Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2023, 6(9); doi: 10.25236/AJCIS.2023.060920.

The Virtual Machine Migration Strategy Based on Dynamic Threshold


Jin Yang

Corresponding Author:
Jin Yang

School of Computer Science, Guangdong Business and Technology University, Zhaoqing, Guangdong, China


With the rapid development of cloud computing, virtualization is widely used in data centers for resource management. However, traditional static threshold-based virtual machine migration strategies struggle to adapt to changing workloads. To address this, we propose a dynamic threshold-based strategy that monitors resource utilization to optimize migration timing and reduce costs. Simulation experiments on a real data center confirm the superior performance of our approach compared to static methods. This intelligent and efficient strategy enhances resource management, and energy efficiency in data centers.


Cloud computing, Dynamic thresholds, Energy efficiency

Cite This Paper

Jin Yang. The Virtual Machine Migration Strategy Based on Dynamic Threshold. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 9: 132-137. https://doi.org/10.25236/AJCIS.2023.060920.


[1] Bokhari, Mohammad Ubaidullah, Qahtan Makki, and Yahya Kord Tamandani. "A survey on cloud computing." Big Data Analytics: Proceedings of CSI 2015. Springer Singapore, 2018.

[2] Jin-Jun L , Gui-Lin C , Cheng-Xiang H U. Virtual Machine Migration Scheduling Strategy Based on Load Characteristic[J]. Computer Engineering, 2011, 37(17):276-278. DOI:10.4028/www.scientific. net/AMR.154-155.87.

[3] Mehmood, Tajwar; Latif, Seemab; Malik, Sheheryaar. Prediction of cloud computing resource utilization. In: 2018 15th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT (HONET-ICT). IEEE, 2018. p. 38-42.

[4] Fan X. Weber W. Barroso L.A., 2007a. Power provisioning for a warehouse-sizedcomputer. In: Proceedings of International Symposium on Computer Architec-ture (ISCA), pp. 13–23.

[5] Arshad, Umer, et al. Utilizing power consumption and SLA violations using dynamic VM consolidation in cloud data centers. Renewable and Sustainable Energy Reviews, 2022, 167: 112782.

[6] Tran C H, Bui T K, Pham T V. Virtual machine migration policy for multi-tier application in cloud computing based on Q-learning algorithm[J]. Computing, 2022, 104(6): 1285-1306.

[7] Khan M S A, Santhosh R. Hybrid optimization algorithm for vm migration in cloud computing [J]. Computers and Electrical Engineering, 2022, 102: 108152.

[8] Belgacem A, Mahmoudi S, Ferrag M A. A machine learning model for improving virtual machine migration in cloud computing[J]. The Journal of Supercomputing, 2023: 1-23.

[9] Kaur A, Kumar S, Gupta D, et al. Algorithmic Approach to Virtual Machine Migration in Cloud Computing with Updated SESA Algorithm[J]. Sensors, 2023, 23(13): 6117.

[10] Hummaida A R, Paton N W, Sakellariou R. Dynamic threshold setting for VM migration [C]// European Conference on Service-Oriented and Cloud Computing. Cham: Springer International Publishing, 2022: 31-46.

[11] Singh S, Kumar R. Energy efficient optimization with threshold based workflow scheduling and virtual machine consolidation in cloud environment [J]. Wireless Personal Communications, 2023, 128(4): 2419-2440.

[12] Yao Y. Three-way decision: an interpretation of rules in rough set theory[C]//Rough Sets and Knowledge Technology: 4th International Conference, RSKT 2009, Gold Coast, Australia, July 14-16, 2009. Proceedings 4. Springer Berlin Heidelberg, 2009: 642-649.

[13] Liu S, Jiang C. A novel prediction approach based on three-way decision for cloud datacenters[J]. Applied Intelligence, 2023: 1-17.

[14] Yueqi Z , Yongqiang S , Yi S. Virtual Machine Migration Strategy Based on the Load Evaluation and Prediction under the Cloud Environment[C]//National Conference on Electrical, Electronics and Computer Engineering.2015.DOI:ConferenceArticle/5af2dbd8c095d70f18a72501.

[15] Beloglazov A.; Buyya R. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud datacenters. Concurr. Comput. Pract. Exp. 2012, 24, 1397–1420

[16] L. Huixi, X. Yinhao, S. Yongluo, A combination of host overloading detection and virtual machine selection in cloud server consolidation based on learning method, 2022, arXiv preprint arXiv: 2206. 13717.