό 점유 κ°€λŠ₯해지기 λ•Œλ¬Έμ— 맀우 μž‘μ€ 선점 μ§€μ—°μ‹œκ°„μ„ 보μž₯ν•œλ‹€. λ‘˜μ§Έ, Virtual Time-based Completely Fair Scheduler(VT-CFS)λŠ” Linux의 νƒœμŠ€ν¬ μŠ€μΌ€μ€„λŸ¬μΈ CFSλ₯Ό κ΅μ°¨κ³„μΈ΅μ μœΌλ‘œ μ΅œμ ν™”ν•œλ‹€. ꡬ체적으둜, μ΄λŠ” weighted fair queueing λ°©μ‹μ˜ μŠ€μΌ€μ€„λ§μ„ ν•˜μ—¬ 보닀 높은 응닡성을 보μž₯ν•œλ‹€. λ˜ν•œ, μƒμœ„ 계측 FTCλ‘œλΆ€ν„° νŒλ³„λœ μ‚¬μš©μž μƒν˜Έμ  νƒœμŠ€ν¬λ“€μ΄ λ°”μš΄λ“œ 된 μ‹€ν–‰ μ§€μ—°μ‹œκ°„μ— μŠ€μΌ€μ€„λ§ 될 수 μžˆλ„λ‘ κ·Έλ“€μ˜ virtual runtime을 μ‘°μ •ν•œλ‹€. λ‘˜μ§Έ, λ³Έ μ—°κ΅¬λŠ” λ©€ν‹°μ½”μ–΄ μŠ€μΌ€μ€„λ§μ„ 톡해 비둀곡정성 μ΅œμ ν™” 달성을 μœ„ν•œ Progress Balancing 기법을 μ œμ•ˆν•œλ‹€. μ΄λŠ” νƒœμŠ€ν¬μ˜ μƒλŒ€μ  진척 정도λ₯Ό virtual runtime으둜 μ •μ˜ν•˜κ³ , 이λ₯Ό κ· λ“±ν•˜κ²Œ λΆ€ν•˜λΆ„μ‚° ν•˜λŠ” 진척 기반 λΆ€ν•˜λΆ„μ‚° 기법이닀. ꡬ체적으둜, Progress Balancing은 μ‹œμŠ€ν…œ λ‚΄ νƒœμŠ€ν¬λ“€μ„ CPU μ½”μ–΄μ˜ 개수만큼의 νƒœμŠ€ν¬ 그룹으둜 λΆ„ν• ν•˜κ³  μƒλŒ€μ μœΌλ‘œ 진척이 λΉ λ₯Έ νƒœμŠ€ν¬λ₯Ό 느린 코어에 ν• λ‹Ήν•˜κ³ , λͺ¨λ“  μ½”μ–΄λ“€ κ°„ λ‘œλ“œ 차이λ₯Ό λ°”μš΄λ“œ μ‹œν‚¨λ‹€. 결과적으둜, νƒœμŠ€ν¬ κ°„ virtual runtime μ°¨μ΄λŠ” μž‘μ€ μƒμˆ˜λ‘œ λ°”μš΄λ“œ λ˜μ–΄μ„œ λΉ„λ‘€κ³΅μ •μ„±μ˜ μ΅œμ ν™”λ₯Ό λ‹¬μ„±ν•œλ‹€. λ³Έ μ—°κ΅¬μ—μ„œ μ œμ•ˆν•œ κΈ°λ²•λ“€μ˜ νš¨μš©μ„±μ„ ν•˜κΈ° μœ„ν•΄μ„œ, λ³Έ μ—°κ΅¬λŠ” 이듀이 λ‹¬μ„±ν•˜κ³ μž ν•˜λŠ” μ„±λŠ₯ μ§€ν‘œμ— 따라 μ„ λ³„λœ λŒ€μƒ μ‹œμŠ€ν…œμ— 각 기법듀을 κ΅¬ν˜„ν•˜μ˜€λ‹€. FTC와 VT-CFSλŠ” Android μŠ€λ§ˆνŠΈν°μ— κ΅¬ν˜„λ˜μ—ˆλ‹€. μ‹€ν—˜ κ²°κ³Ό, κΈ°μ‘΄ μ‹œμŠ€ν…œ λŒ€λΉ„ 무렀 77.35% 더 짧게 양단간 μ‚¬μš©μž λ°˜μ‘μ‹œκ°„μ„ λ‹¨μΆ•ν•¨μœΌλ‘œμ¨ μ‚¬μš©μž λ°˜μ‘μ„±μ˜ μ΅œμ ν™”λ₯Ό λ‹¬μ„±ν•˜μ˜€λ‹€. 이 μ΅œμ ν™” κ²°κ³ΌλŠ” FTCκ°€ 선점 μ§€μ—°μ‹œκ°„μ„ 80.23%, VT-CFSκ°€ μ‹€ν–‰ μ§€μ—°μ‹œκ°„μ„ 78.42% κ°μ†Œμ‹œμΌ°λ‹€λŠ” 사싀에 기인 ν•œλ‹€. λ˜ν•œ, Progress Balancing은 Linux 기반 μ„œλ²„μ— κ΅¬ν˜„λ˜μ—ˆλ‹€. μ‹€ν—˜ κ²°κ³Ό, λͺ¨λ“  νƒœμŠ€ν¬λ“€μ˜ virtual runtime의 차이가 μž‘μ€ μƒμˆ˜λ‘œ λ°”μš΄λ“œ 됨을 ν™•μΈν•˜μ˜€λ‹€. μ΄λŸ¬ν•œ virtual runtime 차이의 λ°”μš΄λ“œλŠ” μ„œλ²„ ν™˜κ²½μ—μ„œ ν˜ΈμŠ€νŒ… λ˜λŠ” μ‚¬μš©μžλ“€μ— λͺ¨λ‘ κ³΅μ •ν•œ CPU μžμ›μ„ 보μž₯ν•  수 μžˆμŒμ„ λœ»ν•œλ‹€. μ΄λŸ¬ν•œ κ²°κ³ΌλŠ” λ³Έ λ…Όλ¬Έμ—μ„œ μ œμ•ˆν•˜λŠ” ꡐ차계측 μžμ›κ΄€λ¦¬μ™€ μŠ€μΌ€μ€„λ§ 기법이 λŒ€μƒ μ‹œμŠ€ν…œμ— 효과적으둜 μ μš©λ˜μ–΄ 보닀 높은 μˆ˜μ€€μ˜ μ„±λŠ₯ μ΅œμ ν™”κ°€ κ°€λŠ₯함을 