Abstract
Cloud computing is an on-demand technology for several IT infrastructures due to many of its aspects. One such aspect is virtualization, which is used for providing platform to deal with resource utilization, workloads, etc. Large data centers emit enormous amount of energy, and virtual machine consolidation is an effective technique to reduce the carbon footprints of data centers. VM consolidation accommodates virtual machines into a less number of physical machines and puts an underutilized server to hibernation mode. This paper contributes novel taxonomy of virtual machine consolidation techniques. We have derived the comparison matrices which represents the comparative analysis of performance matrix, issues resolved and mathematical models used by different VM consolidation techniques for making efficient consolidation decisions. This survey will also be helpful to the researchers intending to work for the development of decision support system for energy consumption minimization and to achieve good quality of service.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alsadie D, Tari Z, Alzahrani EJ, Alshammari A (2018) LIFE-MP: online virtual machine consolidation with multiple resource usages in cloud environments. In: Hacid H, Cellary W, Wang H, Paik HY, Zhou R (eds) Web Information Systems Engineering–WISE 2018. Lecture notes in computer science, vol 11234, WISE 2018. Springer, Cham
Dutta N, Misra IS (2014) Multilayer hierarchical model for mobility management in IPv6: a mathematical exploration. Wirel Pers Commun 78(2):1413–1439, Springer
Dutta N, Sarma HKD, Polkowski Z (2018) Cluster based routing in cognitive radio Adhoc networks: reconnoitering SINR and ETT impact on clustering. Com Com 115:10–20, Elsevier
Dutta N, Sarma HKD (2017) A probability based stable routing for cognitive radio Adhoc networks. Wire Net 23(1):65–78, Springer
Mohiuddin, Almogren A (2018) Workload-aware VM consolidation method in edge/cloud computing for IoT applications. J Parall Distrib Comput 123:204–214
Sotiriadis S, Bessis N, Buyya R (2018) Self-managed virtual machine scheduling in Cloud systems. Inf Sci 433–434:381–400
Guo W, Kuang P, Jiang Y, Xu X, Tian W (2019) SAVE: self-adaptive consolidation of virtual machines for energy efficiency of CPU-intensive applications in the cloud. J Supercomput 70(121):1–25
Shaw R, Howley E, Barrett (2019) An energy-efficient anti-correlated virtual machine placement algorithm using resource usage predictions. J Model Simul Cloud Comput Big Data 93:322–342
Farhadian MK, Rezazadeh J, Farahbakhsh R, Sandrasegaran K (2019) An efficient IoT cloud energy consumption based on genetic algorithm. J Dig Commun Netw
Wang JV, Cheng C-T, Tse CK (2019) A thermal-aware VM consolidation mechanism with outage avoidance. PractExper, Soft, pp 1–15
Shaw R, Howley E, Barrett E (2017) An advanced reinforcement learning approach for energy-aware virtual machine consolidation in cloud data centers. In: The 12th IEEE international conference for internet technology and secured transactions, Cambridge, UK, 2017
Farahnakian F, Pahikkala T, Liljeberg P, Plosila J, Trung Hieu N, Tenhunen H (2016) Energy-aware VM consolidation in cloud data centers using utilization prediction model. IEEE Trans Cloud Comput XX(X)
Haghshenas K, Pahlevan A, Zapater M, Mohammadi S, Atienza D (2019) MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers. IEEE Trans Serv Comput, pp 1–1
Zhou Z et al (2018) Minimizing SLA violation and power consumption in cloud data centers using adaptive energy-aware algorithms. J Future Gener Comput Syst 86:836–850
Alharbi F, Tian Y, Tang M, Zhang W, Peng C, Fei M (2019) An ant colony system for energy-efficient dynamic virtual machine placement in data centers. J Exp Syst Appl 120:228–238
Sharma Y, Si W, Sun D et al (2018) Failure-aware energy-efficient VM consolidation in cloud computing systems. Future Gener Comput Syst (2018)
Cao G (2019) Topology-aware multi-objective virtual machine dynamic consolidation for cloud datacenter. Elsevier. https://doi.org/10.1016/j.suscom.2019.01.004
Askarizade Haghighi M, Maeen M, Haghpar M (2019) An energy-efficient dynamic resource management approach based on clustering and meta-heuristic algorithms in cloud computing IaaS platforms. Int J Wirel Personal Commun 104(4):1367–1391
Shaw R, Howley E, Barrett E (2019) A predictive anti-correlated virtual machine placement algorithm for green cloud computing. In: The 11th IEEE international conference on Utility and Cloud Computing (UCC), Zurich, Switzerland, 2019
Moges F, Abebe S (2019) Energy-aware VM placement algorithms for the Open Stack Neat consolidation framework. J Cloud Comput 8(1)
Bloch T, Sridharan R, Prashanth C (2014) Analysis and survey of issues in live virtual machine migration interferences. Int J Adv Netw Appl (IJANA)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Sureja, S., Bloch, T. (2020). Classification of Virtual Machine Consolidation Techniques: A Survey. In: M. Thampi, S., et al. Applied Soft Computing and Communication Networks. ACN 2019. Lecture Notes in Networks and Systems, vol 125. Springer, Singapore. https://doi.org/10.1007/978-981-15-3852-0_11
Download citation
DOI: https://doi.org/10.1007/978-981-15-3852-0_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3851-3
Online ISBN: 978-981-15-3852-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)