scholarly journals Proposal and Investigation of a Convolutional and LSTM Neural Network for the Cost-Aware Resource Prediction in Softwarized Networks

2021 ◽  
Vol 13 (12) ◽  
pp. 316
Author(s):  
Vincenzo Eramo ◽  
Francesco Valente ◽  
Tiziana Catena ◽  
Francesco Giacinto Lavacca
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Network Function Virtualization ◽  
Prediction Algorithm ◽  
Network Function ◽  
Temporal Correlations ◽  
Cloud Resource ◽  
Traffic Scenario ◽  
The Cost ◽  
Resource Prediction

Resource prediction algorithms have been recently proposed in Network Function Virtualization architectures. A prediction-based resource allocation is characterized by higher operation costs due to: (i) Resource underestimate that leads to quality of service degradation; (ii) used cloud resource over allocation when a resource overestimate occurs. To reduce such a cost, we propose a cost-aware prediction algorithm able to minimize the sum of the two cost components. The proposed prediction solution is based on a convolutional and Long Short Term Memory neural network to handle the spatial and temporal correlations of the need processing capacities. We compare in a real network and traffic scenario the proposed technique to a traditional one in which the aim is to exactly predict the needed processing capacity. We show how the proposed solution allows for cost advantages in the order of 20%.

scholarly journals A graph-based CNN-LSTM stock price prediction algorithm with leading indicators

2021 ◽  
Author(s):  
Jimmy Ming-Tai Wu ◽  
Zhongcui Li ◽  
Norbert Herencsar ◽  
Bay Vo ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Financial Management ◽  
Stock Price ◽  
Short Term Memory ◽  
Prediction Algorithm ◽  
Leading Indicators ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Wealth Management

AbstractIn today’s society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people’s favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long–Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly.

Production Forecasting with the Interwell Interference by Integrating Graph Convolutional and Long Short-Term Memory Neural Network

2021 ◽  
pp. 1-17
Author(s):  
Enda Du ◽  
Yuetian Liu ◽  
Ziyan Cheng ◽  
Liang Xue ◽  
Jing Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Production Forecasting ◽  
Temporal Correlations ◽  
Proposed Model ◽  
The Mean ◽  
Long Short Term Memory ◽  
The Impact

Summary Accurate production forecasting is an essential task and accompanies the entire process of reservoir development. With the limitation of prediction principles and processes, the traditional approaches are difficult to make rapid predictions. With the development of artificial intelligence, the data-driven model provides an alternative approach for production forecasting. To fully take the impact of interwell interference on production into account, this paper proposes a deep learning-based hybrid model (GCN-LSTM), where graph convolutional network (GCN) is used to capture complicated spatial patterns between each well, and long short-term memory (LSTM) neural network is adopted to extract intricate temporal correlations from historical production data. To implement the proposed model more efficiently, two data preprocessing procedures are performed: Outliers in the data set are removed by using a box plot visualization, and measurement noise is reduced by a wavelet transform. The robustness and applicability of the proposed model are evaluated in two scenarios of different data types with the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE). The results show that the proposed model can effectively capture spatial and temporal correlations to make a rapid and accurate oil production forecast.

scholarly journals A Novel Stock Index Intelligent Prediction Algorithm Based on Attention-Guided Deep Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yangzi Zhao
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Stock Index ◽  
Prediction Algorithm ◽  
Short Term ◽  
Term Memory ◽  
Model Based

The stock market is affected by economic market, policy, and other factors, and its internal change law is extremely complex. With the rapid development of the stock market and the expansion of the scale of investors, the stock market has produced a large number of transaction data, which makes it more difficult to obtain valuable information. Because deep neural network is good at dealing with the prediction problems with large amount of data and complex nonlinear mapping relationship, this paper proposes an attention-guided deep neural network stock prediction algorithm. This paper synthesizes the daily stock social media text emotion index and stock technology index as the data source and applies them to the long-term and short-term memory neural network (LSTM) model to predict the stock market. The stock emotion index is extracted by constructing a social text classification emotion model of bidirectional long-term and short-term memory neural network (Bi-LSTM) based on attention mechanism and glove word vector representation algorithm. In addition, a dimensionality reduction model based on decision tree (DT) and principal component analysis (PCA) is constructed to reduce the dimensionality of stock technical indicators and extract the main data information. Furthermore, this paper proposes a model based on nasNet for pattern recognition. The recognition results can be used to automatically identify short-term K-line patterns, predict reliable trading signals, and help investors customize short-term high-efficiency investment strategies. The experimental results show that the prediction accuracy of the proposed algorithm can reach 98.6%, which has high application value.