보이며, λ˜ν•œ κ³„μ†ν•΄μ„œ λ‹€κ°ν™”λ˜κ³  λ³΅μž‘ν™”λ˜λŠ” μ†Œν”„νŠΈμ›¨μ–΄ ν”Œλž«νΌμ˜ 전체둠적 μ„±λŠ₯ μ΅œμ ν™”λ₯Ό μœ„ν•œ μ‹€μš©μ μΈ μˆ˜λ‹¨μž„μ„ λ“œλŸ¬λ‚Έλ‹€.Layering software platform is known to be a promising mean to deal with many problems incurred by its colossal and complex structure. In a layered structure, a performance optimization is performed locally at each individual layer independently from other layers, and restricted to use information provided from its adjacent layers. This leads many difficulties and challenges in achieving holistic system-wide performance optimization. A cross-layer optimization is an optimization paradigm which is proposed to solve such difficulties in layered software structure. It carefully delivers relevant information, which affects overall performance to other appropriate layers. Its objective is to lead a holistic performance optimization by violating the layering principle to a certain degree. Recently, due to its effectiveness, there have been a number of demands and efforts to improve an overall performance using the cross-layer optimization. In particular, task scheduling in a layered software platform is one of the most crucial problems which can affect an overall performance improvement of the platform. This thesis proposes a cross-layer resource control and scheduling to solve two performance optimization problems in layered software platforms. First, we present a cross-layer resource control to optimize a user interactivity for embedded user equipment. The proposed one consists of two components. First, Framework-assisted Task Characterization (FTC) is a cross-layer scheme between Android application framework and its underlying operating systems kernel. At the upper level, it identifies user-interactive tasks using the notion of a user-interactive task chain. It then enables the lower level scheduler to selectively promote the priorities of tasks appearing in the task chain. This effectively reduces the preemption latency of a user-interactive