Abstract
With recent developments in cloud computing, massive unplanned traffic loads are submitted to cloud platforms. High traffic load variations lead to uncertainty in resource utilization. Therefore, efficient data-driven mechanisms for automatic resource management become crucial. These mechanisms enable complex and distributed systems to anticipate and efficiently react to workload fluctuations. They rely on accurate resource utilization prediction techniques to satisfy the resource needs, in order to fulfill the service level objective for cloud applications and infrastructures. In this paper, we propose a deep learning model to predict the resource consumption (e.g., CPU, memory) in network function virtualization infrastructures. We model an augmented graphical neural network (GNN) that exploit neighbouring relationships between virtual network functions (VNF) composing various service function chains (SFC), and use an augmented feature vector allowing to capture the consumption evolution of a VNF. The model enables to predict the resource needs of VNFs by identifying the multidimensional dependencies according to the graph structure of an SFC. The proposed GNN model has been compared with MLP, LSTM, hybrid LSTM and CNN models to evaluate its accuracy and efficiency. Real word datasets have been used to evaluate the proposed model using five performance metrics. The performance analysis reveals that our graph-features based GNN model outperforms the other models for SFCs with high traffic load variation.
Similar content being viewed by others
References
Andrikopoulos V, Binz T, Leymann F, Strauch S (2013) How to adapt applications for the cloud environment. Computing 95:793–535
Mostafavi S, Hakami V, Sanaei M (2021) Quality of service provisioning in network function virtualization: a survey. Computing 103:917–991
Salimian L, Safi Esfahani F, Nadimi-Shahraki M-H (2016) An adaptive fuzzy threshold-based approach for energy and performance efficient consolidation of virtual machines. Computing 98(6):641–660
Amiri M, Mohammad-Khanli L (2017) Survey on prediction models of applications for resources provisioning in cloud. J Netw Comput Appl 82:93–113
Younge AJ, Von Laszewski G, Wang L, Lopez-Alarcon S, Carithers W (2010) Efficient resource management for cloud computing environments. In: International conference on green computing. IEEE, pp 357–364
Patel P, Ranabahu AH, Sheth AP (2009) Service level agreement in cloud computing
da Costa LALF, Kunst R, de Freitas EP (2022) Intelligent resource sharing to enable quality of service for network clients: the trade-off between accuracy and complexity. Computing 1–13
Masdari M, Khoshnevis A (2019) A survey and classification of the workload forecasting methods in cloud computing. Clust Comput 23:1–26
Anuradha VP, Sumathi D (2014) A survey on resource allocation strategies in cloud computing. In: International conference on information communication and embedded systems (ICICES2014). IEEE, pp 1–7
Farahnakian F, Pahikkala T, Liljeberg P, Plosila J, Hieu NT, Tenhunen H (2016) Energy-aware VM consolidation in cloud data centers using utilization prediction model. IEEE Trans Cloud Comput 7(2):524–536
Chen J, Wang Y (2020) An adaptive short-term prediction algorithm for resource demands in cloud computing. IEEE Access 8:53915–53930
Tseng F, Wang X, Chou L, Chao H, Leung VCM (2018) Dynamic resource prediction and allocation for cloud data center using the multiobjective genetic algorithm. IEEE Syst J 12(2):1688–1699
Mijumbi R, Hasija S, Davy S, Davy A, Jennings B, Boutaba R (2017) Topology-aware prediction of virtual network function resource requirements. IEEE Trans Netw Serv Manag 14(1):106–120
Jmila H, Khedher MI, El Yacoubi MA (2017) Estimating VNF resource requirements using machine learning techniques. In: International conference on neural information processing. Springer, pp 883–892
Qiu F, Zhang B, Guo J (2016) A deep learning approach for VM workload prediction in the cloud. In: 2016 17th IEEE/ACIS international conference on software engineering, artificial intelligence, networking and parallel/distributed computing (SNPD). IEEE, pp 319–324
Mijumbi R, Gorricho J-L, Serrat J (2014) Contributions to efficient resource management in virtual networks. In: IFIP international conference on autonomous infrastructure, management and security. Springer, pp 47–51
Vouk MA (2008) Cloud computing-issues, research and implementations. J Comput Inf Technol 16(4):235–246
Weingärtner R, Bräscher GB, Westphall CB (2015) Cloud resource management: a survey on forecasting and profiling models. J Netw Comput Appl 47:99–106
Calheiros RN, Masoumi E, Ranjan R, Buyya R (2014) Workload prediction using ARIMA model and its impact on cloud applications’ QoS. IEEE Trans Cloud Comput 3(4):449–458
Shi R, Zhang J, Chu W, Bao Q, Jin X, Gong C, Zhu Q, Yu C, Rosenberg S (2015) MDP and machine learning-based cost-optimization of dynamic resource allocation for network function virtualization. In: 2015 IEEE international conference on services computing. IEEE, pp 65–73
Gong Z, Gu X, Wilkes J (2010) Press: predictive elastic resource scaling for cloud systems. In: 2010 international conference on network and service management. IEEE, pp 9–16
Nguyen H, Shen Z, Gu X, Subbiah S, Wilkes J (2013) \(\{\)AGILE\(\}\): elastic distributed resource scaling for infrastructure-as-a-service. In: 10th international conference on autonomic computing (\(\{\)ICAC\(\}\) 13), pp 69–82
