Please use this identifier to cite or link to this item:
Performance study on QoS aware scheduling with SDN for smart homes
Wang, Chin Yin
Jang, Hung Chin
Wang, Chin Yin
Software defined networking (SDN)
Internet of things (IoT)
|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) 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)  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%.
|Reference:|| An OpenFlow Extension for the OMNeT++ INET Framework, https://github.com/lsinfo3/ofomnet, retrieved date: 7/1/2017.|
 R. Basukala, H.A. Mohd Ramli and K. Sandrasegaran, “Performance Analysis of EXP/PF and M-LWDF in Downlink 3GPP LTE System,” Proc. 1st Asian Himalayas International Conference on Internet (AH-ICI’09), Aug. 2009, pp. 1-5.
 Colin Dixon, Ratul Mahajan, Sharad Agarwal, et al., “An Operating System for the Home,” 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI’12), Apr. 25-17, 2012, pp. 337-352.
 J. M. Holtzman, QUALCOMM Inc., “Asymptotic Analysis of Proportional Fair Algorithm,” Personal, Indoor and Mobile Radio Communications (IEEE PIMRC), Aug. 2001, pp. 33-37.
 INET Framework, https://inet.omnetpp.org/, retrieved date: 7/1/2017.
 Hung-Chin Jang and Chien-Piao Hu, “Fairness-Based Adaptive QoS Scheduling for LTE,” International Conference on ICT Convergence 2013 (ICTC 2013), Jeju Island, Korea, Oct. 14-16, 2013.
 Hung-Chin Jang, Chi-Wei Huang, Fu-Ku Yeh, "Design a Bandwidth Allocation Framework for SDN Based Smart Home," Proc. 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 1-6 2016.
 Younggi Kim and Younghee Lee, “Automatic Generation of Social Relationships between Internet of Things in Smart Home using SDN-based Home Cloud,” Proc. 29th International Conference on Advanced Information Networking and Applications Workshops (WAINA), Gwangiu, South Korea, March 24-27, 2015, pp. 662-667.
 D. Klein and M. Jarschel, “An OpenFlow Extension for the OMNeT++ INET Framework,” Proc. 6th International ICST Conference on Simulation Tools and Techniques, 2013, pp. 322-329.
 Hoon-Ki Lee, Noh-Sam Park, Jong-Hyun Jang and Hyeon-Soo Kim, "Providing of SoT Collaboration System for Interworking with Smart Home Devices," IEEE International Conference on Consumer Electronics (ICCE), 2015.
 Shuangquan Li, Jian Li, Xinxin Nie and Lingyong Kong, “Design and Implementation of Smart Home Based on Android,” Proc. 4th International Conference on Advanced Information Technology and Sensor Application (AITS), Harbin, China, Aug 21-23, 2015, pp. 32-35.
 Xue Li, Lanshun Nie, Shuo Chen, Dechen Zhan and Xiaofei Xu, “An IoT Service Framework for Smart Home: Case Study on HEM,” Proc. IEEE International Conference on Mobile Services, 2015, pp. 438-445.
 D. Macagnano, G. Destino and G. Abreu, “Indoor Positioning: A Key Enabling Technology for IoT applications,” Proc. IEEE World Forum on Internet of Things (WF-IoT), Seoul, Korea, Mar. 6–8, 2014, pp. 117-118.
 N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peterson, J. Rexford, S. Shenker and J. Turner, "OpenFlow: Enabling Innovation in Campus Networks," SIGCOMM Comput, 2008, pp. 69-74.
 OMNeT++, http://www.omnetpp.org, retrieved date: 7/1/2017.
 OMNeT++ 4.6, https://omnetpp.org/9-articles/software/3724-omnet-4-6-released, retrieved date: 7/1/2017.
 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.
 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.
 K. Sahoo, B. Sahoo and A. Panda, “A Secured SDN Framework for IoT,” International Conference on Man and Machine Interfacing (MAMI), 2015, pp. 1-4.
 M. Schurgot, D. Shinberg and L. Greenwald, “Experiments with Security and Privacy in IoT Networks,” 16th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), June, 2015, pp. 1-6.
 S. Schwarz, C. Mehlfuhrer and M. Rupp, “Low Complexity Approximate Maximum Throughput Scheduling for LTE,” 44th Annual Asilomar Conference on Signals, Systems and Computers, California, USA, 2010.
 SDN Three Tier Architecture, https://www.sdxcentral.com/sdn/definitions/inside-sdn-architecture/, retrieved date: 9/15/2017.
 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.
 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.
 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.
 Wikipedia, IEEE P802.1p, https://en.wikipedia.org/wiki/IEEE_P802.1p, retrieved date: 7/1/2017.
 Wikipedia, Proportionally Fair, https://en.wikipedia.org/wiki/Proportionally_fair, retrieved date: 7/1/2017.
 D. Wu et al., “UbiFlow: Mobility Management in Urban-Scale Software Defined IoT,” Proc. INFOCOM, 2015, pp. 208-16.
|Appears in Collections:||[資訊科學系碩士在職專班] 學位論文|
Files in This Item:
All items in 學術集成 are protected by copyright, with all rights reserved.