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Computing Offloading Strategy Using Improved Genetic Algorithm in Mobile Edge Computing System

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Abstract

For the current research on computing offloading, most of them only considers the multi-user task offloading decision problem or only considers the wireless resource and computing resource allocation. They have failed to comprehensively consider the impact of offloading decision and resource allocation on computing offloading performance, and it is difficult to achieve efficient computing offloading. For this reason, this paper proposes an edge computing task offloading strategy based on improved genetic algorithm (IGA). First, the weighted sum of task execution delay and energy consumption is defined as the optimization function of total overhead. Besides, the paper comprehensively considers the impact of users’ offloading decision, uplink power allocation related to task offloading and MEC computing resource allocation on system performance. Secondly, Genetic Algorithm (GA) is substituted to establish communication model, the offloading strategy is corresponding to the chromosome in algorithm and the gene is encoded by integer coding. Finally, IGA is used to solve the task to achieve efficient offloading. Among them, the use of integer coding, knowledge-based crossover and the mutation of population segmentation improves the optimization ability of this algorithm. Finally, experimental results show that the performance of IGA is the best, and the overall cost is about 52.7% of All-local algorithm and 28.8% of Full-edge algorithm.

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References

  1. Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges[J]. IEEE Internet Things J.. 3(5), 637–646 (2016)

    Article  Google Scholar 

  2. Chen, L., Zhou, S., Xu, J.: Computation Peer Offloading for Energy-Constrained Mobile Edge Computing in Small-Cell Networks[J]. IEEE/ACM Trans Networking. 26(4), 1619–1632 (2018)

    Article  Google Scholar 

  3. Yang, Y., Wang, K., Zhang, G., Chen, X., Luo, X., Zhou, M.T.: MEETS: maximal energy efficient task scheduling in homogeneous fog networks[J]. IEEE Internet Things J. 5(5), 4076–4087 (2018)

    Article  Google Scholar 

  4. Shi, B., Yang, J., Huang, Z., et al.: Offloading guidelines for augmented reality applications on wearable devices[C]// the 23rd ACM international conference. Brisbane. 1271–1274 (2015). https://doi.org/10.1145/2733373.2806402

  5. Nunna, S., Kousaridas, A., Ibrahim, M., et al.: Enabling Real-Time Context-Aware Collaboration through 5G and Mobile Edge Computing[C], pp. 601–605. International Conference on Information Technology - New Generations, Las Vegas (2015)

    Google Scholar 

  6. Bi, S., Zhang, Y.J.: Computation rate maximization for wireless powered Mobile-edge computing with binary computation offloading[J]. IEEE Trans. Wirel. Commun. 17(6), 4177–4190 (2018)

    Article  Google Scholar 

  7. Hu, X., Wong, K., Yang, K.: Wireless powered cooperation-assisted Mobile edge computing[J]. IEEE Trans. Wirel. Commun. 17(4), 2375–2388 (2018)

    Article  Google Scholar 

  8. Wang, F., Xu, J., Wang, X., Cui, S.: Joint offloading and computing optimization in wireless powered Mobile - edge computing systems[J]. IEEE Trans. Wirel. Commun. 17(3), 1784–1797 (2018)

    Article  Google Scholar 

  9. Li, J., Lv, T.: Deep Neural Network Based Computational Resource Allocation for Mobile Edge Computing[C], pp. 1–6. IEEE Global Communications Conference (GLOBECOM), Abu Dhabi (2018)

    Google Scholar 

  10. Mao, Y., Zhang, J., Letaief, K.B.: Dynamic computation offloading for Mobile-edge computing with energy harvesting devices[J]. IEEE J. Select. Areas Commun. 34(12), 3590–3605 (2016)

    Article  Google Scholar 

  11. Liu, J., Mao, Y., Zhang, J., et al.: Delay-optimal computation task scheduling for mobile-edge computing systems[C], pp. 1451–1455. IEEE international symposium on information theory (ISIT), Barcelona (2016)

