Skip to main content
SpringerLink
Log in

A deep learning-based resource usage prediction model for resource provisioning in an autonomic cloud computing environment

  • S. I. : Effective and Efficient Deep Learning
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Cloud computing enables clients to acquire cloud resources dynamically and on demand for their cloud applications and services. For cloud providers, especially, Software as a Service (SaaS) providers, the prediction of future cloud resource requirements, such as CPU usage for their cloud applications, to implement client requests is a complex task because it depends on incoming workloads. Due to workload fluctuations, it is difficult for SaaS cloud providers to predict or forecast future demand for resource usage in the next time interval and, accordingly, to allocate the required resources. Furthermore, cloud computing systems consist of many virtual machines (VMs), which increases the complexity of the prediction problem due to the correlations that exist between the large workload data in these VMs. Therefore, accurate resource usage forecasting remains a challenge, and relatively few studies have explored the prediction of CPU usage for VMs in cloud data centers. This paper proposes an autonomic and intelligent workload forecasting method for cloud resource provisioning based on the concept of autonomic computing and a deep learning approach. In particular, to predict future demand for CPU usage and determine how to respond to workload fluctuations in the next interval, we propose an efficient deep learning model based on a diffusion convolutional recurrent neural network (DCRNN). Existing deep learning models that are widely applied cannot handle accurate real-time forecasting due to the presence of inconsistent and nonlinear workloads in cloud computing systems. The goal of the proposed deep learning model is to improve forecasting accuracy and minimize the error between the predicted and the actual workloads. The effectiveness of the proposed DCRNN-based deep learning model was evaluated using experiments on a real-world dataset of PlanetLab’s CPU usage traces. The results indicate that the proposed approach outperformed other existing deep learning models, achieving a mean absolute percentage error of 0.18 and root-mean-square error of 2.40.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Al-Asaly MS, Hassan MM, Alsanad A (2019) A cognitive/intelligent resource provisioning for cloud computing services: opportunities and challenges. Soft Comput 23:9069–9081

    Google Scholar 

  2. Zhang Q, Cheng L, Boutaba R (2010) Cloud computing: state-of-the-art and research challenges. J Internet Serv Appl 1:7–18

    Google Scholar 

  3. Lorido-Botran T, Miguel-Alonso J, Lozano JA (2014) A review of auto-scaling techniques for elastic applications in cloud environments. J Grid Comput 12:559–592

    Google Scholar 

  4. Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Futur Gener Comput Syst 25:599–616

    Google Scholar 

  5. Buyya R, Vecchiola C, Selvi ST (2013) Mastering cloud computing: foundations and applications programming. Newnes

    Google Scholar 

  6. Chandrasekaran K (2014) Essentials of cloud computing. CrC Press, Boca Raton

    Google Scholar 

  7. Mustafa S, Nazir B, Hayat A, Khan AUR, Madani SA (2015) Resource management in cloud computing: taxonomy, prospects, and challenges. Comput Electr Eng 47:186–203

    Google Scholar 

  8. Manvi SS, Krishna Shyam G (2014) Resource management for Infrastructure as a Service (IaaS) in cloud computing: a survey. J Netw Comput Appl 41:424–440

    Google Scholar 

  9. Herbst NR, Kounev S, Reussner R (2013) Elasticity in cloud computing: What it is, and what it is not. In: 10th Int Conf Auton Comput (ICAC 13). pp 23–27

  10. Qavami HR, Jamali S, Akbari MK, Javadi B (2014) Dynamic resource provisioning in cloud computing: a heuristic markovian approach. Lect Notes Inst Comput Sci Soc Telecommun Eng LNICST 133:102–111

  11. Amiri M, Mohammad-Khanli L (2017) Survey on prediction models of applications for resources provisioning in cloud. J Netw Comput Appl 82:93–113

    Google Scholar 

  12. Kephart JO, Chess DM (2003) The vision of autonomic computing. Computer (Long Beach Calif) 36:41–50

    Google Scholar 

  13. Ghobaei-Arani M, Jabbehdari S, Pourmina MA (2018) An autonomic resource provisioning approach for service-based cloud applications: a hybrid approach. Futur Gener Comput Syst 78:191–210

    Google Scholar 

  14. Jacob B, Lanyon-Hogg R, Nadgir DK, Yassin AF (2004) A practical guide to the IBM autonomic computing toolkit. IBM Redbooks 4

  15. Maurer M, Brandic I, Sakellariou R (2013) Adaptive resource configuration for Cloud infrastructure management. Futur Gener Comput Syst 29:472–487

    Google Scholar 

  16. Mateen M, Hayat S, Tehreem T, Akbar MA (2020) A self-adaptive resource provisioning approach using fuzzy logic for cloud-based applications. Int J Comput Digit Syst 9