task since it can occupy a CPU longer without being preempted. Second, Virtual Time-based Completely Fair Scheduler (VT-CFS) is a cross-layer refinement of CFS in terms of interactivity. Specifically, it schedules a task in a WFQ fashion and allows a task to be preempted at every smaller period. It also adjusts the virtual runtime of the user-interactive task identified by FTC to ensure that a dispatch latency of a user-interactive task is reduced to a small value. Second, we present Progress Balancing in order to optimize a multicore proportional fairness in a server. It defines tasks relative progress as virtual runtimes and periodically balances them. Specifically, it partitions runnable tasks such that a core with larger virtual runtimes receives a larger load and the load difference among cores is bounded. As a result, the virtual runtime difference converges to the balancing period multiplied by some constant derived from the bounded load difference. To show a viability and evaluate an effectiveness of the proposed schemes, we have applied them to the target software platform product line. We have implemented FTC and VT-CFS into Android smartphone and empirically evaluated them with well-known benchmark programs and real-world applications. Results show that the end-to-end response time was reduced by up to 77.35% compared to the legacy one. This improvement comes from the facts that the modified framework achieved 80.23% shorter preemption latency than the legacy framework and that the VT-CFS yielded 78.42% shorter dispatch latency for user-interactive tasks than CFS. We have also implemented Progress Balancing into a multicore Linux-installed server. Experimental results report that virtual runtime difference between tasks is bounded by a small constant by adopting our scheme. Such bounding virtual runtime differences implies that tenants hosted from our target server are able to guarantee a use of fair amount of CPU resources. As such qualitative and quantitative analyses indicate, our cross-layer resource control and scheduling schemes effectively improve the overall system performance of the existing software platforms.