NFVLearn: A multi‐resource, long short‐term memory‐based virtual network function resource usage prediction architecture
Virtual resource load prediction in network function virtualization (NFV) is the subject of intense research due to its crucial role in enabling proactive resource adaptation in dynamic NFV environments whose resource demand constantly changes. Several long short‐term memory (LSTM)‐based approaches...
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| Published in: | Software, practice & experience Vol. 53; no. 3; pp. 555 - 578 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Bognor Regis
Wiley Subscription Services, Inc
01.03.2023
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| ISSN: | 0038-0644, 1097-024X |
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| Abstract | Virtual resource load prediction in network function virtualization (NFV) is the subject of intense research due to its crucial role in enabling proactive resource adaptation in dynamic NFV environments whose resource demand constantly changes. Several long short‐term memory (LSTM)‐based approaches have been proposed to forecast the resource load of multiple resource attributes of a virtual network function (VNF) in a service function chain (SFC). In this article, we present NFVLearn, a flexible multivariate, many‐to‐many LSTM‐based model which uses different types of resource load history (CPU, memory, I/O bandwidth) from various VNFs of an SFC to predict future loads of multiple resources of a VNF. We then compare four novel automated input selection frameworks for NFVLearn. Simulations on those frameworks based on graph neural networks, Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall rank correlation coefficient demonstrate that models using lesser, highly correlated input features retain high prediction root mean squared error accuracy and coefficients of determination scores by leveraging resource attribute inter‐dependencies from the SFC. Those results show that resource attribute interdependency‐based input feature selection frameworks can reduce overhead in the control plane while keeping high accuracy and high fidelity resource load prediction of multiple resource attributes. |
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| AbstractList | Virtual resource load prediction in network function virtualization (NFV) is the subject of intense research due to its crucial role in enabling proactive resource adaptation in dynamic NFV environments whose resource demand constantly changes. Several long short‐term memory (LSTM)‐based approaches have been proposed to forecast the resource load of multiple resource attributes of a virtual network function (VNF) in a service function chain (SFC). In this article, we present NFVLearn, a flexible multivariate, many‐to‐many LSTM‐based model which uses different types of resource load history (CPU, memory, I/O bandwidth) from various VNFs of an SFC to predict future loads of multiple resources of a VNF. We then compare four novel automated input selection frameworks for NFVLearn. Simulations on those frameworks based on graph neural networks, Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall rank correlation coefficient demonstrate that models using lesser, highly correlated input features retain high prediction root mean squared error accuracy and coefficients of determination scores by leveraging resource attribute inter‐dependencies from the SFC. Those results show that resource attribute interdependency‐based input feature selection frameworks can reduce overhead in the control plane while keeping high accuracy and high fidelity resource load prediction of multiple resource attributes. |
| Author | St‐Onge, Cédric Edstrom, Claes Kara, Nadjia |
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| References_xml | – volume: 9 start-page: 1735 issue: 8 year: 1997 end-page: 1780 article-title: Ltsm publication-title: Neural Comput – start-page: 344 year: 2020 end-page: 346 – volume: 1 start-page: 1 year: 2016 end-page: 20 – volume: 6 start-page: 23 551 year: 2018 end-page: 23 560 article-title: LSTM‐based analysis of industrial IoT equipment publication-title: IEEE Access – volume: 193 issue: September year: 2021 article-title: Application of a Long Short Term Memory neural predictor with asymmetric loss function for the resource allocation in NFV network architectures publication-title: Comput Netw – volume: 14 start-page: 106 issue: 1 year: 2017 end-page: 120 article-title: Topology‐aware prediction of virtual network function resource requirements