A Mobility Prediction Based Adaptive Task Migration in Mobile Edge Computing





During the past few years, mobile data traffic has exponentially increased due to emerging applications, such as social media, online gaming, and augmented/virtual reality. Although the capabilities of mobile devices are significantly improved, they are unable to execute computationally intensive tasks. To extend the computing capabilities of resource-constrained mobile devices, computation offloading is performed on edge servers. Due to user mobility, offloaded tasks often need to be migrated from one edge server to another. Mobility-aware task migration faces different challenges due to varying mobility characteristics of end-users. These challenges include latency, server utilization, and energy consumption. Existing techniques of task and machine (VM) migration do not consider the user movement trajectories while making migration decisions. Consequently, the task or VM is migrated to the edge server that may be far away from the mobile users' location that increases the response time. In this paper we proposed Mobility Migration Algorithm based on Linear Regression (MALR). After outsourcing the task, a recurrent neural network (RNN) and linear regression are used to forecast the user's present location. Using the distance between the user and the server, it gets a list of nearby servers, and then moves the task there. The proposed approach eliminates the job migration time with improvement in forecast accuracy as compared to the logistic regression and K-mean.


Radiocrafts, “Cloud vs Fog vs Mist Computing, Which One Should You Use?,” 2019. [Online]. Available: https://radiocrafts.com/cloud-vs-fog-vs-mist-computing-which-one-should-you-use/.

Parkavi R, Priyanka C, Sujitha S, and Sheik Abdullah A, “Mobile Cloud Computing,” in Mobile Cloud Computing, 2018, pp. 105–123, doi: 10.4018/978-1-5225-4044-1.ch006. DOI: https://doi.org/10.4018/978-1-5225-4044-1.ch006

T. Bai, C. Pan, C. Han, and L. Hanzo, “Empowering Mobile Edge Computing by Exploiting Reconfigurable Intelligent Surface,” arXiv, 2021. [Online]. Available: http://arxiv.org/abs/2102.02569.

Q. Cao, Q. Wu, B. Liu, S. Zhang, and Y. Zhang, “An Optimization Method for Mobile Edge Service Migration in Cyberphysical Power System,” Wireless Communications and Mobile Computing, vol. 2021, 2021, doi: 10.1155/2021/6610654.

S. Li, D. Zhai, P. Du, and T. Han, “Energy-efficient task offloading, load balancing, and resource allocation in mobile edge computing enabled IoT networks,” Science China Information Sciences, vol. 62, no. 2, 2019, doi: 10.1007/s11432-017-9440-x.

Y. Wang, H. Zhu, X. Hei, Y. Kong, W. Ji, and L. Zhu, “An energy saving based on task migration for mobile edge computing,” Eurasip Journal on Wireless Communications and Networking, vol. 2019, no. 1, 2019, doi: 10.1186/s13638-019-1469-2.

J. U. Arshed and M. Ahmed, “Race: resource aware cost-efficient scheduler for cloud fog environment,” IEEE Access, vol. 9, pp. 65688–65701, 2021.

W. Zhou, W. Fang, Y. Li, B. Yuan, Y. Li, and T. Wang, “Markov Approximation for Task Offloading and Computation Scaling in Mobile Edge Computing,” Mobile Information Systems, vol. 2019, 2019, doi: 10.1155/2019/8172698.

A. Nadembega, A. S. Hafid, and R. Brisebois, “Mobility prediction model-based service migration procedure for follow me cloud to support QoS and QoE,” in 2016 IEEE International Conference on Communications (ICC), 2016, doi: 10.1109/ICC.2016.7511148. DOI: https://doi.org/10.1109/ICC.2016.7511148

R. M. Abdelmoneem, A. Benslimane, and E. Shaaban, “Mobility-aware task scheduling in cloud-Fog IoT-based healthcare architectures,” Computer Networks, vol. 179, 2020, doi: 10.1016/j.comnet.2020.107348.

J. U. Arshed, M. Ahmed, T. Muhammad, M. Afzal, M. Arif, and B. Bazezew, “GA-IRACE: Genetic algorithm-based improved resource aware cost-efficient scheduler for cloud fog computing environment,” Wireless Communications and Mobile Computing, vol. 2022, 2022.

