Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Elastic edge cloud resource management based on horizontal and vertical scaling

Abstract

The resources in the edge cloud are numerous and complex, and elastic scaling services can make efficient use of these resources. However, the elastic scaling services need to suspend the user’s application tasks forcibly when carrying out resource redistribution, which brings a poor sense of experience to the user. In view of the above problems, a dynamic elastic scaling model based on load prediction is proposed, which improves resource utilization and reduces scaling costs without affecting user experience. The model is divided into two parts. In terms of load prediction, on the one hand, according to the historical features and current trends of the load, the load prediction model based on the improved cloud model is used to predict the load demand at the next moment. On the other hand, the correlation between CPU and memory is considered. In terms of elastic scaling, integer programming algorithm is proposed to expand and release the corresponding resources with the minimum cost of horizontal scaling (HS) and vertical scaling (VS). In order to verify the superiority of elastic scaling model based on load prediction, corresponding comparative experiments are conducted, which show that the proposed model can improve the accuracy of load prediction and resource utilization with low scaling costs. Especially, the cost of elastic scaling proposed by this paper is lower than horizontal and vertical scaling. Compared with HS, the elastic scaling method proposed in this paper reduces the cost by 14%. Compared with VS, this method reduces the cost by 11%.

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

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

References

  1. 1.

    Costache S, Dib D, Parlavantzas N et al (2017) Resource management in cloud platform as a service systems: analysis and opportunities. J Syst Softw 132:98–118

  2. 2.

    Chae MS, Lee HM, Lee K (2019) A performance comparison of Linux containers and virtual machines using Docker and KVM. Clust Comput 22(1):1765–1775

  3. 3.

    Wang S, Zhao Y, Huang L et al (2019) QoS prediction for service recommendations in mobile edge computing. J Parallel Distrib Comput 127:134–144

  4. 4.

    Li C, Wang YP, Chen Y et al (2019) Energy-efficient fault-tolerant replica management policy with deadline and budget constraints in edge-cloud environment. J Netw Comput Appl 143:152–166

  5. 5.

    Rossi F, Nardelli M, Cardellini V (2019) Horizontal and vertical scaling of container-based applications using reinforcement learning. In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD). IEEE, pp 329–338

  6. 6.

    Moghaddam SK, Buyya R, Ramamohanarao K (2019) ACAS: an anomaly-based cause aware auto-scaling framework for clouds. J Parallel Distrib Comput 126:107–120

  7. 7.

    Song B, Yu Y, Zhou Y et al (2018) Host load prediction with long short-term memory in cloud computing. J Supercomput 74(12):6554–6568

  8. 8.

    Barati M, Sharifian S (2015) A hybrid heuristic-based tuned support vector regression model for cloud load prediction. J Supercomput 71(11):4235–4259

  9. 9.

    Patel D, Gupta RK, Pateriya RK (2019) Energy-aware prediction-based load balancing approach with VM migration for the cloud environment. In: Shukla R, Agrawal J, Sharma S, Singh Tomer G (eds) Data, engineering and applications. Springer, Singapore, pp 59–74

  10. 10.

    Dambreville A, Tomasik J, Cohen J et al (2017) Load prediction for energy-aware scheduling for cloud computing platforms. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). IEEE, pp 2604–2607

  11. 11.

    Tang X, Liu Q, Dong Y et al (2018) Fisher: an efficient container load prediction model with deep neural network in clouds. In: 2018 IEEE International Conference on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom). IEEE, pp 199–206

  12. 12.

    Zhong W, Zhuang Y, Sun J et al (2018) A load prediction model for cloud computing using PSO-based weighted wavelet support vector machine. Appl Intell 48(11):4072–4083

  13. 13.

    Yang Q, Zhou Y, Yu Y et al (2015) Multi-step-ahead host load prediction using autoencoder and echo state networks in cloud computing. J Supercomput 71(8):3037–3053

  14. 14.

    Cortés-Mendoza JM, Tchernykh A, Bychkov I et al (2017) Load-aware strategies for cloud-based VoIP optimization with VM startup prediction. In: 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, pp 472–481

  15. 15.

    Bala A, Chana I (2016) Prediction-based proactive load balancing approach through VM migration. Eng Comput 32(4):581–592

  16. 16.

