skip to main content
survey

A Systematic Literature Review on Virtual Machine Consolidation

Published:04 October 2021Publication History
Skip Abstract Section

Abstract

Virtual machine consolidation has been a widely explored topic in recent years due to Cloud Data Centers’ effect on global energy consumption. Thus, academia and companies made efforts to achieve green computing, reducing energy consumption to minimize environmental impact. By consolidating Virtual Machines into a fewer number of Physical Machines, resource provisioning mechanisms can shutdown idle Physical Machines to reduce energy consumption and improve resource utilization. However, there is a tradeoff between reducing energy consumption while assuring the Quality of Service established on the Service Level Agreement. This work introduces a Systematic Literature Review of one year of advances in virtual machine consolidation. It provides a discussion on methods used in each step of the virtual machine consolidation, a classification of papers according to their contribution, and a quantitative and qualitative analysis of datasets, scenarios, and metrics.

References

  1. D. Alsadie, E. J. Alzahrani, N. Sohrabi, Z. Tari, and A. Y. Zomaya. 2018. DFTA: A dynamic threshold-based fuzzy approach for power-efficient VM consolidation. In IEEE 17th International Symposium on Network Computing and Applications (NCA). IEEE, 1–9.Google ScholarGoogle Scholar
  2. Samah Alshathri, Bogdan Ghita, and Nathan Clarke. 2018. Sharing with live migration energy optimization scheduler for cloud computing data centers. Fut. Internet 10, 9 (2018), 86.Google ScholarGoogle ScholarCross RefCross Ref
  3. Albino Altomare, Eugenio Cesario, and Andrea Vinci. 2019. Data analytics for energy-efficient clouds: design, implementation and evaluation. Int. J. Parallel, Emerg. Distrib. Syst. 34, 6 (2019), 690–705.Google ScholarGoogle ScholarCross RefCross Ref
  4. Ehsan Arianyan, Hassan Taheri, Saeed Sharifian, and Mohsen Tarighi. 2018. New six-phase on-line resource management process for energy and sla efficient consolidation in cloud data centers. Int. Arab J. Inf. Technol. 15, 1 (2018), 10–20.Google ScholarGoogle Scholar
  5. Hamdani Arif and Prasan Kumar Sahoo. 2018. Threshold scheme approach to balance virtual machines load in private cloud. In International Conference on Applied Engineering (ICAE). IEEE, 1–6.Google ScholarGoogle ScholarCross RefCross Ref
  6. P. Arroba, J. M. Moya, J. L. Ayala, and R. Buyya. 2015. DVFS-aware consolidation for energy-efficient clouds. In International Conference on Parallel Architecture and Compilation (PACT). 494–495. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Azra Aryania, Hadi S. Aghdasi, and Leyli Mohammad Khanli. 2018. Energy-aware virtual machine consolidation algorithm based on ant colony system. J. Grid Comput. 16, 3 (2018), 477–491. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Ashu, A. Kaur, M. Singh, and P. Singh. 2017. A taxonomy, survey on placement of virtual machines in cloud. In International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). 2054–2058.Google ScholarGoogle Scholar
  9. Emna Baccour, Sebti Foufou, Ridha Hamila, and Aiman Erbad. 2019. Green data center networks: A holistic survey and design guidelines. In 15th International Wireless Communications & Mobile Computing Conference (IWCMC). IEEE, 1108–1114.Google ScholarGoogle ScholarCross RefCross Ref
  10. Wei-Hua Bai, Jian-Qing Xi, Jia-Xian Zhu, and Shao-Wei Huang. 2015. Performance analysis of heterogeneous data centers in cloud computing using a complex queuing model. Math. Prob. Eng. 2015 (2015).Google ScholarGoogle Scholar
  11. Vinayak Bajoria, Avita Katal, and Yash Agarwal. 2018. An energy aware policy for mapping and migrating virtual machines in cloud environment using migration factor. In 8th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 1–5.Google ScholarGoogle ScholarCross RefCross Ref