  17. Li Y, Yu R, Shahabi C, Liu Y (2017) Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv 1–16

  18. Zhang Q, Yang LT, Chen Z, Li P (2018) A survey on deep learning for big data. Inf Fusion 42:146–157

    Google Scholar 

  19. 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 14:3170–3178

    Google Scholar 

  20. Deng L, Yu D (2014) Deep learning: methods and applications. Found Trends Signal Process 7:197–387

    MathSciNet  MATH  Google Scholar 

  21. Qiu F, Zhang B, Guo J (2016) A deep learning approach for VM workload prediction in the cloud. In: 2016 IEEE/ACIS 17th Int Conf Softw Eng Artif Intell Netw Parallel/Distributed Comput SNPD 2016 319–324

  22. Zhang W, Duan P, Yang LT, Xia F, Li Z, Lu Q, Gong W, Yang S (2017) Resource requests prediction in the cloud computing environment with a deep belief network. Softw Pract Exp 47:473–488

    Google Scholar 

  23. Kumar J, Singh AK, Buyya R (2021) Self directed learning based workload forecasting model for cloud resource management. Inf Sci (Ny) 543:345–366

    Google Scholar 

  24. Tran L, Mun MY, Lim M, Yamato J, Huh N, Shahabi C (2020) DeepTRANS: a deep learning system for public bus travel time estimation using traffic forecasting. Proc VLDB Endow 13:2957–2960

    Google Scholar 

  25. Andreoletti D, Troia S, Musumeci F, Giordano S, Maier G, Tornatore M (2019) Network traffic prediction based on diffusion convolutional recurrent neural networks. INFOCOM 2019 - IEEE Conf Comput Commun Work INFOCOM WKSHPS 2019 246–251

  26. Masdari M, Khoshnevis A (2020) A survey and classification of the workload forecasting methods in cloud computing. Cluster Comput 23:2399–2424

    Google Scholar 

  27. Islam S, Keung J, Lee K, Liu A (2012) Empirical prediction models for adaptive resource provisioning in the cloud. Futur Gener Comput Syst 28:155–162

    Google Scholar 

  28. Bankole AA, Ajila SA (2013) Predicting cloud resource provisioning using machine learning techniques. Can Conf Electr Comput Eng 31–34

  29. Garg SK, Toosi AN, Gopalaiyengar SK, Buyya R (2014) SLA-based virtual machine management for heterogeneous workloads in a cloud datacenter. J Netw Comput Appl 45:108–120

    Google Scholar 

  30. Kousiouris G, Menychtas A, Kyriazis D, Gogouvitis S, Varvarigou T (2014) Dynamic, behavioral-based estimation of resource provisioning based on high-level application terms in Cloud platforms. Futur Gener Comput Syst 32:27–40

    Google Scholar 

  31. Chang YC, Chang RS, Chuang FW (2014) A predictive method for workload forecasting in the cloud environment. Lect Notes Electr Eng. https://doi.org/10.1007/978-94-007-7262-5_65

    Article  Google Scholar 

  32. Chen Z, Zhu Y, Di Y, Feng S (2015) Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network. Comput Intell Neurosci. https://doi.org/10.1155/2015/919805

    Article  Google Scholar 

  33. Ramezani F, Naderpour M (2017) A fuzzy virtual machine workload prediction method for cloud environments. IEEE Int Conf Fuzzy Syst. https://doi.org/10.1109/FUZZ-IEEE.2017.8015450

  34. Amiri M, Feizi-Derakhshi MR, Mohammad-Khanli L (2017) IDS fitted Q improvement using fuzzy approach for resource provisioning in cloud. J Intell Fuzzy Syst 32:229–240

    Google Scholar 

  35. Khorsand R, Ghobaei-Arani M, Ramezanpour M (2018) WITHDRAWN: A fuzzy auto-scaling approach using workload prediction for MMOG application in a cloud environment. Simul Model Pract Theory 1

  36. Li S, Wang Y, Qiu X, Wang D, Wang L (2013) A workload prediction-based multi-vm provisioning mechanism in cloud computing. In: 2013 15th Asia-Pacific Netw. Oper. Manag. Symp. IEEE, pp 1–6

  37. Kumar AS, Mazumdar S (2016) Forecasting HPC workload using ARMA models and SSA. In: 2016 Int. Conf. Inf. Technol. IEEE, pp 294–297

  38. Calheiros RN, Masoumi E, Ranjan R, Buyya R (2015) Workload prediction using ARIMA model and its impact on cloud applications’ QoS. IEEE Trans Cloud Comput 3:449–458

    Google Scholar 

  39. Messias VR, Estrella JC, Ehlers R, Santana MJ, Santana RC, Reiff-Marganiec S (2016) Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure. Neural Comput Appl 27:2383–2406

    Google Scholar 

  40. Barati M, Sharifian S (2015) A hybrid heuristic-based tuned support vector regression model for cloud load prediction. J Supercomput 71:4235–4259

    Google Scholar 

  41. Baig SUR, Iqbal W, Berral JL, Erradi A, Carrera D (2019) Adaptive prediction models for data center resources utilization estimation. IEEE Trans Netw Serv Manag 16:1681–1693

