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

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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]
Copyright of Journal of Electronic Imaging is the property of SPIE - International Society of Optical Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Mobile edge computing task scheduling and device cooperation algorithm based on deep learning.
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  Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+computing%22">Mobile computing</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Conjugate+gradient+methods%22">Conjugate gradient methods</searchLink><br /><searchLink fieldCode="DE" term="%22Multipliers+%28Mathematical+analysis%29%22">Multipliers (Mathematical analysis)</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink>
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  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Electronic Imaging is the property of SPIE - International Society of Optical Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1117/1.JEI.31.6.062007
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      – Code: eng
        Text: English
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      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Mobile computing
        Type: general
      – SubjectFull: Edge computing
        Type: general
      – SubjectFull: Conjugate gradient methods
        Type: general
      – SubjectFull: Multipliers (Mathematical analysis)
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      – SubjectFull: Algorithms
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              Text: Nov/Dec2022
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