Abstract
The power consumption of datacenters is multiplying, and several survey reports stated that the power consumption of datacenters will reach approximately 8000 TWh by 2030 if do not utilize cloud resources effectively. To use allocated cloud resources effectively, one of the prominent solutions is the VM consolidation technique. VM consolidation technique manages cloud resources effectively while simultaneously satisfying the objectives of cloud users and providers. Additionally, it helps to increase servers’ performance while reducing the high power consumption of datacenters. However, unnecessary actions of VM consolidation technique cause unsuitable VM selection and inappropriate VM placement, which degrades resource management performance, poor QoS, and SLA violations. To overcome this issue, this paper proposed a resource, SLA, power-aware proactive VM consolidation technique by using an improved LSTM network to manage the allocated resources effectively. The proposed proactive VM consolidation technique helps reduce the high power consumption of datacenters while maximizing resource management performance and avoiding SLA violations. Finally, the authors measure the proposed methodology effectiveness by considering the benchmark dataset of NASA servers, and experimental results proved that an improved LSTM network can able to achieve an average accuracy rate of up to 94% with minimum prediction error rate. Proactive VM consolidation technique minimized nearly 30% of the power consumption of datacenters compared with conventional VM consolidation technique.
















Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability
The dataset that support the finding of this study is available for everyone. This dataset is generated by NASA space research centre and also approved for public use.
References
Shyam GK, Manvi SS (2016) Virtual resource prediction in cloud environment: a Bayesian approach. J Netw Comput Appl 65:144–154
Galante G, de Bona LCE (2012) A survey on cloud computing elasticity. In: 2012 IEEE fifth international conference on utility and cloud computing. IEEE, pp 263–270
Ban T, Zhang R, Pang S, Sarrafzadeh A, Inoue D (2013) Referential k NN regression for financial time series forecasting. In: Neural information processing: 20th international conference, ICONIP 2013, Daegu, Korea, November 3–7 2013 proceedings, part I 20. Springer Berlin Heidelberg, pp 601–608
Huang D, He B, Miao C (2014) A survey of resource management in multi-tier web applications. IEEE Commun Surv Tutor 16(3):1574–1590
CISCO Global Cloud Index (2018). https://www.cisco.com/c/en/us/solutions/executive-perspectives/annual-internet-report/index.html. Accessed 18 March 2023
Amiri M, Mohammad-Khanli L (2017) Survey on prediction models of applications for resources provisioning in cloud. J Netw Comput Appl 82:93–113
Kumar KD, Umamaheswari E (2018) Prediction methods for effective resource provisioning in cloud computing: a survey. Multiagent Grid Syst 14(3):283–305
Singh S, Chana I (2015) QoS-aware autonomic resource management in cloud computing: a systematic review. ACM Comput Surv (CSUR) 48(3):1–46
Huebscher MC, McCann JA (2008) A survey of autonomic computing—degrees, models, and applications. ACM Comput Surv (CSUR) 40(3):1–28
Arora S, Bala A (2020) A survey: ICT enabled energy efficiency techniques for big data applications. Clust Comput 23:775–796
Abdelsamea A, El-Moursy AA, Hemayed EE, Eldeeb H (2017) Virtual machine consolidation enhancement using hybrid regression algorithms. Egypt Inform J 18(3):161–170
Alkhanak EN, Lee SP, Rezaei R, Parizi RM (2016) Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: A review, classifications, and open issues. J Syst Softw 113:1–26
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
Zhang W, Li B, Zhao D, Gong F, Lu Q (2016) Workload prediction for cloud cluster using a recurrent neural network. In: 2016 International conference on identification, information and knowledge in the internet of things (IIKI). IEEE, pp 104–109
Kumar KD, Umamaheswari E (2020) Hpcwmf: a hybrid predictive cloud workload management framework using improved LSTM neural network. Cybern Inf Technol 20(4):55–73
Hu Y, Deng B, Peng F, Wang D (2016) Workload prediction for cloud computing elasticity mechanism. In: 2016 IEEE international conference on cloud computing and big data analysis (ICCCBDA). IEEE, pp 244–249