제 1 μž₯ μ„œλ‘  1 제 1 절 연ꡬ 동기 3 제 2 절 λ…Όλ¬Έμ˜ κΈ°μ—¬ 6 제 3 절 λ…Όλ¬Έ ꡬ성 10 제 2 μž₯ κ΄€λ ¨ 연ꡬ 11 제 1 절 ꡐ차계측 μ΅œμ ν™” 11 제 2 절 비둀곡정 μŠ€μΌ€μ€„λ§ 15 2.1 Weighted Round Robin 기반 기법듀 15 2.2 Weighted Fair Queueing 기반 기법듀 19 제 3 절 λ©€ν‹°μ½”μ–΄ 비둀곡정성 ν–₯상을 μœ„ν•œ μŠ€μΌ€μ€„λ§ 22 3.1 κ°€μ€‘μΉ˜(Weight) 기반 기법듀 23 3.2 속도(Speed) 기반 기법듀 25 제 3 μž₯ μ‹œμŠ€ν…œ λͺ¨λΈ 28 제 1 절 계측적 μ•„ν‚€ν…μ²˜ 28 제 2 절 Completely Fair Scheduler 31 제 4 μž₯ ꡐ차계측 μžμ›κ΄€λ¦¬λ₯Ό ν†΅ν•œ μ‚¬μš©μž λ°˜μ‘μ„± μ΅œμ ν™” 39 제 1 절 λŒ€μƒ μ‹œμŠ€ν…œ μ •μ˜ 40 제 2 절 μˆ˜ν•™μ  ν‘œκΈ°λ²•κ³Ό μš©μ–΄ μ •μ˜ 49 제 3 절 문제 μ •μ˜ 52 제 4 절 Framework-assisted Task Characterization 56 제 5 절 Virtual Time-based CFS 61 제 6 절 λ™μž‘ μ˜ˆμ‹œ 66 제 5 μž₯ Progress Balancing을 ν†΅ν•œ 비둀곡정성 μ΅œμ ν™” 68 제 1 절 λŒ€μƒ μ‹œμŠ€ν…œ μ •μ˜ 69 제 2 절 μˆ˜ν•™μ  ν‘œκΈ°λ²•κ³Ό μš©μ–΄ μ •μ˜ 72 제 3 절 문제 μ •μ˜ 75 제 4 절 Progress Balancing 80 4.1 PARTITION 단계 83 4.2 THROTTLE 단계 84 제 5 절 λ™μž‘ μ˜ˆμ‹œ 88 제 6 μž₯ μ‹€ν—˜ 및 검증 93 제 1 절 μ‹€ν—˜ ν™˜κ²½ 93 제 2 절 μ„±λŠ₯ μ§€ν‘œ 94 제 3 절 μ‹€ν—˜μ  검증 κ²°κ³Ό 97 3.1 μ‚¬μš©μž λ°˜μ‘μ„± μ΅œμ ν™” 98 3.2 비둀곡정성 μ΅œμ ν™” 105 제 4 절 뢄석적 평가 113 제 7 μž₯ κ²°λ‘  121 μ°Έκ³  λ¬Έν—Œ 124 Abstract 133Docto

    Virtual Runtime based Load Balancing Algorithm for Guaranteeing Fairness on Multicore Systems

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    Authors' final versionλ¦¬λˆ…μŠ€μ˜ κΈ°λ³Έ μŠ€μΌ€μ€„λŸ¬μΈ CFSλŠ” ν΄λΌμš°λ“œ μ„œλ²„μ™€ 같은 λŒ€κ·œλͺ¨ μ‹œμŠ€ν…œμ—μ„œ 널리 μ‚¬μš©λ˜κ³  μžˆμ§€λ§Œ, μ΄λŠ” μ‹œμŠ€ν…œμ˜ 규λͺ¨κ°€ κ·Ήλ„λ‘œ 컀짐에 따라 μ‹œμŠ€ν…œμ΄ μš”κ΅¬ν•˜λŠ” μˆ˜μ€€μ˜ 곡정성을 보μž₯ν•˜μ§€ λͺ»ν•œλ‹€. 이 논문은 CFS에 λŒ€ν•œ 심도 κΉŠμ€ 뢄석을 톡해 곡정성 보μž₯의 μ‹€νŒ¨μ˜ 원인을 규λͺ…ν•˜κ³ , 이λ₯Ό ν•΄κ²°ν•˜κΈ° μœ„ν•œ virtual runtime 기반의 λ‘œλ“œ λ°ΈλŸ°μ‹± μ•Œκ³ λ¦¬μ¦˜μ„ μ œμ•ˆν•œλ‹€. μ΄λŠ” νƒœμŠ€ν¬λ“€μ˜ virtual runtime 차이λ₯Ό λ°”μš΄λ“œμ‹œν‚€κΈ° μœ„ν•΄ 주기적으둜 νƒœμŠ€ν¬ 이주λ₯Ό μˆ˜ν–‰ν•œλ‹€. 