scholarly journals An Efficient Availability Guaranteed Deployment Scheme for IoT Service Chains over Fog-Core Cloud Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3970 ◽  
Author(s):  
Ngoc-Thanh Dinh ◽  
Younghan Kim
Keyword(s):  
Critical Issue ◽  
High Availability ◽  
Network Function Virtualization ◽  
Cloud Networks ◽  
Protection Scheme ◽  
Network Function ◽  
Resource Limitations ◽  
Additional Protection ◽  
Cost Efficient ◽  
The Cost

High availability is one of the important requirements of many end-to-end services in the Internet of Things (IoT). This is a critical issue in network function virtualization (NFV) and NFV-enabled service function chaining (SFC) due to hard- and soft-ware failures. Thus, merely mapping primary VNFs is not enough to ensure high availability, especially for SFCs deployed over fog - core cloud networks due to resource limitations of fogs. As a result, additional protection schemes, like VNF redundancy deployments, are required to improve the availability of SFCs to meet predefined requirements. With limited resources of fog instances, a cost-efficient protection scheme is required. This paper proposes a cost-efficient availability guaranteed deployment scheme for IoT services over fog-core cloud networks based on measuring the improvement potential of VNFs for improving the availability of SFCs. In addition, various techniques for redundancy placement for VNFs at the fog layer are also presented. Obtained analysis and simulation results show that the proposed scheme achieves a significant improvement in terms of the cost efficiency and scalability compared to the state-of-the-art approaches.

scholarly journals Impact of the Maximum Number of Switching Reconfigurations on the Cost Saving in Network Function Virtualization Environments with Elastic Optical Interconnection

2019 ◽  
Vol 9 (23) ◽  
pp. 5167
Author(s):  
Vincenzo Eramo ◽  
Francesco G. Lavacca ◽  
Tiziana Catena
Keyword(s):  
Computational Complexity ◽  
Cost Saving ◽  
Virtual Machines ◽  
New Technology ◽  
Network Function Virtualization ◽  
Optical Interconnection ◽  
Network Function ◽  
Network Functions ◽  
The Cost ◽  
The Impact

Network Function Virtualization is based on the virtualization of the network functions and it is a new technology allowing for a more flexible allocation of cloud and bandwidth resources. In order to employ the flexibility of the technology and to adapt its use according to the traffic variation, reconfigurations of the cloud and bandwidth resources are needed by means of both migration of the Virtual Machines executing the network functions and reconfiguration of circuits interconnecting the Virtual Machines. The objective of the paper is to study the impact of the maximum number of switch reconfigurations on the cost saving that the Networking Function Virtualization technology allows us to achieve. The problem is studied in the case of a scenario with an elastic optical network interconnecting datacenters in which the Virtual Machines are executed. The problem can be formulated as an Integer Linear Programming one introducing a constraint on the maximum number of switch reconfigurations but due to its computational complexity we propose a low computational complexity heuristic allowing for results close to the optimization ones. The results show how the limitation on the number of possible reconfigurations has to be taken into account to evaluate the effectiveness in terms of cost saving that the Virtual Machine migrations in Network Function Virtualization environment allows us to achieve.

scholarly journals Deep Reinforcement Learning-Based Algorithm for VNF-SC Deployment

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Junlei Xuan ◽  
Huifang Yang ◽  
Xuelin Zhao ◽  
Xingpo Ma ◽  
Xiaokai Yang
Keyword(s):  
Real Time ◽  
Optimization Model ◽  
Virtual Network ◽  
Network Function Virtualization ◽  
Prediction Algorithm ◽  
Network Service ◽  
The Real ◽  
Service Chain ◽  
Network Function ◽  
Virtual Network Function

Network function virtualization (NFV) has the potential to lead to significant reductions in capital expenditure and can improve the flexibility of the network. Virtual network function (VNF) deployment problem will be one of key problems that need to be addressed in NFV. To solve the problem of routing and VNF deployment, an optimization model, which minimizes the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF, is established. In this optimization model, the dependency among the different VNFs is considered. In order to solve the service chain mapping problem of high dynamic virtual network, a new virtual network function service chain mapping algorithm PDQN-VNFSC was proposed by combining prediction algorithm and DQN (Deep Q-Network). Firstly, the real-time mapping of virtual network service chains is modeled into a partial observable Markov decision process. Then, the real-time mapping process of virtual network service chain is optimized by using global and long-term benefits. Finally, the service chain of virtual network function is mapped through the learning decision framework of offline learning and online deployment. The simulation results show that, compared with the existing algorithms, the proposed algorithm has a lower the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF.

