• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Zu Jiachen, Hu Guyu, Yan Jiajie, Li Shiji. Resource Management of Service Function Chain in NFV Enabled Network: A Survey[J]. Journal of Computer Research and Development, 2021, 58(1): 137-152. DOI: 10.7544/issn1000-1239.2021.20190823
Citation: Zu Jiachen, Hu Guyu, Yan Jiajie, Li Shiji. Resource Management of Service Function Chain in NFV Enabled Network: A Survey[J]. Journal of Computer Research and Development, 2021, 58(1): 137-152. DOI: 10.7544/issn1000-1239.2021.20190823

Resource Management of Service Function Chain in NFV Enabled Network: A Survey

More Information
  • Published Date: December 31, 2020
  • With the emergence of new network technologies such as cloud computing, software-defined network (SDN) and network function virtualization (NFV), the future network’s management is supposed to become virtual and intelligent. NFV provides an approach to realize service functions based on the virtualization technology, and it adopts general servers to substitute the dedicated middlebox in traditional network, which is able to greatly reduce the capital expenditure (CAPEX) and the operating expense (OPEX) of the telecom service provider (TSP). NFV can also improve flexibility and scalability in the management of network services. Since the end-to-end network services are usually composed of different service functions, it is an important research topic to adopt virtualization technology to build service function chain (SFC) and reasonably allocate and schedule resources. In this paper, based on the background of NFV technology, we introduce the infrastructure, technical basis, and application scenarios of SFC in the NFV enabled network. Afterward, we mainly focus on the different stages of SFC orchestration: SFC composition, SFC placement, SFC scheduling, and SFC adaptive scaling. The correlated existing theoretical research is summarized. Finally, in view of the existing problems, some solutions are proposed and the future research directions are prospected.
  • Related Articles

    [1]Du Jinming, Sun Yuanyuan, Lin Hongfei, Yang Liang. Conversational Emotion Recognition Incorporating Knowledge Graph and Curriculum Learning[J]. Journal of Computer Research and Development, 2024, 61(5): 1299-1309. DOI: 10.7544/issn1000-1239.202220951
    [2]Liu Xinghong, Zhou Yi, Zhou Tao, Qin Jie. Self-Paced Learning for Open-Set Domain Adaptation[J]. Journal of Computer Research and Development, 2023, 60(8): 1711-1726. DOI: 10.7544/issn1000-1239.202330210
    [3]Wen Yimin, Yuan Zhe, Yu Hang. A New Semi-Supervised Inductive Transfer Learning Framework: Co-Transfer[J]. Journal of Computer Research and Development, 2023, 60(7): 1603-1614. DOI: 10.7544/issn1000-1239.202220232
    [4]Chen Zhenzhu, Zhou Chunyi, Su Mang, Gao Yansong, Fu Anmin. Research Progress of Secure Outsourced Computing for Machine Learning[J]. Journal of Computer Research and Development, 2023, 60(7): 1450-1466. DOI: 10.7544/issn1000-1239.202220767
    [5]Lu Shaoshuai, Chen Long, Lu Guangyue, Guan Ziyu, Xie Fei. Weakly-Supervised Contrastive Learning Framework for Few-Shot Sentiment Classification Tasks[J]. Journal of Computer Research and Development, 2022, 59(9): 2003-2014. DOI: 10.7544/issn1000-1239.20210699
    [6]Zhuo Junbao, Su Chi, Wang Shuhui, Huang Qingming. Min-Entropy Transfer Adversarial Hashing[J]. Journal of Computer Research and Development, 2020, 57(4): 888-896. DOI: 10.7544/issn1000-1239.2020.20190476
    [7]Feng Wei, Hang Wenlong, Liang Shuang, Liu Xuejun, Wang Hui. Deep Stack Least Square Classifier with Inter-Layer Model Knowledge Transfer[J]. Journal of Computer Research and Development, 2019, 56(12): 2589-2599. DOI: 10.7544/issn1000-1239.2019.20180741
    [8]Wen Yimin, Tang Shiqi, Feng Chao, Gao Kai. Online Transfer Learning for Mining Recurring Concept in Data Stream Classification[J]. Journal of Computer Research and Development, 2016, 53(8): 1781-1791. DOI: 10.7544/issn1000-1239.2016.20160223
    [9]Hong Jiaming, Yin Jian, Huang Yun, Liu Yubao, and Wang Jiahai. TrSVM: A Transfer Learning Algorithm Using Domain Similarity[J]. Journal of Computer Research and Development, 2011, 48(10): 1823-1830.
    [10]Mei Canhua, Zhang Yuhong, Hu Xuegang, and Li Peipei. A Weighted Algorithm of Inductive Transfer Learning Based on Maximum Entropy Model[J]. Journal of Computer Research and Development, 2011, 48(9): 1722-1728.

Catalog

    Article views (1376) PDF downloads (990) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return