Login Register Cart 0
Generic placeholder image

Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

An Energy-saving Data Transmission Approach based on Migrating Virtual Machine Technology to Cloud Computing

Author(s): Pundru Chandra Shaker Reddy* and Yadala Sucharitha

Volume 17, Issue 6, 2024

Published on: 22 September, 2023

Page: [573 - 581] Pages: 9

DOI: 10.2174/2352096516666230713163440

Price: $65

Abstract

Introduction: Over the past few years, researchers have greatly focused on increasing the electrical efficiency of large computer systems. Virtual machine (VM) migration helps data centers keep their pages' content updated on a regular basis, which speeds up the time it takes to access data. Offline VM migration is best accomplished by sharing memory without requiring any downtime.

Objective: The objective of the paper was to reduce energy consumption and deploy a unique green computing architecture. The proposed virtual machine is transferred from one host to another through dynamic mobility.

Methodology: The proposed technique migrates the maximally loaded virtual machine to the least loaded active node, while maintaining the performance and energy efficiency of the data centers. Taking into account the cloud environment, the use of electricity could continue to be critical. These large uses of electricity by the internet information facilities that maintain computing capacity are becoming another major concern. Another way to reduce resource use is to relocate the VM.

Results: Using a non-linear forecasting approach, the research presents improved decentralized virtual machine migration (IDVMM) that could mitigate electricity consumption in cloud information warehouses. It minimizes violations of support agreements in a relatively small number of all displaced cases and improves the efficiency of resources.

Conclusion: The proposed approach further develops two thresholds to divide overloaded hosts into massively overloaded hosts, moderately overloaded hosts, and lightly overloaded hosts. The migration decision of VMs in all stages pursues the goal of reducing the energy consumption of the network during the migration process. Given ten months of data, actual demand tracing is done through PlanetLab and then assessed using a cloud service.