이λ₯Ό μœ„ν•΄ μ•Œκ³ λ¦¬μ¦˜μ€ μΈμ ‘ν•œ 두 CPU의 load 차이가 μ΅œλŒ€ κ°€μ€‘μΉ˜ 차이 μ΄ν•˜κ°€ λ˜λ„λ‘ λ°”μš΄λ“œ ν•˜κ³ , virtual runtime이 큰 νƒœμŠ€ν¬λ“€μ„ loadκ°€ 큰 CPUμ—μ„œ μˆ˜ν–‰λ˜κ²Œ 보μž₯ν•œλ‹€. μš°λ¦¬λŠ” μ œμ•ˆλœ μ•Œκ³ λ¦¬μ¦˜μ„ λ¦¬λˆ…μŠ€ 컀널 2.6.38.8 상에 κ΅¬ν˜„ν•˜κ³  일련의 μ‹€ν—˜μ„ μˆ˜ν–‰ν•˜μ˜€λ‹€. κ·Έ κ²°κ³Ό κΈ°μ‘΄ CFS의 경우 virtual runtime μ°¨μ΄λŠ” μ„ ν˜•μ μœΌλ‘œ μ¦κ°€ν•˜λŠ”λ° λ°˜ν•΄ μ œμ•ˆλœ 기법은 50.53 λ‹¨μœ„ μ‹œκ°„μœΌλ‘œ virtual runtime 차이λ₯Ό λ°”μš΄λ“œμ‹œν‚¬ 수 있으며 κ³ μž‘ 0.14%의 λŸ°νƒ€μž„ μ˜€λ²„ν—€λ“œλ₯Ό μ•ΌκΈ°μ‹œν‚΄μ„ λ³΄μ˜€λ‹€.While the primary task scheduler of Linux, CFS, is widely adopted for the large-scale cloud system, it cannot provide a desired level of fairness when a system scales up to an extreme degree. This paper formally analyzes the behavior of CFS to precisely characterize the reason why it fails to achieve the fairness in multicore systems. Based on the analysis, we present a virtual runtime-based load balancing algorithm which directly bounds the maximum virtual runtime difference among tasks by periodically migrating tasks. In doing so, it bounds the load difference between two adjacent cores by the largest weight in the task set and makes the core with larger virtual runtimes receive a larger load and thus runs more slowly. We have implemented the algorithm into Linux kernel 2.6.38.8. Experimental results show that the maximal virtual runtime difference is 50.53 time units while incurring only 0.14% of run-time overhead comparing to CFS.OAIID:oai:osos.snu.ac.kr:snu2012-01/102/0000004193/2SEQ:2PERF_CD:SNU2012-01EVAL_ITEM_CD:102USER_ID:0000004193ADJUST_YN:YEMP_ID:A005174DEPT_CD:4541CITE_RATE:0FILENAME:(2011좔계 μš°μˆ˜λ°œν‘œλ…Όλ¬Έ) 11-12-12 μ •λ³΄κ³Όν•™νšŒ 논문지 CPL-ν—ˆμŠΉμ£Ό - μ΅œμ’…λ³Έ.pdfDEPT_NM:전기·컴퓨터곡학뢀EMAIL:[email protected]_YN:NCONFIRM:

    DVFS에 κΈ°λ°˜ν•œ μ•ˆλ“œλ‘œμ΄λ“œ 슀마트폰의 CPU μ†Œλͺ¨ μ „λ ₯ 절감 κΈ°λ²•μ˜ ν•œκ³„

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    Authors' final manuscriptOAIID:oai:osos.snu.ac.kr:snu2012-01/102/0000004193/6SEQ:6PERF_CD:SNU2012-01EVAL_ITEM_CD:102USER_ID:0000004193ADJUST_YN:NEMP_ID:A005174DEPT_CD:4541CITE_RATE:0FILENAME:12_μ •λ³΄κ³Όν•™νšŒμ§€_μ €μ „λ ₯.pdfDEPT_NM:전기·컴퓨터곡학뢀EMAIL:[email protected]_YN:NCONFIRM:

