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Orchestration-Based Task Offloading for Mobile Edge Computing in Small-Cell Networks

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Proceedings of International Joint Conference on Computational Intelligence

Abstract

To execute computation-intensive applications and stringent latency-critical tasks at resource constraints smart mobile devices, mobile edge computing (MEC) in small-cell networks is one of the leading thought, where mobile devices will offload their computation-intensive tasks to the adjacent small-cell network for faster processing. Currently, some research work has been done for combining mobile edge computing and small-cell networks together. Existing researches mostly concentrate on the user to small base station (SBS) offloading and improving the radio access performance using optimization, while the computing capability of SBS-MEC server is ignored. In order to acquire superior performance, an efficient orchestration-based task offloading for mobile edge computing in small-cell networks is proposed in this paper where edge orchestrator collects all the information from the neighboring small-cell SBS-MEC server to decide for forwarding the workloads from overloaded SBS-MEC to nearby SBS-MEC with a light workload. Simulation results affirm that orchestration-based task offloading scheme offers the best results not only by reducing the task failure but also with a smaller task completion time compared to other approaches in small-cell networks.

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Acknowledgements

This research was supported by the Ministry of Science and ICT (MIST), Korea, under the National Program for Excellence in SW (2017-0-00093) and Smart Media Research and Business Development Support Program (2019-0-01615) supervised by the Institute for Information and communications Technology Planning and Evaluation (IITP). Corresponding author: Professor Eui-Nam Huh.

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Delowar Hossain, M. et al. (2020). Orchestration-Based Task Offloading for Mobile Edge Computing in Small-Cell Networks. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3607-6_50

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