Deep reinforcement learning for multi-objective placement of virtual machines in cloud datacenters

Caviglione, Luca; Gaggero, Mauro; Paolucci, Massimo; Ronco, Roberto

doi:10.1007/s00500-020-05462-x

Cited by 43 publications

(25 citation statements)

References 55 publications

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“…In the future, we will explore in at least two directions based on proposed method. The first one is to explore the clear image without reference for training the dehazing network [45,46,51,52]. The second is to explore video dehazing [47][48][49][50].…”

Section: Discussionmentioning

confidence: 99%

GGADN: Guided generative adversarial dehazing network

2021

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Image dehazing has always been a challenging topic in image processing. The development of deep learning methods, especially the Generative Adversarial Networks(GAN), provides a new way for image dehazing. In recent years, many deep learning methods based on GAN have been applied to image dehazing. However, GAN has two problems in image dehazing. Firstly, For haze image, haze not only reduces the quality of the image, but also blurs the details of the image. For Gan network, it is difficult for the generator to restore the details of the whole image while removing the haze. Secondly, GAN model is defined as a minimax problem, which weakens the loss function. It is difficult to distinguish whether GAN is making progress in the training process. Therefore, we propose a Guided Generative Adversarial Dehazing Network(GGADN). Different from other generation adversarial networks, GGADN adds a guided module on the generator. The guided module verifies the network of each layer of the generator. At the same time, the details of the map generated by each layer are strengthened. Network training is based on the pre-trained VGG feature model and L1-regularized gradient prior which is developed by new loss function parameters. From the dehazing results of synthetic images and real images, proposed method is better than the state-of-the-art dehazing methods.

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Section: Discussionmentioning

confidence: 99%

GGADN: Guided generative adversarial dehazing network

2021

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“…In order to ensure network performance while minimizing power consumption, Caviglione et al [187] applied a DRL algorithm, named Rainbow DQN, to solve the multi-objective VM placement problem. Their model was based on the percentages of network capacity, CPU, and disk, with full consideration of energy cost, network security, and QoS.…”

Section: Resource Managementmentioning

confidence: 99%

Machine Learning Empowered Intelligent Data Center Networking: A Survey

Li¹,

Wang²,

Yang³

et al. 2022

Preprint

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“…Deep learning (Shrestha and Mahmood 2019) and deep reinforcement learning (Arulkumaran et al 2017;Caviglione et al 2020) have gained much interest in recent years. Kastius and Schlosser (2021) employ deep reinforcement learning for dynamic pricing.…”

Section: Studies Of the Exploration-exploitation Trade-offmentioning

confidence: 99%

Novel pricing strategies for revenue maximization and demand learning using an exploration–exploitation framework

2021

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The price demand relation is a fundamental concept that models how price affects the sale of a product. It is critical to have an accurate estimate of its parameters, as it will impact the company's revenue. The learning has to be performed very efficiently using a small window of a few test points, because of the rapid changes in price demand parameters due to seasonality and fluctuations. However, there are conflicting goals when seeking the two objectives of revenue maximization and demand learning, known as the learn/earn trade-off. This is akin to the exploration/exploitation trade-off that we encounter in machine learning and optimization algorithms. In this paper, we consider the problem of price demand function estimation, taking into account its exploration-exploitation characteristic. We design a new objective function that combines both aspects. This objective function is essentially the revenue minus a term that measures the error in parameter estimates. Recursive algorithms that optimize this objective function are derived. The proposed method outperforms other existing approaches.

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Deep reinforcement learning for multi-objective placement of virtual machines in cloud datacenters

Cited by 43 publications

References 55 publications

GGADN: Guided generative adversarial dehazing network

GGADN: Guided generative adversarial dehazing network

Machine Learning Empowered Intelligent Data Center Networking: A Survey

Novel pricing strategies for revenue maximization and demand learning using an exploration–exploitation framework

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