publication-title: IEEE Trans Netw Serv Manag – volume: 12 start-page: 1 issue: 11 year: 2020 end-page: 13 article-title: Proposal and investigation of an artificial intelligence (Ai)‐based cloud resource allocation algorithm in network function virtualization architectures publication-title: Future Internet – volume: 1 start-page: 57 year: 2020 end-page: 81 article-title: Graph neural networks: a review of methods and applications publication-title: AI Open – start-page: 1 year: 2019 end-page: 6 – volume: 186 year: 2021 article-title: Ordinal pattern dependence as a multivariate dependence measure publication-title: J Multivar Anal – start-page: 272 end-page: 276 – volume: 161 start-page: 251 year: 2019 end-page: 263 article-title: VNE‐TD: a virtual network embedding algorithm based on temporal‐difference learning publication-title: Comput Netw – year: 2020 – start-page: 33 year: 2021 end-page: 68 article-title: Managing virtualized networks and services with machine learning publication-title: Commun Netw Serv Manag Era Artif Intell Mach Learn – volume: 2021 year: 2021 article-title: Determinants of commodity futures prices: decomposition approach publication-title: Math Probl Eng – volume: 18 start-page: 1476 issue: 2 year: 2021 end-page: 1490 article-title: A network intelligence architecture for efficient VNF lifecycle management publication-title: IEEE Trans Netw Serv Manag – volume: 102 start-page: 738 year: 2020 end-page: 745 article-title: A new optimization algorithm for non‐stationary time series prediction based on recurrent neural networks publication-title: Futur Gener Comput Syst – volume: 49 start-page: 617 issue: 4 year: 2019 end-page: 639 article-title: A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers publication-title: Softw Pract Exp – volume: E104.D start-page: 606 issue: 5 year: 2021 end-page: 616 article-title: Sparse regression model‐based relearning architecture for shortening learning time in traffic prediction publication-title: IEICE Trans Inf Syst – volume: 30 issue: 1 year: 2022 article-title: Comparison of machine learning techniques for VNF resource requirements prediction in NFV publication-title: J Netw Syst Manag – volume: 21 start-page: 2224 issue: 3 year: 2019 end-page: 2287 article-title: Deep learning in mobile and wireless networking: a survey publication-title: IEEE Commun Surv Tutor – start-page: 444 year: 2019 end-page: 449 – volume: 81 start-page: 899 year: 2019 end-page: 913 article-title: A hybrid short‐term electricity price forecasting framework: Cuckoo search‐based feature selection with singular spectrum analysis and SVM publication-title: Energy Econ – ident: e_1_2_12_14_1 doi: 10.1007/s10922‐021‐09629‐1 – ident: e_1_2_12_19_1 doi: 10.1109/BigData.2017.8258087 – ident: e_1_2_12_21_1 doi: 10.1016/j.aiopen.2021.01.001 – ident: e_1_2_12_22_1 doi: 10.1016/j.eneco.2019.05.026 – ident: e_1_2_12_16_1 doi: 10.1016/j.comnet.2019.05.004 – ident: e_1_2_12_23_1 doi: 10.1016/j.jmva.2021.104798 – ident: e_1_2_12_26_1 doi: 10.1162/neco.1997.9.8.1735 – ident: e_1_2_12_4_1 doi: 10.1109/ICTC49870.2020.9289275 – ident: e_1_2_12_15_1 doi: 10.1109/ANTS47819.2019.9118065 – ident: e_1_2_12_25_1 doi: 10.1109/ACCESS.2018.2825538 – ident: e_1_2_12_6_1 doi: 10.1109/ICSPCS50536.2020.9310033 – ident: e_1_2_12_20_1 – ident: e_1_2_12_24_1 doi: 10.1155/2021/6032325 – start-page: 33 year: 2021 ident: e_1_2_12_2_1 article-title: Managing virtualized networks and services with machine learning publication-title: Commun Netw Serv Manag Era Artif Intell Mach Learn – ident: e_1_2_12_12_1 doi: 10.1109/TNSM.2020.3015244 – ident: e_1_2_12_7_1 doi: 10.1016/j.comnet.2021.108104 – ident: e_1_2_12_13_1 doi: 10.1109/NOMS.2014.6838258 – ident: e_1_2_12_9_1 doi: 10.1109/NetSoft48620.2020.9165449 – ident: e_1_2_12_3_1 doi: 10.1109/TNSM.2017.2666781 – ident: e_1_2_12_18_1 doi: 10.1016/j.future.2019.09.018 – ident: e_1_2_12_17_1 doi: 10.1109/COMST.2019.2904897 – ident: e_1_2_12_11_1 doi: 10.1109/NETSOFT.2019.8806620 – ident: e_1_2_12_5_1 doi: 10.1002/spe.2635 – ident: e_1_2_12_8_1 doi: 10.3390/fi12110196 – ident: e_1_2_12_10_1 doi: 10.1587/transinf.2020NTP0010 |
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| SubjectTerms | Accuracy Correlation coefficients GNN Graph neural networks input feature selection Load Load history LSTM network function virtualization resource usage prediction Virtual memory systems Virtual networks |
| Title | NFVLearn: A multi‐resource, long short‐term memory‐based virtual network function resource usage prediction architecture |
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