Abstract
As a mainstream computing and storage strategy for mobile communications, Internet of Things and large data applications, mobile edge computing strategy mainly benefits from the deployment and allocation of small base stations. Mobile edge computing mainly helps users to complete complex, intensive and sensitive computing tasks. However, the algorithm has many problems in practical application, such as complex user needs, complex user mobility, numerous services and applications. Therefore, under the above background, it is of great significance to solve the computational pressure of current mobile edge algorithm and optimize its algorithm architecture. This paper creatively proposes a deep learning architecture based on tightly connected network, and transplants it into mobile edge algorithm to realize the payload sharing process of edge computing, so as to establish an efficient network model. At the same time, we creatively propose a multi-task parallel scheduling algorithm, which realizes the mobile edge algorithm in the face of complex computing and algorithm efficiency. Finally, the above algorithms are simulated and tested. The experimental results show that under the same task, the time consumed by the proposed algorithm is 3.5–4, while the time consumed by the traditional algorithm is 4.5–8, and the corresponding time is standardized time, so the practice shows that the algorithm has obvious overall efficiency advantages.
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References
Tran TX, Hajisami A, Pandey P et al (2017) Collaborative mobile edge computing in 5G networks: new paradigms, scenarios, and challenges. IEEE Commun Mag 55(4):54–61
Feng W, Jie X, Xin W et al (2017) Joint offloading and computing optimization in wireless powered mobile-edge computing systems. IEEE Trans Wirel Commun 17(3):1784–1797
Wang C, Liang C, Yu FR et al (2017) Computation offloading and resource allocation in wireless cellular networks with mobile edge computing. IEEE Trans Wirel Commun 16(8):1
Dinh TQ, Tang J, La QD et al (2017) Offloading in mobile edge computing: task allocation and computational frequency scaling. IEEE Trans Commun 65(8):3571–3584
Zhou F, Wu Y, Hu RQ et al (2018) Computation rate maximization in UAV-enabled wireless-powered mobile-edge computing systems. IEEE J Sel Areas Commun 36(9):1927–1941
Mao Y, Zhang J, Song SH et al (2017) Stochastic joint radio and computational resource management for multi-user mobile-edge computing systems. IEEE Trans Wirel Commun 16(9):5994–6009
Sun Y, Sheng Z, Jie X (2017) EMM: energy-aware mobility management for mobile edge computing in ultra dense networks. IEEE J Sel Areas Commun 35(11):2637–2646
Ying H, Yu FR, Nan Z et al (2017) Software-defined networks with mobile edge computing and caching for smart cities: a big data deep reinforcement learning approach. IEEE Commun Mag 55(12):31–37
Bi S, Zhang YJA (2017) Computation rate maximization for wireless powered mobile-edge computing with binary computation offloading. IEEE Trans Wirel Commun 17(6):4177–4190
Zhou Y, Yu FR, Jian C et al (2017) Resource allocation for information-centric virtualized heterogeneous networks with in-network caching and mobile edge computing. IEEE Trans Veh Technol 66(12):11339–11351
Ke Z, Mao Y, Leng S et al (2017) Mobile-edge computing for vehicular networks: a promising network paradigm with predictive off-loading. IEEE Veh Technol Mag 12(2):36–44
Liu J, Wan J, Bi Z et al (2017) A scalable and quick-response software defined vehicular network assisted by mobile edge computing. IEEE Commun Mag 55(7):94–100
Zhang G, Zhang W, Yu C et al (2018) Energy-delay tradeoff for dynamic offloading in mobile-edge computing system with energy harvesting devices. IEEE Trans Ind Inform 14(10):4642–4655
Kim S (2018) One-on-one contract game–based dynamic virtual machine migration scheme for mobile edge computing. Trans Emerg Telecommun Technol 29(1):e3204
Ning Z, Wang X, Huang J (2018) Mobile edge computing-enabled 5G vehicular networks: toward the integration of communication and computing. IEEE Veh Technol Mag 14(1):54–61
Ying H, Yu FR, Nan Z et al (2018) Secure social networks in 5G systems with mobile edge computing, caching, and device-to-device communications. IEEE Wirel Commun 25(3):103–109
Zhang Z, Zhang W, Tseng FH (2019) Satellite mobile edge computing: improving QoS of high-speed satellite-terrestrial networks using edge computing techniques. IEEE Network 33(1):70–76
Rodrigues TG, Suto K, Nishiyama H et al (2018) Cloudlets activation scheme for scalable mobile edge computing with transmission power control and virtual machine migration. IEEE Trans Comput 67(9):1287–1300
Carvalho GHS, Woungang I, Anpalagan A et al (2019) Analysis of joint parallelism in wireless and cloud domains on mobile edge computing over 5G systems. J Commun Netw 20(6):565–577
Meng L, Yu R, Si P et al (2018) Energy-efficient machine-to-machine (M2M) communications in virtualized cellular networks with mobile edge computing (MEC). IEEE Trans Mob Comput 18(7):1541–1555
Zhang H, Chen Z, Wu J et al (2019) FRRF: a fuzzy reasoning routing-forwarding algorithm using mobile device similarity in mobile edge computing-based opportunistic mobile social networks. IEEE Access 7:35874–35889
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Liu, Z., Yang, X. & Shen, J. Optimization of multitask parallel mobile edge computing strategy based on deep learning architecture. Des Autom Embed Syst 24, 129–143 (2020). https://doi.org/10.1007/s10617-019-09222-5
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DOI: https://doi.org/10.1007/s10617-019-09222-5