Abstract
The deployment of edge computing forms a two-tier mobile computing network where each computation task can be processed locally or at the edge node. In this paper, we consider a single mobile device equipped with a list of heavy off-loadable tasks. Our goal is to jointly optimize the offloading decision and the computing resource allocation to minimize the overall tasks processing time. The formulated optimization problem considers both the dedicated energy capacity and the processing deadlines. Therefore, as the obtained problem is NP-hard and we proposed a simulated annealing-based heuristic solution scheme. In order to evaluate and compare our solution, we carried a set of simulation experiments. Finally, the obtained results in terms of total processing time are very encouraging. In addition, the proposed scheme generates the solution within acceptable and feasible timeframes.
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References
Mach, P., Becvar, Z.: Mobile edge computing: a survey on architecture and computation off loading. IEEE Commun. Surveys Tutorials 19(3), 1628–1656 (2017)
You, C., et al.: Energy-efficient resource allocation for mobile-edge computation offloading. IEEE Trans. Wireless Commun. 16(3), 1397–1411 (2017)
Chen, M.-H., Liang, B., Dong, M.,: Joint offloading and resource allocation for computation and communication in mobile cloud with computing access point. In: IEEE INFOCOM Conference on Computer Communications, pp. 1–9 (2017)
Chen, M.-H., Liang, B., Dong, M.,: Joint offloading decision and resource allocation for multi-user multi-task mobile cloud. In: IEEE International Conference on Communications (ICC), pp. 1–6 (2016)
Li, H.: Multi-task offloading and resource allocation for energy-efficiency in mobile edge computing. Int. J. Comput. Techn. 5(1), 5–13 (2018)
Chun, B.-G., et al.: Clonecloud: elastic execution between mobile device and cloud. In: Proceedings of the sixth conference on Computer systems, pp. 301–314 (2011)
Chen, X., et al.: Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans Networking 24(5), 2795–2808 (2016)
Zhang, K., et al.: Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks. IEEE Access 4, 5896–5907 (2016)
Fan, Z., et al.: Simulated-annealing load balancing for resource allocation in cloud environments. In: International Conference on Parallel and Distributed Computing, Applications and Technologies, pp. 1–6 (2013)
Chen, L., et al.: ENGINE: Cost Effective Offloading in Mobile Edge Computing with Fog-Cloud Cooperation. arXiv preprint arXiv:1711.01683 (2017)
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El Ghmary, M., Chanyour, T., Hmimz, Y., Cherkaoui Malki, M.O. (2020). Processing Time and Computing Resources Optimization in a Mobile Edge Computing Node. In: Bhateja, V., Satapathy, S., Satori, H. (eds) Embedded Systems and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1076. Springer, Singapore. https://doi.org/10.1007/978-981-15-0947-6_10
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DOI: https://doi.org/10.1007/978-981-15-0947-6_10
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