Tseng F-H, Wang X, Chou L-D, Chao H-C, Leung VCM (2017) Dynamic resource prediction and allocation for cloud data center using the multiobjective genetic algorithm. IEEE Syst J 12(2):1688–1699
Zhang Q, Yang LT, Yan Z, Chen Z, Li P (2018) An efficient deep learning model to predict cloud workload for industry informatics. IEEE Trans Ind Inform 14(7):3170–3178
Lianming Z, Huan Z, Qian T, Pingping D, Zhen Z, Yehua W, Jing M, Kaiping X (2020) Lntp: an end-to-end online prediction model for network traffic. IEEE Netw 35:226–233
Ouhame S, Hadi Y, Ullah A (2021) An efficient forecasting approach for resource utilization in cloud data center using CNN-LSTM model. Neural Comput Appl 33:1–13
Song B, Yao Yu, Zhou Yu, Wang Z, Sidan D (2018) Host load prediction with long short-term memory in cloud computing. J Supercomput 74(12):6554–6568
Li B, Wei L, Liu S, Zhu Z (2018) Deep-learning-assisted network orchestration for on-demand and cost-effective vNF service chaining in inter-dc elastic optical networks. IEEE/OSA J Opt Commun Netw 10(10):D29–D41
Gupta S, Dinesh DA (2017) Resource usage prediction of cloud workloads using deep bidirectional long short term memory networks. In: 2017 IEEE international conference on advanced networks and telecommunications systems (ANTS). IEEE, pp 1–6
Bi J, Li S, Yuan H, Zhou MC (2021) Integrated deep learning method for workload and resource prediction in cloud systems. Neurocomputing 424:35–48
Kalchbrenner N, Danihelka I, Graves A (2015) Grid long short-term memory. arXiv:1507.01526
Zhu Y, Zhang W, Chen Y, Gao H (2019) A novel approach to workload prediction using attention-based LSTM encoder–decoder network in cloud environment. EURASIP J Wirel Commun Netw 2019(1):1–18
Feng J, Chen X, Gao R, Zeng M, Li Y (2018) Deeptp: an end-to-end neural network for mobile cellular traffic prediction. IEEE Netw 32(6):108–115
Mijumbi R, Serrat J, Gorricho J-L, Latré S, Charalambides M, Lopez D (2016) Management and orchestration challenges in network functions virtualization. IEEE Commun Mag 54(1):98–105
Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2008) The graph neural network model. IEEE Trans Neural Netw 20(1):61–80
Xiao Y, Zhang Q, Liu F, Wang J, Zhao M, Zhang Z, Zhang J (2019) Nfvdeep: adaptive online service function chain deployment with deep reinforcement learning. In: Proceedings of the international symposium on quality of service, pp 1–10
Wang T, Fan Q, Li X, Zhang X, Xiong Q, Fu S, Gao M (2021) Drl-sfcp: adaptive service function chains placement with deep reinforcement learning. In: ICC 2021-IEEE international conference on communications. IEEE, pp 1–6
Kim H, Park S, Lange S, Lee D, Heo D, Choi H, Yoo J-H, Won-Ki HJ (2020) Graph neural network-based virtual network function management. In: APNOMS, pp 13–18
Jalodia N, Henna S, Davy A (2019) 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, pp 1–5
Mijumbi R, Hasija S, Davy S, Davy A, Jennings B, Boutaba R (2016) A connectionist approach to dynamic resource management for virtualised network functions. In: 2016 12th international conference on network and service management (CNSM). IEEE, pp 1–9
Biemann C (2016) Vectors or graphs? On differences of representations for distributional semantic models. In: Proceedings of the 5th workshop on cognitive aspects of the Lexicon (CogALex-V)
Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans Neural Netw 20(1):61–80
Ramchoun H, Idrissi MAJ, Ghanou Y, Ettaouil M (2016) Multilayer perceptron: architecture optimization and training. IJIMAI 4(1):26–30
Albawi S, Mohammed TA, Al-Zawi S (2017) Understanding of a convolutional neural network. In: 2017 international conference on engineering and technology (ICET). IEEE, pp 1–6
O’Shea K, Nash R (2015) An introduction to convolutional neural networks. arXiv:1511.08458
Gardner MW, Dorling SR (1998) Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmos Environ 32(14–15):2627–2636
Scarselli F, Tsoi AC (1998) Universal approximation using feedforward neural networks: a survey of some existing methods, and some new results. Neural Netw 11(1):15–37
Huang Z, Xu W, Yu K (2015) Bidirectional LSTM-CRF models for sequence tagging. arXiv:1508.01991
Greff K, Srivastava RK, Koutník J, Steunebrink BR, Schmidhuber J (2016) LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst 28(10):2222–2232
Lindemann B et al (2021) A survey on long short-term memory networks for time series prediction. Procedia CIRP 99:650–655
Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE). Geosci Model Dev Discuss 7(1):1525–1534
O’Grady KE (1982) Measures of explained variance: cautions and limitations. Psychol Bull 92(3):766
Ozer DJ (1985) Correlation and the coefficient of determination. Psychol Bull 97(2):307
Jeff Heaton (2008) Introduction to neural networks with Java. Heaton Research, Inc, Chesterfield
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest with any financial organization.
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
Bellili, A., Kara, N. A graphical deep learning technique-based VNF dependencies for multi resource requirements prediction in virtualized environments. Computing 106, 449–473 (2024). https://doi.org/10.1007/s00607-023-01225-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00607-023-01225-2
Keywords
- Virtualized network function
- Deep learning
- VNF neighboring dependencies
- Service function chain
- Cloud computing