Optimizing NFV placement for distributing micro-data centers in cellular networks

被引:5
作者
de Freitas Bezerra, Diego [1 ]
Santos, Guto Leoni [1 ]
Goncalves, Glauco [2 ]
Moreira, Andre [1 ]
da Silva, Leylane Graziele Ferreira [1 ]
da Silva Rocha, Elisson [1 ]
Marquezini, Maria Valeria [3 ]
Kelner, Judith [1 ]
Sadok, Djamel [1 ]
Mehta, Amardeep [4 ]
Wildeman, Mattias [4 ]
Endo, Patricia Takako [5 ]
机构
[1] Univ Fed Pernambuco UFPE, Recife, PE, Brazil
[2] Univ Fed Rural Pernambuco UFRPE, Recife, PE, Brazil
[3] Ericsson Res, Sao Paulo, Brazil
[4] Ericsson Res, Stockholm, Sweden
[5] Univ Pernambuco UPE, Recife, PE, Brazil
关键词
Distributed data centers; NFV; Cellular networks; Optimization algorithms; Multi-objective optimization; MULTIOBJECTIVE OPTIMIZATION; NSGA-II; AVAILABILITY; DESIGN; COST; SYSTEM;
D O I
10.1007/s11227-021-03620-y
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
With the popularity of mobile devices, the next generation of mobile networks has faced several challenges. Different applications have been emerged, with different requirements. Offering an infrastructure that meets different types of applications with specific requirements is one of these issues. In addition, due to user mobility, the traffic generated by the mobile devices in a specific location is not constant, making it difficult to reach the optimal resource allocation. In this context, network function virtualization (NFV) can be used to deploy the telecommunication stacks as virtual functions running on commodity hardware to meet users' requirements such as performance and availability. However, the deployment of virtual functions can be a complex task. To select the best placement strategy that reduces the resource usage, at the same time keeps the performance and availability of network functions is a complex task, already proven to be an NP-hard problem. Therefore, in this paper, we formulate the NFV placement as a multi-objective problem, where the risk associated with the placement and energy consumption are taken into consideration. We propose the usage of two optimization algorithms, NSGA-II and GDE3, to solve this problem. These algorithms were taken into consideration because both work with multi-objective problems and present good performance. We consider a triathlon circuit scenario based on real data from the Ironman route as an use case to evaluate and compare the algorithms. The results show that GDE3 is able to attend both objectives (minimize failure and minimize energy consumption), while the NSGA-II prioritizes energy consumption.
引用
收藏
页码:8995 / 9019
页数:25
相关论文
共 41 条
[21]  
Kukkonen S, 2005, IEEE C EVOL COMPUTAT, P443
[22]  
Kukkonen S, 2004, LECT NOTES COMPUT SC, V3242, P752
[23]  
Kukkonen S., 2004, P 4 EUR C COMP METH, P445
[24]   Deep-Learning-Assisted Network Orchestration for On-Demand and Cost-Effective vNF Service Chaining in Inter-DC Elastic Optical Networks [J].
Li, Baojia ;
Lu, Wei ;
Liu, Siqi ;
Zhu, Zuqing .
JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING, 2018, 10 (10) :D29-D41
[25]  
Maksymyuk T, 2018, PROCEEDINGS OF THE 2018 IEEE 4TH INTERNATIONAL SYMPOSIUM ON WIRELESS SYSTEMS WITHIN THE INTERNATIONAL CONFERENCES ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS (IDAACS-SWS), P241, DOI 10.1109/IDAACS-SWS.2018.8525802
[26]  
Mohammad Ali Howraa M., 2015, 2015 20th European Conference on Networks and Optical Communications (NOC), P1, DOI 10.1109/NOC.2015.7238630
[27]  
Mohammadkhan Ali., 2016, P 2016 ACM WORKSHOP, P31
[28]  
Moualla G., 2018, PROC IEEE 7 INT C CL, P1
[29]  
Pei JN, 2018, IEEE INT CONF COMM
[30]  
Sahoo Jyotiprakash, 2010, Proceedings of the 2010 Second International Conference on Computer and Network Technology (ICCNT 2010), P222, DOI 10.1109/ICCNT.2010.49