Load-Balanced and QoS-Aware Software-Defined Internet of Things

• Published in: IEEE internet of things journal Vol. 7; no. 4; pp. 3323 - 3337
• Main Authors: Montazerolghaem, Ahmadreza, Yaghmaee, Mohammad Hossein
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bandwidth; Computer Science; Delays; Floodlighting; Fuzzy logic; Integer programming; Internet of Things; Internet of Things (IoT) softwarization; large-scale IoT network; Linear programming; Load balancing; Load management; Networking and Internet Architecture; Overloading; Performance evaluation; Quality of service; Quality of service architectures; Servers; Software-defined networking; software-defined networking (SDN); Time series; Time series analysis; Traffic capacity; Fuzzy logic; Software-defined networking; Load balancing; Large-scale IoT network; IoT softwarization
• ISSN: 2327-4662; 2372-2541; 2327-4662
• DOI: 10.1109/JIOT.2020.2967081

Internet of Things (IoT) offers a variety of solutions to control industrial environments. The new generation of IoT consists of millions of machines generating huge traffic volumes; this challenges the network in achieving the Quality-of-Service (QoS) and avoiding overload. Diverse classes of applications in IoT are subject to specific QoS treatments. In addition, traffic should be distributed among IoT servers based on their available capacity. In this article, we propose a novel framework based on software-defined networking (SDN) to fulfill the QoS requirements of various IoT services and to balance traffic between IoT servers simultaneously. At first, the problem is formulated as an integer linear programming (ILP) model that is NP-hard. Then, a predictive and proactive heuristic mechanism based on time-series analysis and fuzzy logic is proposed. Afterward, the proposed framework is implemented in a real testbed, which consists of the Open vSwitch, Floodlight controller, and Kaa servers. To evaluate the performance, various experiments are conducted under different scenarios. The results indicate the improved IoT QoS parameters, including throughput and delay, and illustrate the nonoccurrence of overload on IoT servers in heavy traffic. Furthermore, the results show improved performance compared to similar methods.