Optimization of multitask parallel mobile edge computing strategy based on deep learning architecture
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Optimization of multitask parallel mobile edge computing strategy based on deep learning architecture Zongkai Liu1 · Xiaoqiang Yang1
· Jinxing Shen1
Received: 9 April 2019 / Accepted: 4 July 2019 © Springer Science+Business Media, LLC, part of Springer Nature 2019
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. Keywords Deep learning · Multitask parallel processing architecture · Mobile edge computing · Network model · Loading technology
1 Introduction With the rapid development of communication technology, the number of mobile terminal devices is increasing. At the same time, the Internet traffic also shows exponential growth, which greatly drives the development of 5G technology [1–3]. However, due to the increasing complexity of mobile terminal devices, communication resources are further compressed [4]. As an auxiliary base station on the edge of mobile network, small base station has certain computing and storage capacity. The deep integration of mobile edge algorithm and
B 1
Xiaoqiang Yang [email protected] The Army Engineering University of PLA, Nanjing 210007, Jiangsu, China
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Z. Liu et al.
Internet services is conducive to solving the current problem of limited mobile resources. The proposed mobile edge detection algorithm improves the throughput of the network to a certain extent and reduces the delay of network data and computation. Therefore, it is of great significance for the migration decision-making, migration allocation and corresponding transmission processing of communication tasks [5–9]. However, the traditional mobile edge detection algorithm can’t deal with complex computing tasks efficiently,
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