Abstract
Emerging applications, such as mobile cloud computing and WSN, bring lots of challenges to current data center networks, and many of them need to construct their own virtual networks (VNs) for the flow-line calculation. Besides, delay-sensitive applications, like IoV and mobile 5G broadband application, require to move the computing resource and some parts of databases closed to customer sites. Thus, the computing resource near application fields, the so-called edge computing, becomes more and more in short supply; meanwhile, the backbone network is under an increasing pressure on the long-haul communication workload. In this paper, we first make a knowledge-aware of VN and design a mapping orchestration based on the QoS requirement features and workload matching. Here, we distinguish the computing requirement from each virtual node in VN and allocate them into edge or cloud DC, respectively. Then, we formulate this resource allocation problem and propose a fast and efficient algorithm. Finally, the numerical results verified the advantages of our algorithm in terms of average computing latency and average transmission latency.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yang X, Chen Z, Li K, Sun Y, Liu N, Xie W, Zhao Y. Communication-constrained mobile edge computing systems for wireless virtual reality: Scheduling and tradeoff. IEEE Access. 2018;6:16665–77.
Tan Z, Yu FR, Li X, Ji H, Leung VCM. Virtual resource allocation for heterogeneous services in full duplex-enabled SCNS with mobile edge computing and caching. IEEE Trans Veh Technol. 2018;67(2):1794–808.
Wang W, Zhao Y, Tornatore M, Gupta A, Zhang J, Mukherjee B. Virtual machine placement and workload assignment for mobile edge computing. In: Proceedings of CloudNet, Prague, Czech Republic; 2017. p. 1–6.
Yu C, Guo L, Hou W. Novel elastic optical network embedding using re-optimized VCAT framework accompanied by hitless PPSM function. IEEE J Lightwave Technol. 2016;34(22):5199–213.
Tzanakaki A, Anastosopoulos MP, Peng S, Rofoee B, Yan Y, Simeonidou D, Landi G, Bernini G, Ciulli N, Riera J F, Escalona E, Garcia-Espin JA, Katsalis K, Korakis T. A converged network architecture for energy efficient mobile cloud computing. In: Proceedings of ONDM, Stockholm, Sweden; 2014. p. 120–5.
Huang S, Rai S, Mukherjee B. Survivable differential delay aware multi-service over SONET/SDH networks with virtual concatenation. In: Proceedings of OFC/NFOEC, Anaheim, CA; 2007. p. 1–3.
Acknowledgements
The work in this paper is funded by Fundamental Research Funds for Central Universities (Grant No. 3132016318, 3132017078 and 3132018181) and National Natural Science Foundation of China (Grant No. 61371091).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yu, C., He, R., Lin, B., Zhang, L., Li, J. (2020). Knowledge-Aware VNE Orchestration in Cloud- and Edge-Mixed Data Center Networks. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_64
Download citation
DOI: https://doi.org/10.1007/978-981-13-6504-1_64
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6503-4
Online ISBN: 978-981-13-6504-1
eBook Packages: EngineeringEngineering (R0)