    λ©€ν‹°μ½”μ–΄ μ‹œμŠ€ν…œμ—μ„œ 곡정성 ν–₯상을 μœ„ν•œ Virtual Runtime 기반 λ‘œλ“œ λ°ΈλŸ°μ‹± λ©”μ»€λ‹ˆμ¦˜

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    OAIID:oai:osos.snu.ac.kr:snu2011-01/102/0000004193/4SEQ:4PERF_CD:SNU2011-01EVAL_ITEM_CD:102USER_ID:0000004193ADJUST_YN:NEMP_ID:A005174DEPT_CD:4541CITE_RATE:0FILENAME:11-09-07 μ •λ³΄κ³Όν•™νšŒ - CFS λ‘œλ“œ λ°ΈλŸ°μ‹±_좜판용.pdfDEPT_NM:전기·컴퓨터곡학뢀EMAIL:[email protected]:

    Framework-assisted Priority Boosting and Load Balancing for Improving Interactivity of Android Smartphones

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    Authors' final versionλ³Έ 논문은 KCC2012μ—μ„œ μ•ˆλ“œλ‘œμ΄λ“œ 기반 슀마트폰의 μ‚¬μš©μž 응닡성 ν–₯상을 μœ„ν•œ ν”„λ ˆμž„μ›Œν¬ 지원 μš°μ„ μˆœμœ„ λΆ€μŠ€νŠΈ κΈ°λ²•μ˜ 제λͺ©μœΌλ‘œ λ°œν‘œλœ 논문을 ν™•μž₯ν•œ κ²ƒμž„μ΅œκ·Ό μ•ˆλ“œλ‘œμ΄λ“œ ν”Œλž«νΌμ„ νƒ‘μž¬ν•œ 슀마트폰이 널리 λ³΄κΈ‰λ˜λ©΄μ„œ μ•ˆλ“œλ‘œμ΄λ“œ ν”Œλž«νΌμ— λŒ€ν•œ 관심은 λ”μš± 컀지고 μžˆλ‹€. ν•˜μ§€λ§Œ μ•ˆλ“œλ‘œμ΄λ“œ μŠ€λ§ˆνŠΈν°μ€ μ’…μ’… μ–‘μ§ˆμ˜ μ‚¬μš©μž 응닡성을 μ œκ³΅ν•˜μ§€ λͺ»ν•˜λŠ” κ²ƒμœΌλ‘œ μ•Œλ €μ Έ μžˆλ‹€. μ΄λŠ” μ•ˆλ“œλ‘œμ΄λ“œ μƒμ—μ„œ λŒ€ν™”ν˜• νƒœμŠ€ν¬κ°€ λ‹€λ₯Έ νƒœμŠ€ν¬μ™€ κ΅¬λ³„λ˜μ§€ μ•Šκ³  λ™μΌν•œ μš°μ„ μˆœμœ„λ‘œ μŠ€μΌ€μ€„λ§ 되기 λ•Œλ¬Έμ— μ‚¬μš©μž μž…λ ₯을 μ²˜λ¦¬ν•˜λŠ” λ™μ•ˆ μ—¬λŸ¬ 번의 선점을 λ‹Ήν•΄ κΈ΄ μ‘λ‹΅μ‹œκ°„μ„ μ΄ˆλž˜ν•  수 있기 λ•Œλ¬Έμ΄λ‹€. 이 논문은 μ•ˆλ“œλ‘œμ΄λ“œ 슀마트폰의 μ‚¬μš©μž 응닡성 ν–₯상을 μœ„ν•΄ ν”„λ ˆμž„μ›Œν¬ 지원 μš°μ„ μˆœμœ„ λΆ€μŠ€νŠΈ 기법과 λ‘œλ“œ λ°ΈλŸ°μ‹± 기법을 μ œμ‹œν•œλ‹€. ν”„λ ˆμž„μ›Œν¬ 지원 μš°μ„ μˆœμœ„κΈ°λ²•μ€ ν”„λ ˆμž„μ›Œν¬ λ ˆλ²¨μ—μ„œ λŒ€ν™”ν˜• νƒœμŠ€ν¬λ₯Ό μ‹λ³„ν•˜κ³  이λ₯Ό μ»€λ„μ—κ²Œ μ „λ‹¬ν•˜λ©°, 컀널 λ ˆλ²¨μ—μ„œλŠ” μ‹λ³„λœ νƒœμŠ€ν¬μ˜ μš°μ„ μˆœμœ„λ₯Ό μ„ λ³„μ μœΌλ‘œ λΆ€μŠ€νŠΈ μ‹œν‚΄μœΌλ‘œμ¨ μ‚¬μš©μž μž…λ ₯을 μ²˜λ¦¬ν•  만큼 μΆ©λΆ„ν•œ μ‹œκ°„μ„ 보μž₯ν•΄ μ€€λ‹€. λ‘œλ“œ λ°ΈλŸ°μ‹± 기법은 λΆ€μŠ€νŠΈ 된 νƒœμŠ€ν¬λ₯Ό μ—¬μ „νžˆ λ°©ν•΄ν•˜λŠ” νƒœμŠ€ν¬λ“€μ„ λ‹€λ₯Έ μ‹€ν–‰ 큐둜 μ΄μ£Όμ‹œν‚΄μœΌλ‘œμ¨, λŒ€ν™”ν˜• νƒœμŠ€ν¬μ˜ μ‘λ‹΅μ‹œκ°„μ„ μ΅œμ†Œν™” ν•œλ‹€. μ‹€ν—˜ κ²°κ³Ό λŒ€ν™”ν˜• νƒœμŠ€ν¬μ˜ μ‘λ‹΅μ‹œκ°„μ΄ μš°μ„ μˆœμœ„ λΆ€μŠ€νŠΈ 기법을 ν†΅ν•΄μ„œλŠ” κΈ°μ‘΄ μ‹œμŠ€ν…œλ³΄λ‹€ μ΅œλŒ€ 22% 단좕됨을 λ³΄μ˜€κ³  λ‘œλ“œ λ°ΈλŸ°μ‹± 기법을 ν†΅ν•΄μ„œλŠ” μ΅œλŒ€ 43.31% 단좕됨을 보여 μ œμ•ˆλœ κΈ°λ²•μ˜ νš¨μš©μ„±μ„ μž…μ¦ν•˜μ˜€λ‹€.Smartphones on Android platform recently have been come into wide use. However, it is often reported that Android smartphones cannot provide enough interactivity because Android cannot distinguish interactive tasks and non-interactive tasks and they are scheduled with the same priority and preempted. Thus, it occurs poor response time. This paper proposes a framework assisted priority boosting and load balancing for improving interactivity of Android smartphones. The framework assisted priority boosting technique distinguishes the interactive task in the framework level and send the task ID to the kernel. The kernel ensures enough time to process user input by boosting the priority of distinguished task. The load balancing technique minimizes response time of boosted task by migrating tasks disturbing boosted task to other run-queue. The experiment results demonstrate the priority boosting technique reduces response time up to 22% and the load balancing technique along with priority boosting reduces response time up to 43.31% compared to the previous techniques.OAIID:oai:osos.snu.ac.kr:snu2012-01/102/0000004193/8SEQ:8PERF_CD:SNU2012-01EVAL_ITEM_CD:102USER_ID:0000004193ADJUST_YN:YEMP_ID:A005174DEPT_CD:4541CITE_RATE:0FILENAME:(CSTV12-07-09-15).pdfDEPT_NM:전기·컴퓨터곡학뢀EMAIL:[email protected]_YN:NCONFIRM:

    μ•ˆλ“œλ‘œμ΄λ“œ 기반 슀마트폰의 μ‚¬μš©μž 응닡성 ν–₯상을 μœ„ν•œ ν”„λ ˆμž„μ›Œν¬ 지원 μš°μ„ μˆœμœ„ λΆ€μŠ€νŠΈ 기법

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    Authors' final versionOAIID:oai:osos.snu.ac.kr:snu2012-01/102/0000004193/9SEQ:9PERF_CD:SNU2012-01EVAL_ITEM_CD:102USER_ID:0000004193ADJUST_YN:NEMP_ID:A005174DEPT_CD:4541CITE_RATE:0FILENAME:12-06-27 μ •λ³΄κ³Όν•™νšŒ - Android_Interactivity.pdfDEPT_NM:전기·컴퓨터곡학뢀EMAIL:[email protected]:
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