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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/115482

    Title: 智慧家庭中以SDN結合具服務品質感知排程演算法之效能研究
    Performance study on QoS aware scheduling with SDN for smart homes
    Authors: 王芝吟
    Wang, Chin Yin
    Contributors: 張宏慶
    Jang, Hung Chin
    Wang, Chin Yin
    Keywords: 智慧家庭
    Smart home
    Software defined networking (SDN)
    Internet of things (IoT)
    Resource allocation
    Scheduling algorithm
    Date: 2017
    Issue Date: 2018-01-03 16:24:19 (UTC+8)
    Abstract: 隨著物聯網這個萬物連網的概念順勢推動智慧家庭在市場裡蓬勃發展,可預期未來ISP(Internet Service Provider)業者勢必面臨大量智慧家庭中各種不同應用服務互相競爭頻寬資源的情況,甚至遇到網路滿載壅塞時造成應用服務不堪使用的情形。
    為改善上述問題,本文以ISP業者管理智慧家庭中眾多的物聯網設備為情境,透過軟體定義網路 (Software Defined Network,SDN)進行頻寬排程配置,排程演算法以可兼顧公平性(fairness)、時間延遲(delay)及應用服務優先權(service priority)的A-MLWDF (Adaptive Modified Largest Weighted Delay First) [7]演算法,確保優先配置頻寬給智慧家庭中優先權較高、時效較為急迫的流量,以降低應用服務的延遲來提升智慧家庭網路之服務品質(Quality of Service,QoS)。
    本研究透過OMNet++模擬器建構SDN環境與傳統環境中有眾多物聯網設備之智慧家庭。家中物聯網設備包含M2M (Machine to Machine)和非M2M(non Machine to Machine)裝置,以提供各種智慧家庭應用服務。我們透過SDN架構進行頻寬配置,達到集中式管控家中的頻寬資源,其中排程演算法包括PF、MLWDF、A-MLWDF。實驗結果顯示,以上排程演算法雖然於SDN環境下在公平性與抖動率表現並不顯著,公平性約改善1.6%及抖動率約降低1%左右,但在產能與延遲方面表現較為顯著,能有效提高產能約52%,及降低延遲約 52%。
    With the concept of IoT (Internet of Things) spread rapidly, it is the opportunity to promote smart homes in the expanding market. We can see that the future ISP (Internet Service Provider) has to face a large number of smart homes having bandwidth competition in a variety of different applications and causing application services unavailable due to network congestion.
        In order to resolve the above problems, we propose that each ISP (Internet Service Provider) has to manage a large number of IoT devices in a smart home to performs bandwidth scheduling through Software Defined Network (SDN). We choose to use A-MLWDF scheduling algorithm (Adaptive Modified Largest Weighted Delay First) [7] which considers fairness, delay and service priority. A-MLWDF is able to ensure services of higher priority and emergent traffic be allocated bandwidth earlier and greatly reduce delay and thus effectively enhance Quality of Service (QoS) of smart homes.
        In this research, we implement a SDN environment by using OMNet++ to simulate the bandwidth competition among smart homes with IoT devices. The IoT devices consists of M2M (Machine to Machine) and non-M2M (non Machine to Machine) devices which offer a variety of intelligent home application services. We configure the bandwidth allocation under SDN control. The scheduling algorithms include PF, MLWDF and A-MLWDF. When the network traffic is congested, SDN can significantly increase throughput and reduce latency compared to traditional network management. The experimental results show that above scheduling algorithms using SDN environment having no significant performance improvements in fairness and jitter. The fairness increases around 1.6% and the jitter reduces around 1%. However, it shows significant improvement on throughout and delay. The throughput increases around 52% and the delay reduces around 52%.
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    [17] OpenFlow Switch Specification, https://3vf60mmveq1g8vzn48q2o71a-wpengine.netdna-ssl.com/wp-content/uploads/2014/10/openflow-switch-v1.5.1.pdf, retrieved date: 9/28/2017.
    [18] Peter Rothenpieler, Bashar Altakrouri, Oliver Kleine and Lukas Ruge, “Distributed Crowd-sensing Infrastructure for Personalized Dynamic IoT Spaces,” Proc. First International Conference on IoT in Urban Space, Rome, Italy, Oct 27-28, 2014, pp. 90-92.
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    [22] SDN Three Tier Architecture, https://www.sdxcentral.com/sdn/definitions/inside-sdn-architecture/, retrieved date: 9/15/2017.
    [23] Pascal Thubert, Maria Rita Palattella and Thomas Engel, “6TiSCH Centralized Scheduling: When SDN meet IoT,” IEEE International Conference on Standards for Communications and Networking (CSCN), 2015, pp. 42-47.
    [24] A. Varga and R. Hornig, “An Overview of the OMNeT++ Simulation Environment,” Proc. 1st International Conference on Simulation Tools and Techniques for Communications, Networks and Systems & workshops (ICST), 2008.
    [25] Shiwei Wang, Xiaoling Wu, Hainan Chen, Yanwen Wang and Daiping Li, “An Optimal Slicing Strategy for SDN Based Smart Home Network,” Proc. International Conference on Smart Computing, 2014, pp. 118-122.
    [26] Wikipedia, IEEE P802.1p, https://en.wikipedia.org/wiki/IEEE_P802.1p, retrieved date: 7/1/2017.
    [27] Wikipedia, Proportionally Fair, https://en.wikipedia.org/wiki/Proportionally_fair, retrieved date: 7/1/2017.
    [28] D. Wu et al., “UbiFlow: Mobility Management in Urban-Scale Software Defined IoT,” Proc. INFOCOM, 2015, pp. 208-16.
    [29] 林建廷,以SDN為基礎之具服務品質感知的智慧家庭頻寬管理架構,國立政治大學資訊科學系碩士論文,2017.
    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0104971016
    Data Type: thesis
    Appears in Collections:[資訊科學系碩士在職專班] 學位論文

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