Abstract
Network function virtualization (NFV) provides dynamic, energy-efficient, and cost-effective network services by segregating network services from expensive and energy-hungry hardware components. Traditional hardware-based network functions are always ON with 100% capacity, resulting in massive energy consumption. In contrast, NFV enables different network services (NS) through virtual network functions (VNFs), which can reduce energy consumption, by automatic ON/OFF (auto-scaling) of the VNFs depending on the traffic load. However, auto-scaling will introduce delays in the network service management process. This delay can be minimized by estimating future resource requirements accurately and taking proactive steps ahead of time. In this research, we propose the use of deep learning based forecasting algorithms to predict the required VNFs and virtual CPU for each VNF to complete a network service, often deployed as service function chain (SFC). We use long short term memory (LSTM), bidirectional-LSTM, and convolutional neural network (CNN) based algorithms to forecast resources from univariate and multivariate network traffic data. Experimental results show that CNN and bidirectional-LSTM-based forecasting models can forecast more accurately than the prior studies. Based on the forecasting models, we propose a resource allocation algorithm (DeepVRM) that can efficiently allocate resources to complete SFC.
Similar content being viewed by others
References
Montazerolghaem, A., Yaghmaee, M.H., Leon-Garcia, A.: Green cloud multimedia networking: Nfv/sdn based energy-efficient resource allocation. IEEE Trans. Green Commun. Netw. 4(3), 873–889 (2020)
Rahman, S., Gupta, A., Tornatore, M., Mukherjee, B.: Dynamic workload migration over backbone network to minimize data center electricity cost. IEEE Trans. Green Commun. Netw. 2(2), 570–579 (2017)
ETSI, N.F.V.: Network functions virtualisation (nfv). Manage. Orch. 1, V1 (2014)
Askari, L., Hmaity, A., Musumeci, F., Tornatore, M.: Virtual-networkfunction placement for dynamic service chaining in metro-area networks, in International Conference on Optical Network Design and Modeling (ONDM), IEEE, pp. 136-141 (2018)
Rahman, S., Ahmed, T., Huynh, M., Tornatore, M., Mukherjee, B.: Auto-scaling network service chains using machine learning and negotiation game. IEEE Trans. Netw. Serv. Manage. 17(3), 1322–1336 (2020)
Benmakrelouf, S., Kara, N., Tout, H., Rabipour, R., Edstrom, C.: Resource needs prediction in virtualized systems: generic proactive and selfadaptive solution. J. Netw. Comput. Appl. 148102, 443 (2019)
Assi, C., Ayoubi, S., El Khoury, N., Qu, L.: Energy-aware mapping and scheduling of network flows with deadlines on vnfs. IEEE Trans. Green Commun. Netw. 3(1), 192–204 (2018)
Oliveira, T.P., Barbar, J.S., Soares, A.S.: Computer network traffic prediction: a comparison between traditional and deep learning neural networks. Intern. J. Big Data Intell. 3(1), 28–37 (2016)
Zaman, Z., Rahman, S., Naznin, M.: Novel approaches for vnf requirement prediction using dnn and lstm, in IEEE Global Communications Conference (GLOBECOM), IEEE, 2019, pp. 1-6
Rahman, S., Ahmed, T., Huynh, Tornatore, M.M., Mukherjee, B.: Auto-scaling vnfs using machine learning to improve qos and reduce cost, in 2018 IEEE International Conference on Communications (ICC), IEEE, (2018), pp. 1-6
Liaw, A., Wiener, M., et al.: Classification and regression by randomforest. R News 2(3), 18–22 (2002)
Jmila, H., . Khedher, M.I., and M. A. El Yacoubi, Estimating vnf resource requirements using machine learning techniques, in International Conference on Neural Information Processing, pp. 883-892. Springer, Heidelberg (2017)
Drucker, H., Burges, C.J., Kaufman, L., Smola, A., Vapnik, V., et al.: Support vector regression machines. Adv. Neural Inf. Proc. Syst. 9, 155–161 (1997)
A. Mestres, E. Alarcón, and A. Cabellos, A machine learning-based approach for virtual network function modeling, in IEEE Wireless Communications and Networking Conference Workshops (WCNCW), IEEE, 2018, pp. 237-242
Mijumbi, R., Hasija, S., Davy, S., Davy, A., Jennings, B., Boutaba, R.: Topology-aware prediction of virtual network function resource requirements. IEEE Trans. Netw. Serv. Manage. 14(1), 106–120 (2017)
Scalingi, A., Esposito, F., Muhammad, W., Pescapé, A.: Scalable provisioning of virtual network functions via supervised learning, in IEEE Conference on Network Softwarization (NetSoft), IEEE, 2019, pp. 423- 431
Kim, H.-G., Jeong, S.-Y., Lee, D.-Y.Choi, H., Yoo, J.-H., Hong, J. W.-K.: A deep learning approach to vnf resource prediction using correlation between vnfs, in IEEE Conference on Network Softwarization (NetSoft), IEEE, 2019, pp. 444-449