    Lu S, Fang Z, Wu J et al (2017) Elastic scaling of virtual clusters in cloud data center networks. In: 2017 IEEE 36th International Performance Computing and Communications Conference (IPCCC). IEEE, pp 1–8

  17. 17.

    Yu H, Yang J, Fung C et al (2018) ENSC: multi-resource hybrid scaling for elastic network service chain in clouds. In: 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, pp 34–41

  18. 18.

    Goswami B, Sarkar J, Saha S et al (2019) ALVEC: Auto-scaling by Lotka Volterra elastic cloud: a QoS aware non linear dynamical allocation model. Simul Model Pract Theory 93:262–292

  19. 19.

    Fe I, Matos R, Dantas J et al (2017) Stochastic model of performance and cost for auto-scaling planning in public cloud. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, pp 2081–2086

  20. 20.

    Lombardi F, Aniello L, Bonomi S et al (2017) Elastic symbiotic scaling of operators and resources in stream processing systems. IEEE Trans Parallel Distrib Syst 29(3):572–585

  21. 21.

    Ficco M, Esposito C, Palmieri F et al (2018) A coral-reefs and game theory-based approach for optimizing elastic cloud resource allocation. Future Gener Comput Syst 78:343–352

  22. 22.

    Sahni J, Vidyarthi DP (2017) Heterogeneity-aware adaptive auto-scaling heuristic for improved QoS and resource usage in cloud environments. Computing 99(4):351–381

  23. 23.

    Benifa JVB, Dejey D (2019) RLPAS: reinforcement learning-based proactive auto-scaler for resource provisioning in cloud environment. Mob Netw Appl 24(4):1348–1363

  24. 24.

    Guo Y, Stolyar A, Walid A (2018) Online VM auto-scaling algorithms for application hosting in a cloud. In: IEEE Transactions on Cloud Computing. IEEE. https://doi.org/10.1109/TCC.2018.2830793

  25. 25.

    Krishna B, Amarawat G (2019) Data mining in frequent pattern matching using improved apriori algorithm. In: Abraham A, Dutta P, Mandal J, Bhattacharya A, Dutta S (eds) Emerging technologies in data mining and information security. Springer, Singapore, pp 699–709

  26. 26.

    Xiahou J, Lin F, Huang QH et al (2018) Multi-datacenter cloud storage service selection strategy based on AHP and backward cloud generator model. Neural Comput Appl 29(1):71–85

  27. 27.

    Li C, Wang YP, Tang H et al (2019) Dynamic multi-objective optimized replica placement and migration strategies for SaaS applications in edge cloud. Future Gener Comput Syst 100:921–937

  28. 28.

    Li C, Bai J, Tang JH (2019) Joint optimization of data placement and scheduling for improving user experience in edge computing. J Parallel Distrib Comput 125:93–105

  29. 29.

    Li C, Tang J, Tang H et al (2019) Collaborative cache allocation and task scheduling for data-intensive applications in edge computing environment. Future Gener Comput Syst 95:249–264

  30. 30.

    Lima S, Rocha Á, Roque L (2019) An overview of OpenStack architecture: a message queuing services node. Clust Comput 22(3):7087–7098

  31. 31.

    Bi J, Zhang L, Yuan H et al (2018) Hybrid task prediction based on wavelet decomposition and ARIMA model in cloud data center. In: 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC). IEEE, pp 1–6

  32. 32.

    He Q, Shahabi H, Shirzadi A et al (2019) Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF network machine learning algorithms. Sci Total Environ 663:1–15

Download references

Acknowledgements

The work was supported by the National Natural Science Foundation (NSF) under Grants (Nos. 61672397, 61873341), Application Foundation Frontier Project of WuHan (No. 2018010401011290), open fund of Anhui Province Key Laboratory of Big Data Analysis and Application. Any opinions, findings and conclusions are those of the authors and do not necessarily reflect the views of the above agencies.

Author information

Correspondence to Chunlin Li.

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

Verify currency and authenticity via CrossMark

Cite this article

Li, C., Tang, J. & Luo, Y. Elastic edge cloud resource management based on horizontal and vertical scaling. J Supercomput (2020). https://doi.org/10.1007/s11227-020-03192-3

Download citation

Keywords

  • Edge cloud
  • Load prediction
  • Elastic scaling
  • Cloud model
  • Integer programming algorithm