A DRL-Based Container Placement Scheme with Auxiliary Tasks

Yuan, Ningcheng; Jia, Chao; Lu, Jizhao; Guo, Shaoyong; Li, Wen‐Cui; Qiu, Xuesong; Shi, Lei

doi:10.32604/cmc.2020.09840

Cited by 6 publications

(11 citation statements)

References 6 publications

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“…In MobileNets, they developed a set of efficient convolutional neural models, Andrew G. Howard et al [ 66 ]. A class of efficient MobileNets convolution models were developed by him, as part of a class called MobileNets.…”

Section: Real-time Applications Of Fermentioning

confidence: 99%

Methods for Facial Expression Recognition with Applications in Challenging Situations

Pise

Alqahtani

Verma

et al. 2022

Computational Intelligence and Neuroscience

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In the last few years, a great deal of interesting research has been achieved on automatic facial emotion recognition (FER). FER has been used in a number of ways to make human-machine interactions better, including human center computing and the new trends of emotional artificial intelligence (EAI). Researchers in the EAI field aim to make computers better at predicting and analyzing the facial expressions and behavior of human under different scenarios and cases. Deep learning has had the greatest influence on such a field since neural networks have evolved significantly in recent years, and accordingly, different architectures are being developed to solve more and more difficult problems. This article will address the latest advances in computational intelligence-related automated emotion recognition using recent deep learning models. We show that both deep learning-based FER and models that use architecture-related methods, such as databases, can collaborate well in delivering highly accurate results.

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Section: Real-time Applications Of Fermentioning

confidence: 99%

Methods for Facial Expression Recognition with Applications in Challenging Situations

Pise

Alqahtani

Verma

et al. 2022

Computational Intelligence and Neuroscience

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“…Because the environment of edge computing network is complex and changeable, to learn in this challenging environment, it is necessary to use a reliable and scalable intelligent algorithm [15]. Because PPO algorithm guarantees stability by binding the range of parameter update to the trust area, this paper considers using this algorithm to complete the unloading of edge tasks [16].…”

Section: Ppo Algorithm Frameworkmentioning

confidence: 99%

An Energy Aware Algorithm for Edge Task Offloading

Xiong¹,

Meng²,

Guo³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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To solve the problem of energy consumption optimization of edge servers in the process of edge task unloading, we propose a task unloading algorithm based on reinforcement learning in this paper. The algorithm observes and analyzes the current environment state, selects the deployment location of edge tasks according to current states, and realizes the edge task unloading oriented to energy consumption optimization. To achieve the above goals, we first construct a network energy consumption model including servers' energy consumption and link transmission energy consumption, which improves the accuracy of network energy consumption evaluation. Because of the complexity and variability of the edge environment, this paper designs a task unloading algorithm based on Proximal Policy Optimization (PPO), besides we use Dijkstra to determine the connection path between edge servers where adjacent tasks are deployed. Finally, lots of simulation experiments verify the effectiveness of the proposed method in the process of task unloading. Compared with contrast algorithms, the average energy saving of the proposed algorithm can reach 22.69%.

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“…Discussions of specific parts of RL solution design problems occur in smaller number of cases, but these kinds of publication demonstrate the fact that constructing an appropriate RL application is not always trivial. We can highlight state space design [12,25,33,107,144,179,193,208,217,220,222,224,227,266,267] and action space design [109,220,246,268], reward construction [14,76,110,199,220,226,246,[269][270][271][272][273], and exploration strategy planning [86,274] which can be determinants from the whole application point of view. [11,13,17,20,21,24,38,43,61,62,66,69,82,89,…”

Section: Complexitymentioning

confidence: 99%

“…We can highlight state space design [12,25,33,107,144,179,193,208,217,220,222,224,227,266,267] and action space design [109,220,246,268], reward construction [14,76,110,199,220,226,246,[269][270][271][272][273], and exploration strategy planning [86,274] which can be determinants from the whole application point of view. [11,13,17,20,21,24,38,43,61,62,66,69,82,89,93], Allocation, assignment, resource management [20,22,…”

Section: Complexitymentioning

confidence: 99%

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

2021

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Reinforcement learning (RL) methods can successfully solve complex optimization problems. Our article gives a systematic overview of major types of RL methods, their applications at the field of Industry 4.0 solutions, and it provides methodological guidelines to determine the right approach that can be fitted better to the different problems, and moreover, it can be a point of reference for R&D projects and further researches.

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A DRL-Based Container Placement Scheme with Auxiliary Tasks

Cited by 6 publications

References 6 publications

Methods for Facial Expression Recognition with Applications in Challenging Situations

Methods for Facial Expression Recognition with Applications in Challenging Situations

An Energy Aware Algorithm for Edge Task Offloading

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

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