- An Efficient Deep Learning Model to Predict Cloud Workload for ...🔍
- An Efficient Workload Prediction Model in Cloud Computing Using ...🔍
- a self|adaptive deep learning|based model to predict cloud workload🔍
- A deep learning approach for VM workload prediction in the cloud🔍
- Deep CNN and LSTM Approaches for Efficient Workload Prediction ...🔍
- A Self|Optimized Generic Workload Prediction Framework for Cloud ...🔍
- Optimized Hierarchical Tree Deep Convolutional Neural Network of ...🔍
- Deep Learning Approach for Workload Prediction and Balancing in ...🔍
An Efficient Deep Learning Model to Predict Cloud Workload for ...
An Efficient Deep Learning Model to Predict Cloud Workload for ...
In this paper, an efficient deep learning model based on the canonical polyadic decomposition is proposed to predict the cloud workload for ...
An Efficient Deep Learning Model to Predict Cloud Workload for ...
An efficient deep learning model based on the canonical polyadic decomposition is proposed to predict the cloud workload for industry informatics and ...
An Efficient Deep Learning Model to Predict Cloud Workload for ...
In this paper, an efficient deep learning model based on the canonical polyadic decomposition is proposed to predict the cloud workload for industry informatics ...
An Efficient Workload Prediction Model in Cloud Computing Using ...
Z. Chen et al. (2015) “Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network”, ...
An Efficient Deep Learning Model to Predict Cloud Workload for ...
Published in. Institute of Electrical and Electronics Engineers, IEEE Transactions on Industrial Informatics, 7(14), p. 3170-3178, 2018.
An Efficient Deep Learning Model to Predict Cloud Workload for ...
An Efficient Deep Learning Model to Predict Cloud Workload for Industry Informatics · Dalian University of Technology · Saint Francis Xavier University · Xidian ...
a self-adaptive deep learning-based model to predict cloud workload
Recent research has led us to a significant improvement in workload prediction. Although self-adaptive systems have an imperative impact on lowering the number.
A deep learning approach for VM workload prediction in the cloud
The proposed approach utilizes a deep learning model consisting of a Deep Belief Network (DBN) and a logistic regression layer. ... ... A set of VMs may be ...
esDNN: Deep Neural Network Based Multivariate Workload ...
An efficient supervised learning-based Deep Neural Network (esDNN) algorithm is proposed for cloud workload prediction to learn and capture the features of ...
Deep CNN and LSTM Approaches for Efficient Workload Prediction ...
We proposed a Model based on Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) for handling SLAs in the cloud from both the ...
A Self-Optimized Generic Workload Prediction Framework for Cloud ...
several attempts to apply deep learning models to predict cloud workloads [51]–[56]. ... An Efficient Deep Learning Model to PredictCloud Workload for. Industry ...
EsDNN: Deep Neural Network based Multivariate Workload ... - arXiv
The experimental results demonstrate that esDNN can accurately and efficiently predict cloud workloads. Compared with the state-of-the-art ...
A deep learning approach for VM workload prediction in the cloud
Experimental results show that the proposed approach for VM workload prediction based on deep learning improves the workload prediction performance compared ...
Optimized Hierarchical Tree Deep Convolutional Neural Network of ...
The workload prediction model is created to increase cloud efficiency and minimize energy consumption. However, the current models could be ...
Deep Learning Approach for Workload Prediction and Balancing in ...
Our work aims to address this gap and improve efficiency by proposing a Deep Max-out prediction model, which predicts the future workload and facilitates ...
VTGAN: hybrid generative adversarial networks for cloud workload ...
The main challenge in cloud prediction is the need for an effective nonlinear model that tracks the cloud workload [45, 79]. Furthermore, the ...
Deep Neural Network Based Multivariate Workload Prediction in ...
An efficient supervised learning-based Deep Neural Network (esDNN) algorithm is proposed for cloud workload prediction to learn and capture the features of ...
Technical Study of Deep Learning in Cloud Computing for Accurate ...
Workload prediction using Deep Learning (DL) is a popular method of inferring complicated multidimensional data of cloud environments to meet this requirement.
Dynamic Workload Prediction based Phd Proposal in Cloud - S-Logix
Conventional machine learning-based workload prediction models do not effectively predict the workload, and it was confronted with several challenges due to the ...
Microservice-Oriented Workload Prediction Using Deep Learning
Such a prediction mechanism is necessary since in order to fully take advantage of on-demand resources and reduce manual tuning, an auto-scaling, preferable ...