Abstract
Task offloading emerges as a promising solution in Mobile Edge Computing (MEC) scenarios to not only incorporate more processing capability but also save energy. There however exists a key conflict between the heavy processing workloads of terminals and the limited wireless bandwidth, making it challenging to determine the computing placement at the terminals or the remote servers. In this paper, we aim to migrate the most suitable offloading tasks to fully obtain the benefits from the resourceful cloud. The problem in this task offloading scenario is modeled as an optimization problem. Therefore, a Genetic Algorithm is then proposed to achieve maximal user selection and the most valuable task offloading. Specifically, the cloud is pondered to provide computing services for as many edge wireless terminals as possible under the limited wireless channels. The base stations (BSs) serve as the edge for task coordination. The tasks are jointly considered to minimize the computing overhead and energy consumption, where the cost model of local devices is used as one of the optimization objectives in this wireless mobile selective schedule. We also establish the multi-devices task offloading scenario to further verify the efficiency of the proposed allocating schedule. Our extensive numerical experiments demonstrate that our allocating scheme can effectively take advantage of the cloud server and reduce the cost of end users.
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August 2018. https://iot-analytics.com/state-of-the-iot-update-q1-q2-2018-number-of-iot-devices-now-7b/
Bockelmann, N., et al.: Massive machine-type communications in 5G: physical and MAC-layer solutions. IEEE Commun. Mag. 54(9), 59–65 (2016)
Chen, R., Liang, C.Y., Hong, W.C., Gu, D.X.: Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Appl. Soft Comput. 26, 435–443 (2015)
Chen, S., Wang, Y., Pedram, M.: A semi-Markovian decision process based control method for offloading tasks from mobile devices to the cloud. In: 2013 IEEE Global Communications Conference (GLOBECOM), pp. 2885–2890. IEEE (2013)
Chen, X., Jiao, L., Li, W., Fu, X.: Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Netw. 24(5), 2795–2808 (2015)
Di, B., Bayat, S., Song, L., Li, Y., Han, Z.: Joint user pairing, subchannel, and power allocation in full-duplex multi-user ofdma networks. IEEE Trans. Wirel. Commun. 15(12), 8260–8272 (2016)
Du, Y., Dong, B., Chen, Z., Fang, J., Yang, L.: Shuffled multiuser detection schemes for uplink sparse code multiple access systems. IEEE Commun. Lett. 20(6), 1231–1234 (2016)
Huang, D., Wang, P., Niyato, D.: A dynamic offloading algorithm for mobile computing. IEEE Trans. Wirel. Commun. 11(6), 1991–1995 (2012)
Kwak, J., Kim, Y., Lee, J., Chong, S.: DREAM: dynamic resource and task allocation for energy minimization in mobile cloud systems. IEEE J. Sel. Areas Commun. 33(12), 2510–2523 (2015)
Li, J., Gao, H., Lv, T., Lu, Y.: Deep reinforcement learning based computation offloading and resource allocation for MEC. In: 2018 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE (2018)
Liu, X., Cao, J., Yang, Y., Qu, W., Zhao, X., Li, K., Yao, D.: Fast rfidsensory data collection: trade-off between computation and communicationcosts. IEEE/ACM Trans. Netw. (2019)
Liu, X., Xie, X., Wang, S., Liu, J., Yao, D., Cao, J.: Efficient range queries for large-scale sensor-augmented RFID systems. In: EEE/ACM Trans. Netw. (TON) (2019, in press)
Mao, Y., You, C., Zhang, J., Huang, K., Letaief, K.B.: A survey on mobile edge computing: the communication perspective. IEEE Commun. Surv. Tutor. 19(4), 2322–2358 (2017)
Mao, Y., Zhang, J., Song, S., Letaief, K.B.: Stochastic joint radio and computational resource management for multi-user mobile-edge computing systems. IEEE Trans. Wireless Commun. 16(9), 5994–6009 (2017)
Nordrum, A., Clark, K., et al.: Everything you need to know about 5G. IEEE Spectrum (2017)
Satyanarayanan, M.: The emergence of edge computing. Computer 50(1), 30–39 (2017)
Teli, S.R., Zvanovec, S., Ghassemlooy, Z.: Optical internet of things within 5G: applications and challenges. In: 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS), pp. 40–45. IEEE (2018)
Wang, F., Wang, F., Ma, X., Liu, J.: Demystifying the crowd intelligence in last mile parcel delivery for smart cities. IEEE Netw. 33(2), 23–29 (2019)
Wang, F., et al.: Intelligent edge-assisted crowdcast with deep reinforcement learning for personalized QoE. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 910–918. IEEE (2019)
You, C., Huang, K., Chae, H., Kim, B.H.: Energy-efficient resource allocation for mobile-edge computation offloading. IEEE Trans. Wirel. Commun. 16(3), 1397–1411 (2016)
Zhao, T., Zhou, S., Guo, X., Niu, Z.: Tasks scheduling and resource allocation in heterogeneous cloud for delay-bounded mobile edge computing. In: 2017 IEEE International Conference on Communications (ICC), pp. 1–7. IEEE (2017)
Zhao, X., Zhao, L., Liang, K.: An energy consumption oriented offloading algorithm for fog computing. In: Lee, J.-H., Pack, S. (eds.) QShine 2016. LNICST, vol. 199, pp. 293–301. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60717-7_29
Acknowledgement
This research was supported by China Scholarship Council (CSC), Fund of Applied Basic Research Programs of Science and Technology Department (No. 2018JY0290). The work of Lei Zhang was supported in part by the National Natural Science Foundation of China under Grant 61902257. The work of Fangxin Wang and Jiangchuan Liu is supported by a Canada NSERC Discovery Grant.
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Li, W., Pan, Y., Wang, F., Zhang, L., Liu, J. (2020). Toward Optimal Resource Allocation for Task Offloading in Mobile Edge Computing. In: Chu, X., Jiang, H., Li, B., Wang, D., Wang, W. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Systems. QShine 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 300. Springer, Cham. https://doi.org/10.1007/978-3-030-38819-5_4
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DOI: https://doi.org/10.1007/978-3-030-38819-5_4
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