« Previous Next »
Graphical Abstract

[1]
T.P. Latchoumi, R. Swathi, P. Vidyasri, and K. Balamurugan, "Develop new algorithm to improve safety on WMSN in health disease monitoring", In 2022, International Mobile and Embedded Technology Conference (MECON), 2022, pp. 357-362
[http://dx.doi.org/10.1109/MECON53876.2022.9752178]
[2]
P.C.S. Reddy, S. Yadala, and S.N. Goddumarri, "Development of rainfall forecasting model using machine learning with singular spectrum analysis", IIUM Engine. J., vol. 23, no. 1, pp. 172-186, 2022.
[http://dx.doi.org/10.31436/iiumej.v23i1.1822]
[3]
L. Liu, M. Shafiq, V.R. Sonawane, M.Y.B. Murthy, P.C.S. Reddy, and K.M.N.C. Reddy, "Spectrum trading and sharing in unmanned aerial vehicles based on distributed blockchain consortium system", Comput. Electr. Eng., vol. 103, p. 108255, 2022.
[http://dx.doi.org/10.1016/j.compeleceng.2022.108255]
[4]
P.R. Garikapati, K. Balamurugan, T.P. Latchoumi, and G. Shankar, A quantitative study of small dataset machining by agglomerative hierarchical cluster and k-Medoid.Emergent Converging Technologies and Biomedical Systems., Springer: Singapore, 2022, pp. 717-727.
[http://dx.doi.org/10.1007/978-981-16-8774-7_59]
[5]
A. Singhal, S. Varshney, T.A. Mohanaprakash, R. Jayavadivel, K. Deepti, P.C.S. Reddy, and M.B. Mulat, "Minimization of latency using multitask scheduling in industrial autonomous systems", Wirel. Commun. Mob. Comput., vol. 2022, pp. 1-10, 2022.
[http://dx.doi.org/10.1155/2022/1671829]
[6]
R. Dhanalakshmi, N.P.G. Bhavani, S.S. Raju, P.C. Shaker Reddy, D. Mavaluru, D.P. Singh, and A. Batu, "Onboard pointing error detection and estimation of observation satellite data using extended kalman filter", Comput. Intell. Neurosci., vol. 2022, pp. 1-8, 2022.
[http://dx.doi.org/10.1155/2022/4340897] [PMID: 36248921]
[7]
J.F. Banu, P. Muneeshwari, K. Raja, S. Suresh, T.P. Latchoumi, and S. Deepan, "Ontology based image retrieval by utilizing model annotations and content", In 12th International Conference on Cloud Computing, Data Science & Engineering, 2022, pp. 300-305
[http://dx.doi.org/10.1109/Confluence52989.2022.9734194]
[8]
K. Ashok, R. Boddu, S.A. Syed, V.R. Sonawane, R.G. Dabhade, and P.C.S. Reddy, "GAN Base feedback analysis system for industrial IOT networks", Automatika, vol. 64, pp. 1-9, 2022.
[9]
L. Sujihelen, R. Boddu, S. Murugaveni, M. Arnika, A. Haldorai, P.C.S. Reddy, S. Feng, and J. Qin, "Node replication attack detection in distributed wireless sensor networks", Wirel. Commun. Mob. Comput., vol. 2022, pp. 1-11, 2022.
[http://dx.doi.org/10.1155/2022/7252791]
[10]
S.M. Fati, A.K. Jaradat, I. Abunadi, and A.S. Mohammed, "Modelling virtual machine workload in heterogeneous cloud computing platforms", J. Inf. Technol. Res., vol. 13, no. 4, pp. 156-170, 2020.
[http://dx.doi.org/10.4018/JITR.20201001.oa1]
[11]
P. Reddy, and A. Sureshbabu, "An adaptive model for forecasting seasonal rainfall using predictive analytics", Int. J. Intell. Syst., vol. 12, no. 5, pp. 22-32, 2019.
[http://dx.doi.org/10.22266/ijies2019.1031.03]
[12]
P.C. Shaker Reddy, and A. Sureshbabu, "An enhanced multiple linear regression model for seasonal rainfall prediction", Int. J. Sensors Wirel. Commun. Control, vol. 10, no. 4, pp. 473-483, 2020.
[http://dx.doi.org/10.2174/2210327910666191218124350]
[13]
A. Jalal, N. Khalid, and K. Kim, "Automatic recognition of human interaction via hybrid descriptors and maximum entropy markov model using depth sensors", Entropy, vol. 22, no. 8, p. 817, 2020.
[http://dx.doi.org/10.3390/e22080817] [PMID: 33286588]
[14]
D. Balamurugan, S.S. Aravinth, P.C.S. Reddy, A. Rupani, and A. Manikandan, "Multiview objects recognition using deep learning-based wrap-cnn with voting scheme", Neural Process. Lett., vol. 54, no. 3, pp. 1495-1521, 2022.
[http://dx.doi.org/10.1007/s11063-021-10679-4]
[15]
P. Geetha, and C.R.R. Robin, "RETRACTED ARTICLE: Power conserving resource allocation scheme with improved QoS to promote green cloud computing", J. Ambient Intell. Humaniz. Comput., vol. 12, no. 7, pp. 7153-7164, 2021.
[http://dx.doi.org/10.1007/s12652-020-02384-2]
[16]
S. Suresh, V. Prabhu, V. Parthasarathy, R. Boddu, Y. Sucharitha, and G. Teshite, "A novel routing protocol for low-energy wireless sensor networks", J. Sens., vol. 2022, pp. 1-8, 2022.
