A Fast Q-Learning Based Data Storage Optimization for Low Latency in Data Center Networks

Liao, Zhuofan; Peng, Jingsheng; Chen, Yuantao; Zhang, Jingyu; Jin, Wang

doi:10.1109/access.2020.2994328

Cited by 21 publications

(11 citation statements)

References 23 publications

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“…However, thanks to technologies, the number of sensors and IOT objects have proliferated and thus we have huge amounts of data that require big data algorithms like Spark and Hadoop to handle the data. [30] Figure 2: Smart cities data analytics framework Additionally, due to the huge quantity of data, researchers used deep learning algorithms, especially Transfer learning and Meta-learning [7] and some other famous machine learning techniques to learn within Reinforcement Learning like Q-learning [31,32,33] for generating smart systems [34,35].…”

Section: Discussionmentioning

confidence: 99%

Data Analytics for Smart cities: Challenges and Promises

Mohammadi¹,

Shenavarmasouleh²,

Amini³

et al. 2021

Preprint

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The explosion of advancements in artificial intelligence, sensor technologies, and wireless communication activates ubiquitous sensing through distributed sensors. These sensors are various domains of networks that lead us to smart systems in healthcare, transportation, environment, and other relevant branches/networks. Having collaborative interaction among the smart systems connects end-user devices to each other which enables achieving a new integrated entity called Smart Cities. The goal of this study is to provide a comprehensive survey of data analytics in smart cities. In this paper, we aim to focus on one of the smart cities important branches, namely Smart Mobility, and its positive ample impact on the smart cities decision-making process. Intelligent decision-making systems in smart mobility offer many advantages such as saving energy, relaying city traffic, and more importantly, reducing air pollution by offering real-time useful information and imperative knowledge. Making a decision in smart cities in time is challenging due to various and high dimensional factors and parameters, which are not frequently collected. In this paper, we first address current challenges in smart cities and provide an overview of potential solutions to these challenges. Then, we offer a framework of these solutions, called universal smart cities decision making, with three main sections of data capturing, data analysis, and decision making to optimize the smart mobility within smart cities. With this framework, we elaborate on fundamental concepts of big data, machine learning, and deep leaning algorithms that have been applied to smart cities and discuss the role of these algorithms in decision making for smart mobility in smart cities.

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Section: Discussionmentioning

confidence: 99%

Data Analytics for Smart cities: Challenges and Promises

Mohammadi¹,

Shenavarmasouleh²,

Amini³

et al. 2021

Preprint

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show abstract

“…Deep convolutional neural networks have achieved very successful applications in the field of steganography [11]- [16], [18], [19]. In this paper, we propose a new deep convolution neural network to achieve image steganography, and in our steganography framework, add a pyramid pool module to the hiding network and the reveal network to achieve better results.…”

Section: Related Workmentioning

confidence: 99%

StegoPNet: Image Steganography With Generalization Ability Based on Pyramid Pooling Module

et al. 2020

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In terms of payload capacity and visual effects, the existing image steganography technology based on deep neural networks still needs improvement, to solve this problem, this paper proposes a new deep convolutional steganography network based on the pyramid pooling module to achieve better image steganography. The deep convolutional neural network itself can extract features efficiently. Based on the combination of up-sampling structure, we added a pyramid pool module, under the premise of ensuring safety, fully integrated the previous important global features, achieved good hiding and extraction effects, fully integrated the previous important global features, and effective it reduces the loss of contextual information between different sub-regions in the feature extraction process and achieves better hiding and extraction effects under the premise of ensuring security. Experiments show that the average peak signalto-noise ratio (PSNR)/structure similarity (SSIM) and other indicators between the images obtained by this method have achieved good results in the experiment. Also, we have verified through ablation experiments that the pyramid pooling module can enhance the steganography effect of the network model and can further cut down the loss function of the model. INDEX TERMS Image steganography, deep neural network, pyramid pooling module.

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“…With the further development of deep learning [58][59][60] [37][38][39], the Generative Adversarial Networks (GAN) have been proposed by the literature [40]. The literature [40] is a milestone in the development of deep learning.…”

Section: The Related Workmentioning

confidence: 99%

The image inpainting algorithm used on multi-scale generative adversarial networks and neighbourhood

Zhou

2020

Automatika

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Various problems existed in the image inpainting algorithms, which can't meet people's requirements visually. Aiming at the defects of the existing image inpainting algorithms, such as low accuracy, poor visual consistency, and unstable training, an improved image inpainting algorithm used on a multi-scale generative adversarial network (GAN) and neighbourhood model have been proposed in the paper. The proposed algorithm mainly improves the network structure of the discriminator, and introduces a multi-scale discriminator based on the global discriminator and the local discriminator. The multi-scale discriminators were trained on images of different resolutions. Discriminators of different scales have different receptive fields, which can guide the generator to generate more global image views and finer details. Aiming at the problem of gradient disappearance or gradient explosion that often occurs in GAN training, the method of WGAN (Wasserstein GAN) has been used to simulate the sample data distribution using EM distance. The proposed model has been trained and tested on the CelebA, ImageNet, and Place2. The experimental results show that compared with the previous algorithm model, the proposed algorithm improves the accuracy of image inpainting and can generate more realistic repairing images, and it is suitable for many types of images.

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A Fast Q-Learning Based Data Storage Optimization for Low Latency in Data Center Networks

Cited by 21 publications

References 23 publications

Data Analytics for Smart cities: Challenges and Promises

Data Analytics for Smart cities: Challenges and Promises

StegoPNet: Image Steganography With Generalization Ability Based on Pyramid Pooling Module

The image inpainting algorithm used on multi-scale generative adversarial networks and neighbourhood

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