Transactions on Machine Intelligence 2821-1693 2 3 2019 09 01 Migration of Virtual Machines in Heterogeneous Mobile Cloud Computing Using Grey Wolf Optimization Algorithm 191 204 206087 10.47176/TMI.2019.191 EN R. Zare Farkhadi Faculty Member, Department of Computer Engineering, Roshdieh Higher Education Institute, Tabriz, Iran S. Vahabzadeh Master’s Student, Software Engineering, Roshdieh Higher Education Institute, Tabriz, Iran Journal Article 2019 06 12 Mobile cloud computing improves the performance of a mobile phone by executing an application on an efficient cloud server, which can minimize execution time compared to a resource-constrained mobile device. Virtual machine migration in mobile cloud computing brings cloud resources closer to the user, thereby minimizing response time to an application. Such resource transfers are highly effective for interactive and real-time applications. However, the main challenge is to find an optimal cloud server for migration that offers the maximum reduction in computation time. In this paper, a virtual machine migration model based on the Grey Wolf Optimization (GWO) algorithm is proposed for heterogeneous mobile cloud computing systems. The proposed approach optimizes the effectiveness of virtual machine transfer by considering user mobility and the load on cloud servers. The main objective of the proposed approach is to select an optimal cloud server for a mobile virtual machine and to minimize the total number of virtual migrations, which results in reduced job execution time. Additionally, a comprehensive numerical evaluation is presented to assess the effectiveness of the proposed model compared to advanced virtual machine migration policies. The proposed approach is simulated using MATLAB software, and the results are compared with those obtained using the Genetic Algorithm (GA). Experimental results indicate that the Grey Wolf Optimization algorithm has shown improvements in performance compared to the Genetic Algorithm. https://www.tmachineintelligence.ir/article_206087_81db8d65feb36b4dab2624f83a8f6a66.pdf