An Application-aware QoS Routing Algorithm for SDN-based IoT Networking

With numerous emerging internet of things (IoT) devices, they generate big data. The big data transmitted to the cloud or fog will consume massive network bandwidth. This may result in the IoT network easily encountering network congestion. Moreover, there are IoT applications that need to transfer multimedia data with multiple quality of service (QoS) requirements. A state-of-the-art, MINA, intends to meet multiple QoS requirements of IoT applications; however, it is unable to guarantee QoS requirements of high-priority IoT applications and it is also unable to adapt to the current network status. To conquer the above problems, we propose an application-aware QoS routing algorithm (AQRA) for SDN-based IoT networking to guarantee multiple QoS requirements of high-priority IoT applications and to adapt to the current network status for better routing paths. Evaluation results have shown that, the AQRA has better fitness ratios of QoS requirements compared to MINA while multiple QoS requirements of high-priority IoT applications are guaranteed. The AQRA improves the average end-to-end flow performance by 10.75%, 11.88% and 10.82% compared to MINA in terms of delay, jitter and packet loss rate, respectively. The AQRA improves the standard deviation of end-to-end flow performance by 14.37%, 17.95% and 14.28% compared to MINA in terms of delay, jitter and packet loss rate, respectively. In addition, the runtime of the AQRA is 38.56% shorter than that of MINA.

[1]  Nalini Venkatasubramanian,et al.  A Software Defined Networking architecture for the Internet-of-Things , 2014, 2014 IEEE Network Operations and Management Symposium (NOMS).

[2]  Jon Crowcroft,et al.  Quality-of-Service Routing for Supporting Multimedia Applications , 1996, IEEE J. Sel. Areas Commun..

[3]  Igor Radusinovic,et al.  Software-Defined Fog Network Architecture for IoT , 2016, Wireless Personal Communications.

[4]  Nirwan Ansari,et al.  EdgeIoT: Mobile Edge Computing for the Internet of Things , 2016, IEEE Communications Magazine.

[5]  Mahmoud Al-Ayyoub,et al.  SDIoT: a software defined based internet of things framework , 2015, Journal of Ambient Intelligence and Humanized Computing.

[6]  Geoffrey D. Rubin,et al.  Learning-enhanced simulated annealing: method, evaluation, and application to lung nodule registration , 2008, Applied Intelligence.

[7]  Antonio Pescapè,et al.  A tool for the generation of realistic network workload for emerging networking scenarios , 2012, Comput. Networks.

[8]  Fouad A. Tobagi,et al.  Analysis of delay and delay jitter of voice traffic in the Internet , 2002, Comput. Networks.

[9]  Hung-Chin Jang,et al.  Design a bandwidth allocation framework for SDN based smart home , 2016, 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON).

[10]  Peter Bull,et al.  Pre-emptive Flow Installation for Internet of Things Devices within Software Defined Networks , 2015, 2015 3rd International Conference on Future Internet of Things and Cloud.

[11]  Antonio Pescapè,et al.  Challenges and solution for measuring available bandwidth in software defined networks , 2017, Comput. Commun..

[12]  Mario Marchese,et al.  Q o S OVER HETEROGENEOUS NETWORKS , 2007 .

[13]  Christian Esteve Rothenberg,et al.  Mininet-WiFi: Emulating software-defined wireless networks , 2015, 2015 11th International Conference on Network and Service Management (CNSM).

[14]  Aniruddha S. Gokhale,et al.  Publish/subscribe-enabled software defined networking for efficient and scalable IoT communications , 2015, IEEE Communications Magazine.

[15]  Mirjami Jutila,et al.  An Adaptive Edge Router Enabling Internet of Things , 2016, IEEE Internet of Things Journal.

[16]  A P Adewole,et al.  A Comparative Study of Simulated Annealing and Genetic Algorithm for Solving the Travelling Salesman Problem , 2012 .