• DOI:10.1109/jiot.2021.3095237
• Corpus ID: 237801052

Software-Defined Internet of Multimedia Things: Energy-Efficient and Load-Balanced Resource Management

@article{Montazerolghaem2022SoftwareDefinedIO,
  title={Software-Defined Internet of Multimedia Things: Energy-Efficient and Load-Balanced Resource Management},
  author={Ahmadreza Montazerolghaem},
  journal={IEEE Internet of Things Journal},
  year={2022},
  volume={9},
  pages={2432-2442}
}

• Ahmadreza Montazerolghaem
• Published 6 July 2021
• Computer Science
• IEEE Internet of Things Journal

Internet of Multimedia Things (IoMT) is becoming attractive day by day and provides more services to the Internet users. The ever-increasing multimedia applications and services led to an outburst in IoMT. The multimedia things connected to IoMT are also increasing; millions of devices and high volume of traffic. This large traffic is directed toward the servers of the service provider in the IoMT cloud through the network switches. As a result, the IoMT infrastructure is facing the crisis of the resource management of switches and servers from two aspects: 1) load imbalance and 2) energy loss. This article proves that the problem of optimal resource management of IoMT networks with the energy and load constraints simultaneously is an NP-hard problem that has a high time complexity. The problem has decomposed. Then, we propose a modular system of energy and load control in IoMT using the concepts of network softwarization and virtual resources. The proposed controller first dynamically adjusts the resources through accurate determination of the IoMT network size. It then distributes the load between the IoMT servers as well as routes the traffic between switches to the desired server. The Open vSwitch, Floodlight Controller, and Kaa Servers are, respectively, used for implementing the switches, controller, and servers of IoMT in the test platform. The results show that the proposed system both minimizes the number of IoMT active servers and switches and distributes the load between them. As a result, the parameters for evaluating the quality of service and quality of experience, such as throughput, multimedia delay, R factor, and mean opinion score improved.