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Mobile edge computing task scheduling and device cooperation algorithm based on deep learning.

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Bibliographic Details
Title: Mobile edge computing task scheduling and device cooperation algorithm based on deep learning.
Authors: Liu, Manjun1 liumanjun@elnu.edu.cn
Source: Journal of Electronic Imaging. Nov/Dec2022, Vol. 31 Issue 6, p62007-062007-14. 1p.
Subjects: Deep learning, Mobile computing, Edge computing, Conjugate gradient methods, Multipliers (Mathematical analysis), Algorithms
Abstract: With the changes in the era of science and technology, the emergence of 5G technology makes a communication speed leap, and the delay is also getting shorter and shorter. At present, the computing power of computing equipment is also steadily improving. As one of its key technologies, mobile edge computing (MEC) is becoming more and more important. Here, we introduce MEC task scheduling and device collaboration algorithms based on deep learning. Aiming at the multi-task joint optimization scheduling strategy microtearing mode to minimize the total service cost of multiple tasks, we establish a theoretical optimization model of multi-task joint scheduling and use the model to design a multi-task cloud collaborative optimization scheduling algorithm based on Livepeer Token rules. We propose a deep-learning device collaboration algorithm based on alternating direction multiplier. The sub differential algorithm and the improved conjugate gradient method are designed to solve the sub problem respectively. This paper simulates the proposed deep-learning task scheduling algorithm and device collaboration algorithm. The simulation results show that, in the task scheduling algorithm, the proposed algorithm reduces the cost of completing the task, but in the total task time, this algorithm has a certain gap compared with other algorithms. In the equipment collaboration algorithm, with the increase of the n value, the algorithm converges in a certain iteration increment; in terms of algorithm convergence, the algorithm needs 142 iterations at most to achieve convergence, and the fastest speed only needs 17 iterations. In the experiment based on real data sets, the proposed algorithm is also better than the other two algorithms. From these results, we can see that the proposed algorithm has good convergence, stability, and scalability, and it has excellent performance in task scheduling and device collaboration of MEC. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:With the changes in the era of science and technology, the emergence of 5G technology makes a communication speed leap, and the delay is also getting shorter and shorter. At present, the computing power of computing equipment is also steadily improving. As one of its key technologies, mobile edge computing (MEC) is becoming more and more important. Here, we introduce MEC task scheduling and device collaboration algorithms based on deep learning. Aiming at the multi-task joint optimization scheduling strategy microtearing mode to minimize the total service cost of multiple tasks, we establish a theoretical optimization model of multi-task joint scheduling and use the model to design a multi-task cloud collaborative optimization scheduling algorithm based on Livepeer Token rules. We propose a deep-learning device collaboration algorithm based on alternating direction multiplier. The sub differential algorithm and the improved conjugate gradient method are designed to solve the sub problem respectively. This paper simulates the proposed deep-learning task scheduling algorithm and device collaboration algorithm. The simulation results show that, in the task scheduling algorithm, the proposed algorithm reduces the cost of completing the task, but in the total task time, this algorithm has a certain gap compared with other algorithms. In the equipment collaboration algorithm, with the increase of the n value, the algorithm converges in a certain iteration increment; in terms of algorithm convergence, the algorithm needs 142 iterations at most to achieve convergence, and the fastest speed only needs 17 iterations. In the experiment based on real data sets, the proposed algorithm is also better than the other two algorithms. From these results, we can see that the proposed algorithm has good convergence, stability, and scalability, and it has excellent performance in task scheduling and device collaboration of MEC. [ABSTRACT FROM AUTHOR]
ISSN:10179909
DOI:10.1117/1.JEI.31.6.062007