H. Wang, X. Li, H. Ji, and H. Zhang, “Federated Offloading Scheme to Minimize Latency in MEC-Enabled Vehicular Networks,” in 2018 IEEE Globecom Workshops (GC Wkshps 2018) - Proceedings, 2019, doi: 10.1109/GLOCOMW.2018.8644315.

S. Wang, R. Urgaonkar, T. He, M. Zafer, K. Chan, and K. K. Leung, “Mobility-induced service migration in mobile micro-clouds,” in IEEE Military Communications Conference (MILCOM), 2014, pp. 835–840, doi: 10.1109/MILCOM.2014.145. DOI: https://doi.org/10.1109/MILCOM.2014.145

L. Rui, S. Wang, Z. Wang, A. Xiong, and H. Liu, “A dynamic service migration strategy based on mobility prediction in edge computing,” International Journal of Distributed Sensor Networks, vol. 17, no. 2, 2021, doi: 10.1177/1550147721993403.

C. Zhang and Z. Zheng, “Task migration for mobile edge computing using deep reinforcement learning,” Future Generation Computer Systems, vol. 96, pp. 111–118, 2019, doi: 10.1016/j.future.2019.01.059.

J. Plachy, Z. Becvar, and E. C. Strinati, “Dynamic resource allocation exploiting mobility prediction in mobile edge computing,” in IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2016, doi: 10.1109/PIMRC.2016.7794955. DOI: https://doi.org/10.1109/PIMRC.2016.7794955

L. Gkatzikis and I. Koutsopoulos, “Migrate or not? Exploiting dynamic task migration in mobile cloud computing systems,” IEEE Wireless Communications, vol. 20, no. 3, pp. 24–32, 2013, doi: 10.1109/MWC.2013.6549280. DOI: https://doi.org/10.1109/MWC.2013.6549280

A. Hadachi, O. Batrashev, A. Lind, G. Singer, and E. Vainikko, “Cell phone subscribers mobility prediction using enhanced Markov Chain algorithm,” in IEEE Intelligent Vehicles Symposium Proceedings, 2014, pp. 1049–1054, doi: 10.1109/IVS.2014.6856442. DOI: https://doi.org/10.1109/IVS.2014.6856442

I. Labriji et al., “Mobility Aware and Dynamic Migration of MEC Services for the Internet of Vehicles,” IEEE Transactions on Network and Service Management, vol. 18, no. 1, pp. 570–584, 2021, doi: 10.1109/TNSM.2021.3052808.

C. Kang et al., “Analyzing and geo-visualizing individual human mobility patterns using mobile call records,” in 2010 18th International Conference on Geoinformatics (Geoinformatics 2010), 2010, doi: 10.1109/GEOINFORMATICS.2010.5567857. DOI: https://doi.org/10.1109/GEOINFORMATICS.2010.5567857

S. Wang, R. Urgaonkar, M. Zafer, T. He, K. Chan, and K. K. Leung, “Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process,” IEEE/ACM Transactions on Networking, vol. 27, no. 3, pp. 1272–1288, 2019, doi: 10.1109/TNET.2019.2916577.

M. Zeng and V. Fodor, “Dynamic Spectrum Sharing for Load Balancing in Multi-Cell Mobile Edge Computing,” IEEE Wireless Communications Letters, vol. 9, no. 2, pp. 189–193, 2020, doi: 10.1109/LWC.2019.2947479.

F. Zhang, G. Liu, B. Zhao, X. Fu, and R. Yahyapour, “Reducing the network overhead of user mobility–induced virtual machine migration in mobile edge computing,” Software: Practice and Experience, vol. 49, no. 4, pp. 673–693, 2019, doi: 10.1002/spe.2642.

Q. Peng et al., “Mobility-aware and migration-enabled online edge user allocation in mobile edge computing,” in 2019 IEEE International Conference on Web Services (ICWS) - Part of the 2019 IEEE World Congress on Services, 2019, pp. 91–98, doi: 10.1109/ICWS.2019.00026.




How to Cite

Abbasi, M. H. A., Arshed, J. U., Imtiaz Ahmad, Afzal, M., Hasnat Ali, & Ghulam Hussain. (2024). A Mobility Prediction Based Adaptive Task Migration in Mobile Edge Computing. VFAST Transactions on Software Engineering, 12(2), 46–55. https://doi.org/10.21015/vtse.v12i2.1768