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.
- 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 Scholar
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 Scholar
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarCross Ref
- 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 Scholar
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- Luiz André Barroso and Urs Hölzle. 2007. The case for energy-proportional computing. Computer 40, 12 (2007). Google ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 Scholar
- 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 ScholarDigital Library
- 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 Scholar
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 Scholar
- 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 Scholar
- Nicola Jones. 2018. How to stop data centres from gobbling up the world’s electricity.Nature 561, 7722 (2018), 163–167.Google Scholar
- 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 ScholarCross Ref
- 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 Scholar
- Barbara Kitchenham. 2004. Procedures for performing systematic reviews. Keele, UK, Keele University 33, 2004 (2004), 1–26.Google Scholar
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 Scholar
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 Scholar
- 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 ScholarCross Ref
- Babar Nazir et al. 2018. QoS-aware VM placement and migration for hybrid cloud infrastructure. J. Supercomput. 74, 9 (2018), 4623–4646. Google ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- Chitu Okoli. 2015. A guide to conducting a standalone systematic literature review. Commun. Assoc. Inf. Syst. 37 (2015).Google Scholar
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- Shekhar Srikantaiah, Aman Kansal, and Feng Zhao. 2008. Energy-aware consolidation for cloud computing. Clust. Comput. 12 (11 2008), 1–5.Google Scholar
- 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 Scholar
- 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 ScholarCross Ref
- Paolo Toth and Silvano Martello. 1990. Knapsack Problems: Algorithms and Computer Implementations. Wiley.Google Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- John Vidal. 2017. Tsunami of data could consume one fifth of global electricity by 2025. Clim. Home News 11 (2017).Google Scholar
- Hui Wang and Huaglory Tianfield. 2018. Energy-aware dynamic virtual machine consolidation for cloud datacenters. IEEE Access 6 (2018), 15259–15273.Google ScholarCross Ref
- 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 Scholar
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 Scholar
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarCross Ref
Index Terms
- A Systematic Literature Review on Virtual Machine Consolidation
Recommendations
Bayesian network-based Virtual Machines consolidation method
Efficient Virtual Machines (VMs) consolidation, as one of the primary methods for balancing between guaranteeing Quality of Service (QoS) and saving energy, is critical for data centers. Most existing VMs consolidation methods reallocate physical ...
A Technical Review for Efficient Virtual Machine Migration
CUBE '13: Proceedings of the 2013 International Conference on Cloud & Ubiquitous Computing & Emerging TechnologiesThis paper presents the recent technical research survey on the efficient live migration of virtual machines. Virtual machine migration is required for many reasons like load balancing, energy reduction, dynamic resizing, and to increase availability. ...
Performance Analysis for Pareto-Optimal Green Consolidation Based on Virtual Machines Live Migration
Huge energy requirement of cloud data centers is prime concern. Dynamic Virtual Machine VM consolidation based on VM live migration to switched-off or put some of the under-loaded host Physical Machines PMs into a low power consumption mode can ...
Comments