  12. Paul Barham, Boris Dragovic, Keir Fraser, Steven Hand, Tim Harris, Alex Ho, Rolf Neugebauer, Ian Pratt, and Andrew Warfield. 2003. Xen and the art of virtualization. In ACM SIGOPS Operating Systems Review, Vol. 37/5. ACM, 164–177. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Esha Barlaskar, Yumnam Jayanta Singh, and Biju Issac. 2018. Enhanced cuckoo search algorithm for virtual machine placement in cloud data centers. Int. J. Grid Util. Comput. 9, 1 (2018), 1–17. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Luiz André Barroso and Urs Hölzle. 2007. The case for energy-proportional computing. Computer 40, 12 (2007). Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Anton Beloglazov, Jemal Abawajy, and Rajkumar Buyya. 2012. Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing. Fut. Gen. Comput. Syst. 28, 5 (2012), 755–768. DOI:https://doi.org/10.1016/j.future.2011.04.017. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Anton Beloglazov and Rajkumar Buyya. 2010. Energy efficient allocation of virtual machines in cloud data centers. In 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing. IEEE, 577–578. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Anton Beloglazov and Rajkumar Buyya. 2012. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concur. Comput. Pract. Exper. 24, 13 (2012), 1397–1420. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Belen Bermejo, Carlos Juiz, and Carlos Guerrero. 2019. Virtualization and consolidation: A systematic review of the past 10 years of research on energy and performance. J. Supercomput. 75, 2 (2019), 808–836. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. B. Beyer, C. Jones, J. Petoff, and N. R. Murphy. 2016. Site Reliability Engineering: How Google Runs Production Systems. O’Reilly Media. Retrieved from https://books.google.com.br/books?id=_4rPCwAAQBAJ. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Amir Hossein Borhani, Terence Hung, Bu-Sung Lee, and Zheng Qin. 2019. Power-network aware VM migration heuristics for multi-tier web applications. Clust. Comput. 22, 3 (2019), 757–782.Google ScholarGoogle ScholarCross RefCross Ref
  21. Dinh-Mao Bui, Eui-Nam Huh, and Sungyoung Lee. 2018. Optimizing power consumption in cloud computing based on optimization and predictive analysis. In 12th International Conference on Ubiquitous Information Management and Communication. 1–6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Rajkumar Buyya, Satish Narayana Srirama, Giuliano Casale, Rodrigo Calheiros, Yogesh Simmhan, Blesson Varghese, Erol Gelenbe, Bahman Javadi, Luis Miguel Vaquero, Marco A. S. Netto, Adel Nadjaran Toosi, Maria Alejandra Rodriguez, Ignacio M. Llorente, Sabrina De Capitani Di Vimercati, Pierangela Samarati, Dejan Milojicic, Carlos Varela, Rami Bahsoon, Marcos Dias De Assuncao, Omer Rana, Wanlei Zhou, Hai Jin, Wolfgang Gentzsch, Albert Y. Zomaya, and Haiying Shen. 2018. A manifesto for future generation cloud computing: Research directions for the next decade. ACM Comput. Surv. 51, 5 (Nov. 2018). DOI:https://doi.org/10.1145/3241737. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Hao Cao, Hongguang Sun, Min Sheng, Yan Shi, and Jiandong Li. 2018. A QoS-guaranteed energy-efficient VM dynamic migration strategy in cloud data centers. In 10th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 1–6.Google ScholarGoogle ScholarCross RefCross Ref
  24. Yaohui Chang, Chunhua Gu, Fei Luo, Guisheng Fan, and Wenhao Fu. 2018. Energy efficient resource selection and allocation strategy for virtual machine consolidation in cloud datacenters. IEICE Trans. Inf. Syst. 101, 7 (2018), 1816–1827.Google ScholarGoogle ScholarCross RefCross Ref
  25. Eli Cortez, Anand Bonde, Alexandre Muzio, Mark Russinovich, Marcus Fontoura, and Ricardo Bianchini. 2017. Resource central: Understanding and predicting workloads for improved resource management in large cloud platforms. In 26th Symposium on Operating Systems Principles. 153–167. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Pintea Cristian, Pintea Eugen, Antal Marcel, Claudia Pop, Tudor Cioara, Ionut Anghel, and Ioan Salomie. 2018. CoolCloudSim: Integrating cooling system models in cloudsim. In IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 387–394.Google ScholarGoogle ScholarCross RefCross Ref
  27. Mustafa Daraghmeh, Suhib Bani Melhem, Anjali Agarwal, Nishith Goel, and Marzia Zaman. 2018. Linear and logistic regression based monitoring for resource management in cloud networks. In IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud). IEEE, 259–266.Google ScholarGoogle ScholarCross RefCross Ref
  28. Nabila Djennane, Rachida Aoudjit, and Samia Bouzefrane. 2018. Energy-efficient algorithm for load balancing and VMs reassignment in data centers. In 6th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW). IEEE, 225–230.Google ScholarGoogle ScholarCross RefCross Ref
  29. John Dulac, Thibaut Abergel, and Chiara Delmastro. 2020. Data Centres and Data Transmission Networks. Retrieved from https://www.iea.org/reports/data-centres-and-data-tr ansmission-networks.Google ScholarGoogle Scholar
  30. Xiaobo Fan, Wolf-Dietrich Weber, and Luiz Andre Barroso. 2007. Power provisioning for a warehouse-sized computer. In ACM SIGARCH Computer Architecture News, Vol. 35/2. ACM, 13–23. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Fahimeh Farahnakian, Tapio Pahikkala, Pasi Liljeberg, Juha Plosila, Nguyen Trung Hieu, and Hannu Tenhunen. 2016. Energy-aware VM consolidation in cloud data centers using utilization prediction model. IEEE Trans. Cloud Comput. 7, 2 (2016), 524–536.Google ScholarGoogle ScholarCross RefCross Ref
  32. Mohammad H. Fathi and Leyli M. Khanli. 2018. consolidating VMs in green cloud computing using harmony search algorithm. In International Conference on Internet and e-Business. 146–151. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Mostafa Ghobaei-Arani, Ali Asghar Rahmanian, Mahboubeh Shamsi, and Abdolreza Rasouli-Kenari. 2018. A learning-based approach for virtual machine placement in cloud data centers. Int. J. Commun. Syst. 31, 8 (2018), e3537.Google ScholarGoogle ScholarCross RefCross Ref
  34. Madnesh K. Gupta, Ankit Jain, and Tarachand Amgoth. 2018. Power and resource-aware virtual machine placement for IaaS cloud. Sustain. Comput.: Inform. Syst. 19 (2018), 52–60.Google ScholarGoogle Scholar
  35. Maryam Askarizade Haghighi, Mehrdad Maeen, and Majid Haghparast. 2019. An energy-efficient dynamic resource management approach based on clustering and meta-heuristic algorithms in cloud computing IaaS platforms. Wirel. Person. Commun. 104, 4 (2019), 1367–1391. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Kawsar Haghshenas, Ali Pahlevan, Marina Zapater, Siamak Mohammadi, and David Atienza. 2019. Magnetic: Multi-agent machine learning-based approach for energy efficient dynamic consolidation in data centers. IEEE Trans. Serv. Comput. [Preprint] (2019). Available from: https://doi.org/10.1109/TSC.2019.2919555Google ScholarGoogle Scholar
  37. Najet Hamdi and Walid Chainbi. 2019. A survey on energy aware VM consolidation strategies. Sustain. Comput.: Inform. Syst. 23 (2019), 80–87. DOI:https://doi.org/10.1016/j.suscom.2019.06.003.Google ScholarGoogle Scholar
  38. Guangjie Han, Wenhui Que, Gangyong Jia, and Wenbo Zhang. 2018. Resource-utilization-aware energy efficient server consolidation algorithm for green computing in IIOT. J. Netw. Comput. Applic. 103 (2018), 205–214. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Fabien Hermenier, Nicolas Loriant, and Jean-Marc Menaud. 2006. Power management in grid computing with Xen. In Frontiers of High Performance Computing and Networking – ISPA 2006 Workshops, Geyong Min, Beniamino Di Martino, Laurence T. Yang, Minyi Guo, and Gudula Rünger (Eds.). Springer Berlin, 407–416. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. I. Hwang and M. Pedram. 2018. Hierarchical, Portfolio theory-based virtual machine consolidation in a compute cloud. IEEE Trans. Serv. Comput. 11, 01 (1 2018), 63–77. DOI:https://doi.org/10.1109/TSC.2016.2531672.Google ScholarGoogle ScholarCross RefCross Ref
  41. Kudamaduwage Pubudu Nuwanthika Jayasena, Lin Li, Mohamed Abd Elaziz, and Shengwu Xiong. 2018. Multi-objective energy efficient resource allocation using virus colony search (VCS) algorithm. In IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 766–773.Google ScholarGoogle Scholar
  42. K. P. N. Jayasena, L. Li, M. A. Elaziz, S. Xiong, and J. Xiang. 2018. Optimizing the energy efficient VM consolidation by a multi-objective algorithm. In IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 81–86.Google ScholarGoogle Scholar
  43. Nicola Jones. 2018. How to stop data centres from gobbling up the world’s electricity.Nature 561, 7722 (2018), 163–167.Google ScholarGoogle Scholar
  44. Mohamed Amine Kaaouache and Sadok Bouamama. 2018. An energy-efficient VM placement method for cloud data centers using a hybrid genetic algorithm. J. Syst. Inf. Technol. 20, 4 (2018), 430–445.Google ScholarGoogle ScholarCross RefCross Ref
  45. Md Anit Khan, Andrew Paplinski, Abdul Malik Khan, Manzur Murshed, and Rajkumar Buyya. 2018. Dynamic virtual machine consolidation algorithms for energy-efficient cloud resource management: A review. In Sustainable Cloud and Energy Services. Springer, 135–165.Google ScholarGoogle Scholar
  46. Barbara Kitchenham. 2004. Procedures for performing systematic reviews. Keele, UK, Keele University 33, 2004 (2004), 1–26.Google ScholarGoogle Scholar
  47. Ashok Kumar, Rajesh Kumar, and Anju Sharma. 2018. Energy aware resource allocation for clouds using two level ant colony optimization. Comput. Inform. 37, 1 (2018), 76–108.Google ScholarGoogle ScholarCross RefCross Ref
  48. Ashok Kumar, Rajesh Kumar, and Anju Sharma. 2018. Equal: Energy and QoS aware resource allocation approach for clouds. Comput. Inform. 37, 4 (2018), 781–814.Google ScholarGoogle ScholarCross RefCross Ref
  49. H. Li, T. Li, and Z. Shuhua. 2018. Energy-performance optimisation for the dynamic consolidation of virtual machines in cloud computing. Int. J. Serv. Oper. Inform. 9, 1 (2018), 62–82.Google ScholarGoogle Scholar
  50. Lianpeng Li, Jian Dong, Decheng Zuo, and Jin Wu. 2019. SLA-aware and energy-efficient VM consolidation in cloud data centers using robust linear regression prediction model. IEEE Access 7 (2019), 9490–9500.Google ScholarGoogle ScholarCross RefCross Ref
  51. Xiang Li, Peter Garraghan, Xiaohong Jiang, Zhaohui Wu, and Jie Xu. 2018. Holistic virtual machine scheduling in cloud datacenters towards minimizing total energy. IEEE Trans. Parallel Distrib. Syst. 29, 6 (2018), 1317–1331.Google ScholarGoogle ScholarCross RefCross Ref
  52. Zhihua Li. 2019. An adaptive overload threshold selection process using Markov decision processes of virtual machine in cloud data center. Clust. Comput. 22, 2 (2019), 3821–3833.Google ScholarGoogle ScholarCross RefCross Ref
  53. Zhihua Li, Chengyu Yan, Lei Yu, and Xinrong Yu. 2018. Energy-aware and multi-resource overload probability constraint-based virtual machine dynamic consolidation method. Fut. Gen. Comput. Syst. 80 (2018), 139–156. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Ying Liu, Junjie Gao, and Yu Yao. 2017. Research on virtual machine migration algorithm for cloud data center. In International Conference on Computer Systems, Electronics and Control (ICCSEC). IEEE, 1376–1381.Google ScholarGoogle ScholarCross RefCross Ref
  55. Yaqiu Liu, Xinyue Sun, Wei Wei, and Weipeng Jing. 2018. Enhancing energy-efficient and QoS dynamic virtual machine consolidation method in cloud environment. IEEE Access 6 (2018), 31224–31235.Google ScholarGoogle ScholarCross RefCross Ref
  56. Shin-Li Lu and Jen-Hsiang Chen. 2018. Host overloading detection based on EWMA algorithm in cloud computing environment. In IEEE 15th International Conference on e-Business Engineering (ICEBE). IEEE, 274–279.Google ScholarGoogle ScholarCross RefCross Ref
  57. Mohammad-Hossein Malekloo, Nadjia Kara, and May El Barachi. 2018. An energy efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environments. Sustain. Comput.: Inform. Syst. 17 (2018), 9–24.Google ScholarGoogle ScholarCross RefCross Ref
  58. Hardik Mandora, Divyesh Patel, and Nilesh Dubey. 2018. Migration and cooling aware approach for virtual machine spreading in data centers. In 3rd International Conference for Convergence in Technology (I2CT). IEEE, 1–6.Google ScholarGoogle ScholarCross RefCross Ref
  59. Antonio Marotta, Stefano Avallone, and Andreas Kassler. 2018. A joint power efficient server and network consolidation approach for virtualized data centers. Comput. Netw. 130 (2018), 65–80. Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. Mahammad S. Mekala and P. Viswanathan. 2019. Energy-efficient virtual machine selection based on resource ranking and utilization factor approach in cloud computing for IoT. Comput. Electric. Eng. 73 (2019), 227–244.Google ScholarGoogle ScholarCross RefCross Ref
  61. Suhib Bani Melhem, Anjali Agarwal, Nishith Goel, and Marzia Zaman. 2018. Markov prediction model for host load detection and VM placement in live migration. IEEE Access 6 (2018), 7190–7205.Google ScholarGoogle ScholarCross RefCross Ref
  62. Jafar Meshkati and Faramarz Safi-Esfahani. 2019. Energy-aware resource utilization based on particle swarm optimization and artificial bee colony algorithms in cloud computing. J. Supercomput. 75, 5 (2019), 2455–2496. Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. Seyedhamid Mashhadi Moghaddam, Sareh Fotuhi Piraghaj, Michael O’Sullivan, Cameron Walker, and Charles Unsworth. 2018. Energy-efficient and SLA-Aware virtual machine selection algorithm for dynamic resource allocation in cloud data centers. In IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC). IEEE, 103–113.Google ScholarGoogle ScholarCross RefCross Ref
  64. A. R. Mohazabiyeh and K. H. Amirizadeh. 2018. Energy-aware adaptive four thresholds technique for optimal virtual machine placement. Int. J. Electric. Comput. Eng. 8, 5 (2018), 3890.Google ScholarGoogle Scholar
  65. André Monteiro and Orlando Loques. 2019. Quantum virtual machine: Power and performance management in virtualized web servers clusters. Clust. Comput. 22, 1 (2019), 205–221. Google ScholarGoogle ScholarDigital LibraryDigital Library
  66. Abdelkhalik Mosa and Rizos Sakellariou. 2018. Dynamic tuning for parameter-based virtual machine placement. In 17th International Symposium on Parallel and Distributed Computing (ISPDC). IEEE, 38–45.Google ScholarGoogle ScholarCross RefCross Ref
  67. Hanan A. Nadeem and Mai A. Fadel. 2018. Priority-aware virtual machine selection algorithm in dynamic consolidation. Int. J. Adv. Comput. Sci. Applic. 9, 11 (2018), 416–420.Google ScholarGoogle Scholar
  68. Hossein Monshizadeh Naeen, Esmaeil Zeinali, and Abolfazl Toroghi Haghighat. 2018. A stochastic process-based server consolidation approach for dynamic workloads in cloud data centers. J. Supercomput. 76, 3 (2020), 1903–1930.Google ScholarGoogle ScholarCross RefCross Ref
  69. Babar Nazir et al. 2018. QoS-aware VM placement and migration for hybrid cloud infrastructure. J. Supercomput. 74, 9 (2018), 4623–4646. Google ScholarGoogle ScholarDigital LibraryDigital Library
  70. Trung Hieu Nguyen, Mario Di Francesco, and Antti Yla-Jaaski. 2017. Virtual machine consolidation with multiple usage prediction for energy-efficient cloud data centers. IEEE Trans. Serv. Comput. 13, 1 (2017), 186–199.Google ScholarGoogle Scholar
  71. K. A. Nuaimi, N. Mohamed, M. A. Nuaimi, and J. Al-Jaroodi. 2012. A survey of load balancing in cloud computing: challenges and algorithms. In 2nd Symposium on Network Cloud Computing and Applications. 137–142. Google ScholarGoogle ScholarDigital LibraryDigital Library
  72. Chitu Okoli. 2015. A guide to conducting a standalone systematic literature review. Commun. Assoc. Inf. Syst. 37 (2015).Google ScholarGoogle Scholar
  73. Ali Pahlevan, Marina Zapater, Ayse Coskun, and David Atienza. 2020. ECOGreen: Electricity cost optimization for green datacenters in emerging power markets. IEEE Trans. Sustain. Comput. 6, 2 (2020), 289–305.Google ScholarGoogle ScholarCross RefCross Ref
  74. F. L. Pires and B. Barán. 2015. A virtual machine placement taxonomy. In 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. 159–168. Google ScholarGoogle ScholarDigital LibraryDigital Library
  75. Ali Asghar Rahmanian, Abbas Horri, and Gholamhossein Dastghaibyfard. 2018. Toward a hierarchical and architecture-based virtual machine allocation in cloud data centers. Int. J. Commun. Syst. 31, 4 (2018), e3490.Google ScholarGoogle ScholarCross RefCross Ref
  76. Milad Ranjbari and Javad Akbari Torkestani. 2018. A learning automata-based algorithm for energy and SLA efficient consolidation of virtual machines in cloud data centers. J. Parallel Distrib. Comput. 113 (2018), 55–62.Google ScholarGoogle ScholarDigital LibraryDigital Library
  77. Joseph Roque, Lia Chauvel, Moayad Aloqaily, and Burak Kantarci. 2018. A feasibility study on sustainability-driven infrastructure management in cloud data centers. In IEEE Canadian Conference on Electrical & Computer Engineering (CCECE). IEEE, 1–4.Google ScholarGoogle ScholarCross RefCross Ref
  78. Monireh H. Sayadnavard, Abolfazl Toroghi Haghighat, and Amir Masoud Rahmani. 2019. A reliable energy-aware approach for dynamic virtual machine consolidation in cloud data centers. J. Supercomput. 75, 4 (2019), 2126–2147. Google ScholarGoogle ScholarDigital LibraryDigital Library
  79. Neeraj Kumar Sharma, Priyanka Sharma, and Ram Mohana Reddy Guddeti. 2018. Energy efficient quality of service aware virtual machine migration in cloud computing. In 4th International Conference on Recent Advances in Information Technology (RAIT). IEEE, 1–6.Google ScholarGoogle ScholarCross RefCross Ref
  80. Haiying Shen and Liuhua Chen. 2018. CompVM: A complementary VM allocation mechanism for cloud systems. IEEE/ACM Trans. Netw. 26, 3 (2018), 1348–1361. Google ScholarGoogle ScholarDigital LibraryDigital Library
  81. Shekhar Srikantaiah, Aman Kansal, and Feng Zhao. 2008. Energy-aware consolidation for cloud computing. Clust. Comput. 12 (11 2008), 1–5.Google ScholarGoogle Scholar
  82. A. Tarafdar, S. Khatua, and R. K. Das. 2018. QoS aware energy efficient VM consolidation techniques for a virtualized data center. In IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC). IEEE, 114–123.Google ScholarGoogle Scholar
  83. Mehran Tarahomi and Mohammad Izadi. 2019. A prediction-based and power-aware virtual machine allocation algorithm in three-tier cloud data centers. Int. J. Commun. Syst. 32, 3 (2019), e3870.Google ScholarGoogle ScholarCross RefCross Ref
  84. Paolo Toth and Silvano Martello. 1990. Knapsack Problems: Algorithms and Computer Implementations. Wiley.Google ScholarGoogle Scholar
  85. R. Uhlig, G. Neiger, D. Rodgers, A. L. Santoni, F. C. M. Martins, A. V. Anderson, S. M. Bennett, A. Kagi, F. H. Leung, and L. Smith. 2005. Intel virtualization technology. Computer 38, 5 (2005), 48–56. Google ScholarGoogle ScholarDigital LibraryDigital Library
  86. Jitendra Kumar Verma, Sushil Kumar, Omprakash Kaiwartya, Yue Cao, Jaime Lloret, Chittaranjan Padmanabha Katti, and Rupak Kharel. 2018. Enabling green computing in cloud environments: Network virtualization approach toward 5G support. Trans. Emerg. Telecommun. Technol. 29, 11 (2018), e3434.Google ScholarGoogle ScholarDigital LibraryDigital Library
  87. John Vidal. 2017. Tsunami of data could consume one fifth of global electricity by 2025. Clim. Home News 11 (2017).Google ScholarGoogle Scholar
  88. Hui Wang and Huaglory Tianfield. 2018. Energy-aware dynamic virtual machine consolidation for cloud datacenters. IEEE Access 6 (2018), 15259–15273.Google ScholarGoogle ScholarCross RefCross Ref
  89. J. Wang, X. Sun, W. Song, and L. Tang. 2018. Resource scheduling method based on Bayes for cloud computing. J. Inf. Hiding Multimedia Sig. Process. 9 (11 2018), 1444–1451.Google ScholarGoogle Scholar
  90. Joseph Nathanael Witanto, Hyotaek Lim, and Mohammed Atiquzzaman. 2018. Adaptive selection of dynamic VM consolidation algorithm using neural network for cloud resource management. Fut. Gen. Comput. Syst. 87 (2018), 35–42.Google ScholarGoogle ScholarCross RefCross Ref
  91. Lei Xie, Shengbo Chen, Wenfeng Shen, and Huaikou Miao. 2018. A novel self-adaptive VM consolidation strategy using dynamic multi-thresholds in IaaS clouds. Fut. Internet 10, 6 (2018), 52.Google ScholarGoogle ScholarCross RefCross Ref
  92. R. Yadav, W. Zhang, O. Kaiwartya, P. R. Singh, I. A. Elgendy, and Y. Tian. 2018. Adaptive energy-aware algorithms for minimizing energy consumption and SLA violation in cloud computing. IEEE Access 6 (2018), 55923–55936.Google ScholarGoogle ScholarCross RefCross Ref
  93. Rahul Yadav, Weizhe Zhang, Keqin Li, Chuanyi Liu, Muhammad Shafiq, and Nabin Kumar Karn. 2018. An adaptive heuristic for managing energy consumption and overloaded hosts in a cloud data center. Wirel. Netw. 26, 3 (2020), 1905–1919.Google ScholarGoogle ScholarCross RefCross Ref
  94. Hanmin Ye, Zihang Song, and Qianting Sun. 2014. Design of green data center deployment model based on cloud computing and TIA942 heat dissipation standard. In IEEE Workshop on Electronics, Computer and Applications. IEEE, 433–437.Google ScholarGoogle Scholar
  95. F. Zhang, G. Liu, X. Fu, and R. Yahyapour. 2018. A survey on virtual machine migration: challenges, techniques, and open issues. IEEE Commun. Surv. Tutor. 20, 2 (1 2018), 1206–1243.Google ScholarGoogle Scholar
  96. Zhou Zhou, Jemal Abawajy, Morshed Chowdhury, Zhigang Hu, Keqin Li, Hongbing Cheng, Abdulhameed A. Alelaiwi, and Fangmin Li. 2018. Minimizing SLA violation and power consumption in Cloud data centers using adaptive energy-aware algorithms. Fut. Gen. Comput. Syst. 86 (2018), 836–850.Google ScholarGoogle ScholarDigital LibraryDigital Library
  97. Zhou Zhou, Junyang Yu, Fangmin Li, and Fei Yang. 2018. Virtual machine migration algorithm for energy efficiency optimization in cloud computing. Concur. Comput.: Pract. Exper. 30, 24 (2018), e4942.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. A Systematic Literature Review on Virtual Machine Consolidation

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      • Published in

        cover image ACM Computing Surveys
        ACM Computing Surveys  Volume 54, Issue 8
        November 2022
        754 pages
        ISSN:0360-0300
        EISSN:1557-7341
        DOI:10.1145/3481697
        Issue’s Table of Contents

        Copyright © 2021 Association for Computing Machinery.

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 4 October 2021
        • Revised: 1 June 2021
        • Accepted: 1 June 2021
        • Received: 1 September 2020
        Published in csur Volume 54, Issue 8

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • survey
        • Refereed

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format .

      View HTML Format