A Smartphone Crowdsensing System Enabling Environmental Crowdsourcing for Municipality Resource Allocation with LSTM Stochastic Prediction

Resource allocation of the availability of certain departments for dealing with emergency recovery is of high importance in municipalities. Efficient planning for facing possible disasters in the coverage area of a municipality provides reassurance for citizens. Citizens can assist with such malfunctions by acting as human sensors at the edge of an infrastructure to provide instant feedback to the appropriate departments fixing the problems. However, municipalities have limited department resources to handle upcoming emergency events. In this study, we propose a smartphone crowdsensing system that is based on citizens’ reactions as human sensors at the edge of a municipality infrastructure to supplement malfunctions exploiting environmental crowdsourcing location-allocation capabilities. A long short-term memory (LSTM) neural network is incorporated to learn the occurrence of such emergencies. The LSTM is able to stochastically predict future emergency situations, acting as an early warning component of the system. Such a mechanism may be used to provide adequate department resource allocation to treat future emergencies.

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Adapted Convolutional Neural Networks and Long Short-Term Memory for Host Utilization prediction in Cloud Data Center

10.21203/rs.3.rs-597475/v1 ◽

2021 ◽

Author(s):

ARIF ullah ◽

Irshad Ahmed Abbasi ◽

Muhammad Zubair Rehman ◽

Tanweer Alam ◽

Hanane Aznaoui

Keyword(s):

Neural Network ◽

Resource Allocation ◽

Cloud Computing ◽

Data Center ◽

Short Term Memory ◽

Short Term ◽

Cloud Data Center ◽

Term Memory ◽

Cloud Data ◽

Long Short Term Memory

Abstract Infrastructure service model provides different kinds of virtual computing resources such as networking, storage service, and hardware as per user demands. Host load prediction is an important element in cloud computing for improvement in the resource allocation systems. Hosting initialization issues still exist in cloud computing due to this problem hardware resource allocation takes serval minutes of delay in the response process. To solve this issue prediction techniques are used for proper prediction in the cloud data center to dynamically scale the cloud in order for maintaining a high quality of services. Therefore in this paper, we propose a hybrid convolutional neural network long with short-term memory model for host prediction. In the proposed hybrid model, vector auto regression method is firstly used to input the data for analysis which filters the linear interdependencies among the multivariate data. Then the enduring data are computed and entered into the convolutional neural network layer that extracts complex features for each central processing unit and virtual machine usage components after that long short-term memory is used which is suitable for modeling temporal information of irregular trends in time series components. In all process, the main contribution is that we used scaled polynomial constant unit activation function which is most suitable for this kind of model. Due to the higher inconsistency in data center, accurate prediction is important in cloud systems. For this reason in this paper two real-world load traces were used to evaluate the performance. One is the load trace in the Google data center, while the other is in the traditional distributed system. The experiment results show that our proposed method achieves state-of-the-art performance with higher accuracy in both datasets as compared with ARIMA-LSTM, VAR-GRU, VAR-MLP, and CNN models.

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Application of a Long Short Term Memory neural predictor with asymmetric loss function for the resource allocation in NFV network architectures

Computer Networks ◽

10.1016/j.comnet.2021.108104 ◽

2021 ◽

Vol 193 ◽

pp. 108104

Author(s):

Vincenzo Eramo ◽

Francesco Giacinto Lavacca ◽

Tiziana Catena ◽

Paul Jaime Perez Salazar

Keyword(s):

Resource Allocation ◽

Loss Function ◽

Short Term Memory ◽

Network Architectures ◽

Short Term ◽

Term Memory ◽

Asymmetric Loss ◽

Long Short Term Memory ◽

Asymmetric Loss Function

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Fractional Rider Deep Long Short Term Memory Network for Workload Prediction-Based Distributed Resource Allocation Using Spark in Cloud Gaming

Engineering ◽

10.4236/eng.2021.133011 ◽

2021 ◽

Vol 13 (03) ◽

pp. 135-157

Author(s):

Koné Kigninman Désiré ◽

Kouassi Adlès Francis ◽

Konan Hyacinthe Kouassi ◽

Eya Dhib ◽

Nabil Tabbane ◽

...

Keyword(s):

Resource Allocation ◽

Short Term Memory ◽

Short Term ◽

Workload Prediction ◽

Distributed Resource Allocation ◽

Term Memory ◽

Memory Network ◽

Long Short Term Memory

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Long-Short Term Memory Networks For Resource Allocation Forecasting in Wifi Networks

2019 6th NAFOSTED Conference on Information and Computer Science (NICS) ◽

10.1109/nics48868.2019.9023880 ◽

2019 ◽

Author(s):

Ta Anh Son ◽

Nguyen Thi Phuong ◽

Le The Phong

Keyword(s):

Resource Allocation ◽

Short Term Memory ◽

Short Term ◽

Wifi Networks ◽

Term Memory ◽

Long Short Term Memory

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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