[http://dx.doi.org/10.1155/2022/8244176]
[17]
Y. Sucharitha, Y. Vijayalata, and V.K. Prasad, "Predicting election results from twitter using machine learning algorithms", Recent Adv. Comput. Sci. Commun., vol. 14, no. 1, pp. 246-256, 2021.
[18]
R. Zolfaghari, and A.M. Rahmani, "Virtual machine consolidation in cloud computing systems: Challenges and future trends", Wirel. Pers. Commun., vol. 115, no. 3, pp. 2289-2326, 2020.
[http://dx.doi.org/10.1007/s11277-020-07682-8]
[19]
J. Liu, S. Wang, A. Zhou, J. Xu, and F. Yang, "SLA-driven container consolidation with usage prediction for green cloud computing", Front. Comput. Sci., vol. 14, no. 1, pp. 42-52, 2020.
[http://dx.doi.org/10.1007/s11704-018-7172-3]
[20]
P.C. Shaker Reddy, and Y. Sucharitha, "IoT-enabled energy-efficient multipath power control for underwater sensor networks", Int. J. Sensors Wirel. Commun. Control, vol. 12, no. 6, pp. 478-494, 2022.
[http://dx.doi.org/10.2174/2210327912666220615103257]
[21]
S.Y. Hsieh, C.S. Liu, R. Buyya, and A.Y. Zomaya, "Utilization-prediction-aware virtual machine consolidation approach for energy-efficient cloud data centers", J. Parallel Distrib. Comput., vol. 139, pp. 99-109, 2020.
[http://dx.doi.org/10.1016/j.jpdc.2019.12.014]
[22]
Y. Huang, H. Xu, H. Gao, X. Ma, and W. Hussain, "SSUR: An approach to optimizing virtual machine allocation strategy based on user requirements for cloud data center", IEEE Trans. Green Commun. Netw., vol. 5, no. 2, pp. 670-681, 2021.
[http://dx.doi.org/10.1109/TGCN.2021.3067374]
[23]
M.E. Karim, M.M.S. Maswood, S. Das, and A.G. Alharbi, "BHyPreC: A novel Bi-LSTM based hybrid recurrent neural network model to predict the CPU workload of cloud virtual machine", IEEE Access, vol. 9, pp. 131476-131495, 2021.
[http://dx.doi.org/10.1109/ACCESS.2021.3113714]
[24]
W. Sun, Y. Wang, and S. Li, "An optimal resource allocation scheme for virtual machine placement of deploying enterprise applications into the cloud", AIMS Mathematics, vol. 5, no. 4, pp. 3966-3989, 2020.
[http://dx.doi.org/10.3934/math.2020256]
[25]
C.T. Yang, and T.Y. Wan, "Implementation of an energy saving cloud infrastructure with virtual machine power usage monitoring and live migration on OpenStack", Computing, vol. 102, no. 6, pp. 1547-1566, 2020.
[http://dx.doi.org/10.1007/s00607-020-00808-7]
[26]
D. Saxena, I. Gupta, J. Kumar, A.K. Singh, and X. Wen, "A Secure and Multiobjective Virtual Machine Placement Framework for Cloud Data Center", IEEE Syst. J., 2021.
[27]
S. Azizi, M. Zandsalimi, and D. Li, "An energy-efficient algorithm for virtual machine placement optimization in cloud data centers", Cluster Comput., vol. 23, no. 4, pp. 3421-3434, 2020.
[http://dx.doi.org/10.1007/s10586-020-03096-0]
[28]
E. Parvizi, and M.H. Rezvani, "Utilization-aware energy-efficient virtual machine placement in cloud networks using NSGA-III meta-heuristic approach", Cluster Comput., vol. 23, no. 4, pp. 2945-2967, 2020.
[http://dx.doi.org/10.1007/s10586-020-03060-y]
[29]
T. Deepika, and P. Prakash, "Power consumption prediction in cloud data center using machine learning", Iran. J. Electr. Comput. Eng., vol. 10, no. 2, pp. 1524-1532, 2020.
[30]
S. Farzai, M.H. Shirvani, and M. Rabbani, "Multi-objective communication-aware optimization for virtual machine placement in cloud datacenters", Sustainable Computing: Informatics and Systems, vol. 28, p. 100374, 2020.
[http://dx.doi.org/10.1016/j.suscom.2020.100374]
[31]
S. Gharehpasha, and M. Masdari, "A discrete chaotic multi-objective SCA-ALO optimization algorithm for an optimal virtual machine placement in cloud data center", J. Ambient Intell. Humaniz. Comput., vol. 12, no. 10, pp. 9323-9339, 2021.
[http://dx.doi.org/10.1007/s12652-020-02645-0]
[32]
S. Omer, S. Azizi, M. Shojafar, and R. Tafazolli, "A priority, power and traffic-aware virtual machine placement of IoT applications in cloud data centers", J. Systems Archit., vol. 115, p. 101996, 2021.
[http://dx.doi.org/10.1016/j.sysarc.2021.101996]
[33]
M. Kiani, and M.R. Khayyambashi, "A network-aware and power-efficient virtual machine placement scheme in cloud datacenters based on chemical reaction optimization", Comput. Netw., vol. 196, p. 108270, 2021.
[http://dx.doi.org/10.1016/j.comnet.2021.108270]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy