Energy Efficiency Opposition-Based Learning and Brain Storm Optimization for VNF-SC Deployment in IoT

Network Function Virtualization (NFV) can provide the resource according to the request and can improve the flexibility of the network. It has become the key technology of the Internet of Things (IoT). Resource scheduling for the virtual network function service chain (VNF-SC) is the key issue of th...

Full description

Saved in:
Bibliographic Details
Published in:Wireless communications and mobile computing 2021, Vol.2021 (1)
Main Authors: Xuan, Hejun, Zhao, Xuelin, Liu, Zhenghui, Fan, Jianwei, Li, Yanling
Format: Article
Language:English
Subjects:
Algorithms
Bandwidths
Decision making
Energy consumption
Heterogeneity
Internet of Things
Learning
Optimization
Resource scheduling
Spectrum allocation
Virtual networks
Wireless networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Network Function Virtualization (NFV) can provide the resource according to the request and can improve the flexibility of the network. It has become the key technology of the Internet of Things (IoT). Resource scheduling for the virtual network function service chain (VNF-SC) is the key issue of the NFV. Energy consumption is an important indicator for the IoT; we take the energy consumption into the objective and define a novel objective to satisfying different objectives of the decision-maker. Due to the complexity of VNF-SC deployment problem, through taking into consideration of the heterogeneity of nodes (each node only can provide some specific VNFs), and the limitation of resources in each node, a novel optimal model is constructed to define the problem of VNF-SC deployment problem. To solve the optimization model effectively, a weighted center opposition-based learning is introduced to brainstorm optimization to find the optimal solution (OBLBSO). To show the efficiency of the proposed algorithm, numerous of simulation experiments have been conducted. Experimental results indicate that OBLBSO can improve the accuracy of the solution than compared algorithm.
ISSN:1530-8669
1530-8677
DOI:10.1155/2021/6651112