Name
Title | ||
|---|---|---|
An Energy-driven Network Function Virtualization for Multi-domain Software Defined Networks |
Abstract | ||
|---|---|---|
Network Functions Virtualization (NFV) in Software Defined Networks (SDN) emerged as a new technology for creating virtual instances for smooth execution of multiple applications. Their amalgamation provides flexible and programmable platforms to utilize the network resources for providing Quality of Service (QoS) to various applications. In SDN-enabled NFV setups, the underlying network services can be viewed as a series of virtual network functions (VNFs) and their optimal deployment on physical/virtual nodes is considered a challenging task to perform. However, SDNs have evolved from single-domain to multi-domain setups in the recent era. Thus, the complexity of the underlying VNF deployment problem in multi-domain setups has increased manifold. Moreover, the energy utilization aspect is relatively unexplored with respect to an optimal mapping of VNFs across multiple SDN domains. Hence, in this work, the VNF deployment problem in multi-domain SDN setup has been addressed with a primary emphasis on reducing the overall energy consumption for deploying the maximum number of VNFs with guaranteed QoS. The problem in hand is initially formulated as a “Multi-objective Optimization Problem” based on Integer Linear Programming (ILP) to obtain an optimal solution. However, the formulated ILP becomes complex to solve with an increasing number of decision variables and constraints with an increase in the size of the network. Thus, we leverage the benefits of the popular evolutionary optimization algorithms to solve the problem under consideration. In order to deduce the most appropriate evolutionary optimization algorithm to solve the considered problem, it is subjected to different variants of evolutionary algorithms on the widely used MOEA framework (an open source java framework based on multi-objective evolutionary algorithms). The experimental results demonstrate that the proposed scheme achieves better results in comparison to the e-Nen-dominated Sorting Genetic Algorithm (NSGA)-II (ϵ-NSGA-II) with the respect to the overall energy consumption and optimal deployment of VNFs in multi-domain SDN scenarios. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/INFCOMW.2019.8845314 | IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) |
Keywords | Field | DocType |
Network Function Virtualization,Software Defined Network,Evolutionary Optimization,Energy Consumption,Multi-objective Optimization | Virtual network,Software deployment,Evolutionary algorithm,Computer science,Computer network,Quality of service,Integer programming,Software-defined networking,Energy consumption,Optimization problem,Distributed computing | Journal |
Volume | ISSN | ISBN |
abs/1903.09924 | 2159-4228 | 978-1-7281-1879-6 |
Citations | PageRank | References |
1 | 0.35 | 17 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kuljeet Kaur | 1 | 195 | 19.59 |
| Sahil Garg | 2 | 267 | 40.16 |
| Georges Kaddoum | 3 | 874 | 94.42 |
| Francois Gagnon | 4 | 131 | 33.07 |
| Neeraj Kumar | 5 | 2889 | 236.13 |
| Syed Hassan Ahmed | 6 | 5 | 0.74 |