    Google Scholar 

  12. Kamoun, M., Labidi, W., Sarkiss, M.: Joint resource allocation and offloading strategies in cloud enabled cellular networks[C], pp. 5529–5534. IEEE international conference on communications (ICC), London (2015)

    Google Scholar 

  13. Mao, Y., Zhang, J., Song, S.H., et al.: Power-delay tradeoff in multi-user Mobile-edge computing systems[C], pp. 1–6. IEEE global communications conference (GLOBECOM), Washington (2016)

    Google Scholar 

  14. Chen, X., Jiao, L., Li, W., Fu, X.: Efficient multi-user computation offloading for Mobile-edge cloud computing[J]. IEEE/ACM Trans. Networking. 24(5), 2795–2808 (2016)

    Article  Google Scholar 

  15. Chen, X.: Decentralized computation offloading game for Mobile cloud computing[J]. IEEE Trans. Parallel Distrib. Syst. 26(4), 974–983 (2015)

    Article  Google Scholar 

  16. Shulei, L.I., Zhai, D., Pengfei, D.U., et al.: Energy-efficient task offloading, load balancing, and resource allocation in mobile edge computing enabled IoT networks[J]. ence China Inform. ences. 62(002), 1–3 (2019)

    Google Scholar 

  17. Jiao, Z., Hu, X., Zhaolong, N., et al.: Energy-latency tradeoff for energy- aware offloading in Mobile edge computing networks[J]. IEEE Internet Things J. 5(4), 2633–2645 (2018)

    Article  Google Scholar 

  18. Ketyko, I., Kecskes, L., Nemes, C., et al.: Multi-User Computation Offloading as Multiple Knapsack Problem for 5G Mobile Edge Computing[C]// 2016 European Conference on Networks and Communications (Eu CNC), pp. 225–229. IEEE Press, Athens (2016)

    Google Scholar 

  19. Le, H.Q., Al-Shatri, H., Klein, A.: Efficient resource allocation in mobile-edge computation offloading: completion time minimization [C]// 2017 IEEE International Symposium on Information Theory (ISIT), pp. 2513–2517. IEEE Press, Aachen (2017)

    Google Scholar 

  20. Khair, U., Lestari, Y.D., Perdana, A., Hidayat, D., Budiman, A.: Genetic Algorithm Modification Analysis Of Mutation Operators, pp. 1–6. Max One Problem[C]// 2018 Third international conference on informatics and computing (ICIC), Palembang (2018)

    Google Scholar 

  21. Yiqiu, F., Xia, X., Junwei, G.: Cloud Computing Task Scheduling Algorithm Based On Improved Genetic Algorithm[C], vol. 2019, pp. 852–856. 2019 IEEE 3rd information technology, networking, electronic and automation control conference (ITNEC), Chengdu

  22. Pyrih, Y., Kaidan, M., Tchaikovskyi, I., Pleskanka, M.: Research of Genetic Algorithms for Increasing the Efficiency of Data Routing[C], vol. 2019, pp. 157–160. 2019 3rd international conference on advanced information and communications technologies (AICT), Lviv

  23. Li, T., Lei, G., Wan, F., Shu, Y.: Research on Intelligent Volume Algorithm Based on Improved Genetic Annealing Algorithm[C], vol. 2020, pp. 196–198. 2020 IEEE international conference on power, intelligent computing and systems (ICPICS), Shenyang

  24. Evolved Universal Terrestrial Radio Acess(E-UTRA); Further Advancements for E-UTRA Physical Layer Aspects (Release 9), 3rd Generation Partnership Project 3GPP TS 36.814 (2012)

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  1. Management School, Guangdong University of Foreign Studies South China Business College, Guangzhou, 510000, Guangdong, China

    Anqing Zhu & Youyun Wen

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  1. Anqing Zhu
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  2. Youyun Wen
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Correspondence to Anqing Zhu.

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Zhu, A., Wen, Y. Computing Offloading Strategy Using Improved Genetic Algorithm in Mobile Edge Computing System. J Grid Computing 19, 38 (2021). https://doi.org/10.1007/s10723-021-09578-8

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