    Google Scholar 

  42. Nikravesh AY, Ajila SA, Lung CH (2015) Towards an autonomic auto-scaling prediction system for cloud resource provisioning. In: Proc - 10th Int Symp Softw Eng Adapt Self-Managing Syst SEAMS 2015 35–45

  43. Ran Y, Yang J, Zhang S, Xi H (2017) Dynamic IaaS computing resource provisioning strategy with QoS constraint. IEEE Trans Serv Comput 10:190–202

    Google Scholar 

  44. Moreno-Vozmediano R, Montero RS, Huedo E, Llorente IM (2019) Efficient resource provisioning for elastic Cloud services based on machine learning techniques. J Cloud Comput. https://doi.org/10.1186/s13677-019-0128-9

    Article  Google Scholar 

  45. Tofighy S, Rahmanian AA, Ghobaei-Arani M (2018) An ensemble CPU load prediction algorithm using a Bayesian information criterion and smooth filters in a cloud computing environment. Softw Pract Exp 48:2257–2277

    Google Scholar 

  46. Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60:84–90

    Google Scholar 

  47. Ciregan D, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 3642–3649

  48. Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y (2014) Overfeat: integrated recognition, localization and detection using convolutional networks. 2nd Int. Conf. Learn. Represent. ICLR 2014 - Conf. Track Proc

  49. Ciresan DC, Meier U, Schmidhuber J (2012) Transfer learning for Latin and Chinese characters with deep neural networks. Proc Int Jt Conf Neural Networks. https://doi.org/10.1109/IJCNN.2012.6252544

    Article  Google Scholar 

  50. Ren JS, Xu L (2015) On vectorization of deep convolutional neural networks for vision tasks. Proc Natl Conf Artif Intell 3:1840–1846

    Google Scholar 

  51. Farabet C, Couprie C, Najman L, Lecun Y (2013) Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell 35:1915–1929

    Google Scholar 

  52. Tompson J, Jain A, LeCun Y, Bregler C (2014) Joint training of a convolutional network and a graphical model for human pose estimation. Adv Neural Inf Process Syst 2:1799–1807

    Google Scholar 

  53. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proc IEEE Conf Comput Vis pattern Recognit pp 1–9

  54. Hayat M, Bennamoun M, An S (2015) Deep reconstruction models for image set classification. IEEE Trans Pattern Anal Mach Intell 37:713–727

    Google Scholar 

  55. Mikolov T, Deoras A, Povey D, Burget L, Černocký J (2011) Strategies for training large scale neural network language models. In: 2011 IEEE Work Autom Speech Recognit Understanding, ASRU 2011, Proc 196–201

  56. Hinton G, Deng L, Yu D, Dahl GE, Mohamed A, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath TN (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29:82–97

    Google Scholar 

  57. Sainath TN, Mohamed AR, Kingsbury B, Ramabhadran B (2013) Deep convolutional neural networks for LVCSR. ICASSP, IEEE Int Conf Acoust Speech Signal Process - Proc 8614–8618

  58. Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12:2493–2537

    MATH  Google Scholar 

  59. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst 26:3111–3119

    Google Scholar 

  60. Lv Y, Duan Y, Kang W, Li Z, Wang FY (2015) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16:865–873

    Google Scholar 

  61. Dey S, Pratiher S, Mukherjee CK, Banerjee S (2020) Solarisnet: a deep regression network for solar radiation prediction. Mausam 71:443–450

    Google Scholar 

  62. Cole JH, Poudel RPK, Tsagkrasoulis D, Caan MWA, Steves C, Spector TD, Montana G (2017) Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage 163:115–124

    Google Scholar 

  63. Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    Google Scholar 

  64. Patel YS, Misra R (2018) Performance comparison of deep VM workload prediction approaches for cloud. In: Prog Comput Anal Netw. Springer, pp 149–160

  65. Gupta S, Dinesh DA (2018) Resource usage prediction of cloud workloads using deep bidirectional long short term memory networks. In: 11th IEEE Int Conf Adv Networks Telecommun Syst ANTS 2017 1–6

  66. Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41:23–50

    Google Scholar 

Download references

Acknowledgements

This work was supported by King Saud University, Riyadh, Saudi Arabia, under Researchers Supporting Project number RSP-2021/18.

Author information

Authors and Affiliations

  1. Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    Mahfoudh Saeed Al-Asaly, Ahmed Alsanad & Mohammad Mehedi Hassan

  2. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

    Mohamed A. Bencherif

Authors
  1. Mahfoudh Saeed Al-Asaly
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed A. Bencherif
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmed Alsanad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohammad Mehedi Hassan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Mehedi Hassan.

Ethics declarations

Conflict of interest

There is no conflict of interests among the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Al-Asaly, M.S., Bencherif, M.A., Alsanad, A. et al. A deep learning-based resource usage prediction model for resource provisioning in an autonomic cloud computing environment. Neural Comput & Applic 34, 10211–10228 (2022). https://doi.org/10.1007/s00521-021-06665-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06665-5

Keywords

Search

Navigation