Virtual Network Embedding in Fiber-Wireless Access Networks for Resource-Efficient IoT Service Provisioning

Abstract

The rise of Industry 4.0 is pushing more machine devices into the connection of the communication network. Internet of Things (IoT) emerges as an enabling technique to support automatic information transmission and the universal interconnection among massive machine devices. Unlike the human type communication (HTC) in the conventional Internet, IoT features machine type communication (HTC) with small data granularity and massive connections, which impose new challenges on the network resource allocation earlier designed for HTC communication. One of the challenging issues is how we can manage the network resource for the IoT service provisioning. As a promising network architecture for the ''lastmile'' access in 5G communication, Fiber-Wireless (FiWi) access network compromises the high bandwidth capacity of the optical access network and the flexibility of wireless access network. Especially, network virtualization as an emerging technique provides FiWi with the feasibility for the coordinated bandwidth allocation of wireless access network and optical access network. The evolution of FiWi access network should be not only oriented to the technological breakthrough in regular traffic, but also be supportive of the emerging IoT traffic. In this paper, we focus on the resource allocation problem in FiWi access network supporting IoT service. We first analyze the dynamics of regular traffic and propose the traffic prediction method based on Q-Learning. Then, we propose a virtual network embedding algorithm to map exactly the virtual networks of IoT traffic to the idle resource of the virtual networks of regular traffic without degrading the performance of regular traffic. Thus, the proposed algorithm can improve the resource utilization more effectively. The simulation results demonstrate that the proposed algorithm can achieve a higher acceptance ratio and lower migration ratio of IoT traffic.

SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models

“…To maximize resource utilization and minimize energy consumption in IoT devices, numerous resource allocation schemes such as joint bandwidth allocation have been proposed in [5]. In 2019, Y. Liu et al proposed the bandwidth provision problem in the FiWi network, supporting the IoT service based on the machine learning prediction method [4]. An integrated heterogeneous networking system for cloud computing and the virtualization of FiWi access networks is proposed in [15].…”

“…Fiber-wireless (FiWi) converged access networks are gaining more attention for their ability to handle this level of traffic in the ubiquitous community. The FiWi integrates the access network connects of the passive optical network (PON) and wireless local area network (WLAN) and takes advantage of both optical and wireless access [4]. Although optical networks can provide a wide bandwidth, it is not possible to deploy these networks everywhere because of their reliance on physical infrastructure and the associated deployment costs.…”

Cooperative Resource Management for Cognitive Satellite-Aerial-Terrestrial Integrated Networks Towards IoT

“…In terrestrial cellular networks, to improve the resource utilization as well as minimizing the energy consumption of IoT system, many resource allocation schemes have been proposed, such as bandwidth allocation [15], joint optimization of sub-carriers and transmit power [16], [17], and joint allocation of resource blocks and transmit power [18].…”

Network Resource Allocation Strategy Based on Deep Reinforcement Learning

“…In recent years, due to the rapid development of cloud computing services, the world's major Internet service providers in order to obtain greater profits, constantly update and improve the network architecture and service quality. Therefore, the application of DRL to solve the VNE problem has become a hot topic [27], [28]. Many researchers at home and abroad have also proposed many ML-based VNE algorithms.…”