scholarly journals Reducing the Dependence of the Neural Network Function to Systematic Uncertainties in the Input Space

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Stefan Wunsch ◽  
Simon Jörger ◽  
Roger Wolf ◽  
Günter Quast
Keyword(s):  
Neural Network ◽  
Particle Physics ◽  
High Energy ◽  
High Energy Particle ◽  
Input Space ◽  
Network Function ◽  
Systematic Uncertainties ◽  
The Neural Network ◽  
Network Output ◽  
The Cost

AbstractApplications of neural networks to data analyses in natural sciences are complicated by the fact that many inputs are subject to systematic uncertainties. To control the dependence of the neural network function to variations of the input space within these systematic uncertainties, several methods have been proposed. In this work, we propose a new approach of training the neural network by introducing penalties on the variation of the neural network output directly in the loss function. This is achieved at the cost of only a small number of additional hyperparameters. It can also be pursued by treating all systematic variations in the form of statistical weights. The proposed method is demonstrated with a simple example, based on pseudo-experiments, and by a more complex example from high-energy particle physics.

scholarly journals Multi-Source Reliable Multicast Routing with QoS Constraints of NFV in Edge Computing

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1106 ◽  
Author(s):  
Shiming He He ◽  
Kun Xie ◽  
Xuhui Zhou ◽  
Thabo Semong ◽  
Jin Wang
Keyword(s):  
Heuristic Algorithms ◽  
Multicast Routing ◽  
Multicast Tree ◽  
Edge Computing ◽  
Network Function Virtualization ◽  
Reliable Multicast ◽  
Network Function ◽  
Tree Construction ◽  
Service Function Chain ◽  
The Cost

Edge Computing (EC) allows processing to take place near the user, hence ensuring scalability and low latency. Network Function Virtualization (NFV) provides the significant convenience of network layout and reduces the service operation cost in EC and data center. Nowadays, the interests of the NFV layout focus on one-to-one communication, which is costly when applied to multicast or group services directly. Furthermore, many artificial intelligence applications and services of cloud and EC are generally communicated through groups and have special Quality of Service (QoS) and reliable requirements. Therefore, we are devoted to the problem of reliable Virtual Network Function (VNF) layout with various deployment costs in multi-source multicast. To guarantee QoS, we take into account the bandwidth, latency, and reliability constraints. Additionally, a heuristic algorithm, named Multi-Source Reliable Multicast Tree Construction (RMTC), is proposed. The algorithm aims to find a common link to place the Service Function Chain (SFC) in the multilevel overlay directed (MOD) network of the original network, so that the deployed SFC can be shared by all users, thereby improving the resource utilization. We then constructed a Steiner tree to find the reliable multicast tree. Two real topologies are used to evaluate the performance of the proposed algorithm. Simulation results indicate that, compared to other heuristic algorithms, our scheme effectively reduces the cost of reliable services and satisfies the QoS requirements.

scholarly journals Proactive VNF Scaling with Heterogeneous Cloud Resources: Fusing Long Short-Term Memory Prediction and Cooperative Allocation

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ran Xu
Keyword(s):  
Short Term Memory ◽  
Service Providers ◽  
Network Function Virtualization ◽  
Dynamic Allocation ◽  
Short Term ◽  
Term Memory ◽  
Resource Allocation Algorithm ◽  
Network Function ◽  
Network Functions ◽  
Long Short Term Memory

Network function virtualization (NFV) is designed to implement network functions by software that replaces proprietary hardware devices in traditional networks. In response to the growing demand of resource-intensive services, for NFV cloud service providers, software-oriented network functions face a number of challenges, such as dynamic deployment of virtual network functions and efficient allocation of multiple resources. This study aims at the dynamic allocation and adjustment of network multiresources and multitype flows for NFV. First, to seek a proactive approach to provision new instances for overloaded VNFs ahead of time, a model called long short-term memory recurrent neural network (LSTM RNN) is proposed to estimate flows in this paper. Then, based on the estimated flow, a cooperative and complementary resource allocation algorithm is designed to reduce resource fragmentation and improve the utilization. The final results demonstrate the advantage of the LSTM model on predicting the network function flow requirements, and our algorithm achieves good results and performance improvement in dynamically expanding network functions and improving resource utilization.

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