Vakilinia S, Heidarpour B, Cheriet M (2016) Energy efficient resource allocation in cloud computing environments. IEEE Access 4:8544–8557
Hemmat RA., Hafid A (2016) SLA violation prediction in cloud computing: a machine learning perspective. arXiv:1611.10338
Yu Y, Jindal V, Yen IL, Bastani F (2016) Integrating clustering and learning for improved workload prediction in the cloud. In: 2016 IEEE 9th international conference on cloud computing (CLOUD). IEEE, pp 876–879
Tang X, Liao X, Zheng J, Yang X (2018) Energy efficient job scheduling with workload prediction on cloud data center. Clust Comput 21:1581–1593
Kumar J, Singh AK (2018) Workload prediction in cloud using artificial neural network and adaptive differential evolution. Future Gener Comput Syst 81:41–52
Qazi K, Aizenberg I (2018) Cloud datacenter workload prediction using complex-valued neural networks. In: 2018 IEEE second international conference on data stream mining and processing (DSMP). IEEE, pp 315–321
Ardagna D, Barbierato E, Evangelinou A, Gianniti E, Gribaudo M, Pinto TB, Almeida JM (2018) Performance prediction of cloud-based big data applications. In: Proceedings of the 2018 ACM/SPEC international conference on performance engineering, pp 192–199
Upadhyay PK, Pandita A, Joshi N (2019) Scaled conjugate gradient backpropagation based sla violation prediction in cloud computing. In: 2019 International conference on computational intelligence and knowledge economy (ICCIKE). IEEE, pp 203–208
Gao J, Wang H, Shen H (2020) Machine learning based workload prediction in cloud computing. In: 2020 29th International conference on computer communications and networks (ICCCN). IEEE, pp 1–9
Kumar KD, Umamaheswari E (2019) Ewptnn: an efficient workload prediction model in cloud computing using two-stage neural networks. Procedia Comput Sci 165:151–157
Xu M, Song C, Wu H, Gill SS, Ye K, Xu C (2022) esDNN: deep neural network based multivariate workload prediction in cloud computing environments. ACM Trans Internet Technol (TOIT) 22(3):1–24
Patel YS, Bedi J (2023) MAG-D: A multivariate attention network based approach for cloud workload forecasting. Future Gener Comput Syst 142:376–392
Alqahtani D (2023) Leveraging sparse auto-encoding and dynamic learning rate for efficient cloud workloads prediction. IEEE Access 11:64586–64599. https://doi.org/10.1109/ACCESS.2023.3289884
Bao L, Yang J, Zhang Z, Liu W, Chen J, Wu C (2023) On accurate prediction of cloud workloads with adaptive pattern mining. J Supercomput 79(1):160–187
Rossi A, Visentin A, Prestwich S, Brown KN (2022) Bayesian uncertainty modelling for cloud workload prediction. In: 2022 IEEE 15th international conference on cloud computing (CLOUD). IEEE pp 19–29
Amekraz Z, Hadi MY (2022) CANFIS: a chaos adaptive neural fuzzy inference system for workload prediction in the cloud. IEEE Access 10:49808–49828
Liu C, Jiao J, Li W, Wang J, Zhang J (2022) Tr-predictior: an ensemble transfer learning model for small-sample cloud workload prediction. Entropy 24(12):1770
Prasad VK, Bhavsar MD (2020) Monitoring and prediction of SLA for IoT based cloud. Scalable Comput Pract Exp 21(3):349–358
Kaur G, Bala A, Chana I (2019) An intelligent regressive ensemble approach for predicting resource usage in cloud computing. J Parallel Distrib Comput 123:1–12
Rahmanian AA, Ghobaei-Arani M, Tofighy S (2018) A learning automata-based ensemble resource usage prediction algorithm for cloud computing environment. Future Gener Comput Syst 79:54–71
Deepika T, Prakash P (2020) Power consumption prediction in cloud data center using machine learning. Int J Electr Comput Eng (IJECE) 10(2):1524–1532
Chang BJ, Lee YW, Liang YH (2018) Reward-based Markov chain analysis adaptive global resource management for inter-cloud computing. Future Gener Comput Syst 79:588–603
Mavrovouniotis M, Li C, Yang S (2017) A survey of swarm intelligence for dynamic optimization: algorithms and applications. Swarm Evol Comput 33:1–17
NASA Dataset (1995) ftp://ita.ee.lbl.gov/html/contrib/NASA-HTTP.html. Accessed 28 Aug 2020
Funding
The authors received no specific funding for this study.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declared that they have no conflict of interest.
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
Dinesh Kumar, K., Umamaheswari, E. An efficient proactive VM consolidation technique with improved LSTM network in a cloud environment. Computing 106, 1–28 (2024). https://doi.org/10.1007/s00607-023-01214-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00607-023-01214-5