TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Traffic Forecast Assisted Adaptive VNF Dynamic Scaling |
| 영문제목(English Title) |
Traffic Forecast Assisted Adaptive VNF Dynamic Scaling |
| 저자(Author) |
Wanwan Guo
Mengkai Zhao
Zhihua Cui
Liping Xie
Hang Qiu
Hongbo Tang
Yu Zhao
Wei You
Xinsheng Ji
|
| 원문수록처(Citation) |
VOL 16 NO. 11 PP. 3584 ~ 3602 (2022. 11) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
NFV realizes flexible and rapid software deployment and management of network functions in the cloud network, and provides network services in the form of chained virtual network functions (VNFs). However, using VNFs to provide quality guaranteed services is still a challenge because of the inherent difficulty in intelligently scaling VNFs to handle traffic fluctuations. Most existing works scale VNFs with fixed-capacity instances, that is they take instances of the same size and determine a suitable deployment location without considering the cloud network resource distribution. This paper proposes a traffic forecasted assisted proactive VNF scaling approach, and it adopts the instance capacity adaptive to the node resource. We first model the VNF scaling as integer quadratic programming and then propose a proactive adaptive VNF scaling (PAVS) approach. The approach employs an efficient traffic forecasting method based on LSTM to predict the upcoming traffic demands. With the obtained traffic demands, we design a resource-aware new VNF instance deployment algorithm to scale out under-provisioning VNFs and a redundant VNF instance management mechanism to scale in over-provisioning VNFs. Trace-driven simulation demonstrates that our proposed approach can respond to traffic fluctuation in advance and reduce the total cost significantly. |
| 키워드(Keyword) |
Bi-objective game
cloud computing
many-objective optimization algorithms
task scheduling
NFV
VNF scaling
Traffic forecasting
New instance deployment
Redundant instance management
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