Near-Optimal Energy-Efficient Algorithm for Virtual Network Function Placement

To accommodate heterogeneous and sophisticated network services, Network Function Virtualization (NFV) is invented as a hopeful networking technology. The most distinct feature of NFV is that it separates network functions from physical hardware. In the NFV architecture, various types of Virtual Net...

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Bibliographic Details
Published in:IEEE transactions on cloud computing 2022-01, Vol.10 (1), p.553-567
Main Authors: Zhang, Xiaoning, Xu, Zhichao, Fan, Lang, Yu, Shui, Qu, Youyang
Format: Article
Language:English
Subjects:
Algorithms
Approximation algorithms
Cloud computing
Energy consumption
Energy efficiency
Heuristic algorithms
Integer programming
Linear programming
Markov approximation
Optimization
Placement
Polynomials
Power consumption
Power demand
Servers
service function chain
Steering
Telecommunications
Virtual networks
Virtualized network function
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Summary:To accommodate heterogeneous and sophisticated network services, Network Function Virtualization (NFV) is invented as a hopeful networking technology. The most distinct feature of NFV is that it separates network functions from physical hardware. In the NFV architecture, various types of Virtual Network Functions (VNFs) are placed on specific software-based middleboxes by telecom providers. Traffic traverses through a sequence of Virtual Network Functions (VNFs) in pre-defined order, which is named as Service Function Chain (SFC). However, how to effectively place VNFs at different locations and steer SFC requests while minimizing energy consumption is still an open problem. Accordingly, we investigate on the joint optimization of VNF placement and traffic steering for energy efficiency in telecom networks. We first present the power consumption model in NFV-enabled telecom networks, and then formulate the studied problem as an Integer Linear Programming (ILP) model. Since the problem is proved as NP-hard, we design a polynomial algorithm that can achieve near-optimal performances based on the Markov approximation technique. In addition, our algorithm can be extended to an online version to serve dynamic arriving SFC requests. The online algorithm achieves a near-optimal long-term averaged performance. Extensive simulation results show that compared with the benchmark algorithms, in the offline and online scenario, our algorithm can reduce up to 14.08 and 13.72 percent power consumption in telecom networks, respectively.
ISSN:2168-7161
2372-0018
DOI:10.1109/TCC.2019.2947554