D. M. Manias, M. Jammal, H. Hawilo, et al., Machine learning for performanceaware virtual network function placement, in IEEE Global Communications Conference (GLOBECOM), IEEE, 2019, pp. 1-6
Yao, Y., Guo, S., Li, P., Liu, G., Zeng, Y.: Forecasting assisted vnf scaling in nfv-enabled networks. Comput. Netw. 168, 107040 (2020)
Alawe, I., Ksentini, A., Hadjadj-Aoul, Y., Bertin, P.: Improving traffic forecasting for 5g core network scalability: a machine learning approach. IEEE Netw. 32(6), 42–49 (2018)
N. Jalodia, S. Henna, and A. Davy, Deep reinforcement learning for topology-aware vnf resource prediction in nfv environments, in 2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), IEEE, 2019, pp. 1-5
Pei, J., Hong, P., Pan, M., Liu, J., Zhou, J.: Optimal vnf placement via deep reinforcement learning in sdn/nfv-enabled networks. IEEE J. Sel. Areas Commun. 38(2), 263–278 (2019)
Mahboob, T., Jung, Y.R., Chung, M.Y.: Dynamic vnf placement to manage user traffic flow in software-defined wireless networks. J. Netw. Syst. Manage. 28(3), 436–456 (2020)
Schardong, F., Nunes, I., Schaeffer-Filho, A.: Nfv resource allocation: a systematic review and taxonomy of vnf forwarding graph embedding. Comput. Netw. 185, 107726 (2021)
Fang, L., Zhang, X., Sood, K., Wang, Y., Yu, S.: Reliability-aware virtual network function placement in carrier networks. J. Netw. Compu. Appl. 154, 102536 (2020)
Gupta, A., Habib, M.F., Mandal, U., Chowdhury, P., Tornatore, M., Mukherjee, B.: On service-chaining strategies using virtual network functions in operator networks. Comput. Netw. 133, 1–16 (2018)
Li, B., Lu, W., Liu, S., Zhu, Z.: Deep-learning-assisted network orchestration for on-demand and cost-effective vnf service chaining in inter-dc elastic optical networks. J. Opt. Commun. Netw. 10(10), D29–D41 (2018)
Datamarket. Internet traffic data. (2018), [Online]. Available: https://datamarket.com/data/set/232n
Orlowski, S., Wessäly, R., Pióro, M., Tomaszewski, A.: Sndlib 1.0survivable network design library. Netw.: Intern. J. 55(3), 276–286 (2010)
CISCO. Cisco cloud services router 1000v data sheet. (2020), [Online]. Available: https://www.cisco.com/c/en/us/products/collateral/routers/cloud-services-router-1000v-series/data_sheet-c78-733443.html
M. Bloem, T. Alpcan, S. Schmidt, and T. Basar, Malware filtering for network security using weighted optimality measures, in IEEE International Conference on Control Applications, IEEE, 2007, pp. 295-300
Contreras, J., Espinola, R., Nogales, F., Conejo, A.: Arima models to predict next-day electricity prices. IEEE Trans. Power Syst. 18(3), 1014–1020 (2003). https://doi.org/10.1109/TPWRS.2002.804943
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
A. Deshpande, A beginner’s guide to understanding convolutional neural networks, Retrieved March, vol. 31, no. 2017, (2016)
Wang, X., Wang, Y., Peng, J., Zhang, Z., Tang, X.: A hybrid framework for multivariate long-sequence time series forecasting. Appl. Intell. 13, 1–20 (2022)
McKinney, W., et al.: Pandas: a foundational python library for data analysis and statistics. Python High Perform. Sci. Comput. 14(9), 1–9 (2011)
Pedregosa, F., Varoquaux, G., Gramfort, A., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980 (2014)
M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio, Binarized neural networks: training deep neural networks with weights and activations constrained to+ 1 or-1, arXiv preprint arXiv:1602.02830 (2016)
A. F. Agarap, Deep learning using rectified linear units (relu), arXiv preprint arXiv:1803.08375 (2018)
bluesummers. Lstm and bidirectional lstm. (2017), [Online]. Available: https://stackoverflow.com/questions/43035827/whats-the-differencebetween-a-bidirectional-lstm-and-an-lstm
H. Pokharna. The best explanation of cnn. (2016), [Online]. Available: https://medium.com/technologymadeeasy/the-best-explanationof-convolutional-neural-networks-on-the-internet-fbb8b1ad5df8
Acknowledgements
This work has been carried out in the Department of Computer Science and Engineering (CSE), Bangladesh University of Engineering and Technology (BUET). The authors gratefully acknowledge the support and facilities provided by BUET Basic Research Grant.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zaman, Z., Rahman, S., Rafsani, F. et al. DeepVRM: Deep Learning Based Virtual Resource Management for Energy Efficiency. J Netw Syst Manage 31, 66 (2023). https://doi.org/10.1007/s10922-023-09752-1
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10922-023-09752-1