QoS-Based Multicast Routing in Network Function Virtualization-Enabled Software-Defined Mobile Edge Computing Networks

Abstract

Mobile Edge Computing (MEC) technology brings the unprecedented computing capacity to the edge of mobile network. It provides the cloud and end user swift high-quality services with seamless integration of mobile network and Internet. With powerful capability, virtualized network functions can be allocated to MEC. In this paper, we study QoS guaranteed multicasting routing with Network Function Virtualization (NFV) in MEC. Specifically, data should pass through a service function chain before reaching destinations along a multicast tree with minimal computational cost and meeting QoS requirements. Furthermore, to overcome the problems of traditional IP multicast and software-defined multicasting approaches, we propose an implementable multicast mechanism that delivers data along multicast tree but uses unicast sessions. We finally evaluate the performance of the proposed mechanism based on experimental simulations. The results show that our mechanism outperforms others reported in the literature.

Laboratory Virtualization Management Based on Deep Integration of Cloud Desktop

“…The infrastructure cluster server consists of 2 H3C blades configured with 2-way 2.0 GHz CPU, 256 G memory, and 1 dual-port 20 G converged network card, providing a computing environment for Microsoft RDS infrastructure servers; the running infrastructure includes active directory (active directory), RD connection broker (controller), RD Web (authorized access), RD license (authorization), RD gateway (gateway), file server, database server, and other virtual machine servers. The virtual desktop hosting cluster server consists of 30 H3C blades configured with 2-way 2.0 GHz CPU, 256 G memory, and 1 dual-port 20 G converged network card and hosts a virtual desktop pool with a total scale of 800 users [17].…”

QoS based Optimal Path Selection Using Adaptive Sparse Bayesian Find-FixFinish-Exploit-Analyze Based Extreme Learning Machine for SDN based IoT Networks

“…The necessity of QoS is very high in the form of differentiable data flows and so, the procedure of efficient classification is performed in SDN [23]. The differentiable data flows in SDN network are not efficient to choose an optimal path to acquire QoS [24]. When the network link is in heavy traffic, the IoT services are influenced.…”