A Classification-Based Demand Trend Prediction Model in Cloud Computing

2015 ◽  
pp. 442-457 ◽  
Author(s):  
Qifeng Zhou ◽  
Bin Xia ◽  
Yexi Jiang ◽  
Qianmu Li ◽  
Tao Li
Keyword(s):  
Cloud Computing ◽  
Prediction Model ◽  
Trend Prediction

scholarly journals RVLBPNN: A Workload Forecasting Model for Smart Cloud Computing

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Yao Lu ◽  
John Panneerselvam ◽  
Lu Liu ◽  
Yan Wu
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Service Providers ◽  
Cloud Service ◽  
Backpropagation Neural Network ◽  
Environmental Implications ◽  
Cloud Service Providers ◽  
Cloud Workloads

Given the increasing deployments of Cloud datacentres and the excessive usage of server resources, their associated energy and environmental implications are also increasing at an alarming rate. Cloud service providers are under immense pressure to significantly reduce both such implications for promoting green computing. Maintaining the desired level of Quality of Service (QoS) without violating the Service Level Agreement (SLA), whilst attempting to reduce the usage of the datacentre resources is an obvious challenge for the Cloud service providers. Scaling the level of active server resources in accordance with the predicted incoming workloads is one possible way of reducing the undesirable energy consumption of the active resources without affecting the performance quality. To this end, this paper analyzes the dynamic characteristics of the Cloud workloads and defines a hierarchy for the latency sensitivity levels of the Cloud workloads. Further, a novel workload prediction model for energy efficient Cloud Computing is proposed, named RVLBPNN (Rand Variable Learning Rate Backpropagation Neural Network) based on BPNN (Backpropagation Neural Network) algorithm. Experiments evaluating the prediction accuracy of the proposed prediction model demonstrate that RVLBPNN achieves an improved prediction accuracy compared to the HMM and Naïve Bayes Classifier models by a considerable margin.

Pressure Prediction in Natural Gas Desulfurization Process Based on PCA and SVR

2014 ◽  
Vol 962-965 ◽  
pp. 564-569 ◽  
Author(s):  
Yan Chao Shao ◽  
Liang Jun Xu ◽  
Yan Zhu Hu ◽  
Xin Bo Ai
Keyword(s):  
Natural Gas ◽  
Prediction Model ◽  
Measured Data ◽  
Support Vector ◽  
Trend Prediction ◽  
Generalization Ability ◽  
Model Based ◽  
Scada System ◽  
Desulfurization Process ◽  
Natural Gas Desulfurization

Pressure monitoring is an important means to reflect the running status of the natural gas desulphurization process. By using the data mining technology, the interaction relationships between the pressure and other monitoring parameters are analyzed in this paper. A pressure trend prediction model is established to show the pressure status in the natural gas desulfurization process. Firstly, the theory of Principal Component Analysis (PCA) is used to reduce the dimensions of measured data from traditional Supervisory Control and Data Acquisition (SCADA) system. Secondly the principal components are taken as input data into the pressure trend prediction model based on multiple regression theory of Support Vector Regression (SVR). Finally the accuracy and the generalization ability of the model are tested by the measured data obtained from SCADA system. Compared with other prediction models, pressure trend prediction model based on PCA and SVR gets smaller MSE and higher correlation. The pressure trend prediction model gets better generalization ability and stronger robustness, and is an effective complement to SCADA system in the natural gas desulphurization process.

scholarly journals An Improved Prediction Model Combining Inverse Exponential Smoothing and Markov Chain

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Tong Niu ◽  
Lin Zhang ◽  
Bo Zhang ◽  
Bofan Yang ◽  
Shengjun Wei ◽  
...  
Keyword(s):  
Markov Chain ◽  
Prediction Model ◽  
Threshold Value ◽  
Exponential Smoothing ◽  
Initial Value ◽  
Trend Prediction ◽  
Index Model ◽  
Model Combining ◽  
Improved Model ◽  
Setting Parameters

On the basis of the triple exponential smoothing prediction model, this paper introduces the reverse prediction idea and establishes the reverse triple exponential smoothing model by setting parameters such as threshold value and iteration times and reasonably correcting its initial value. This method can effectively reduce the error of early prediction value. At the same time, aiming at the problem that the predicting advantage of the reverse triple exponential smoothing model weakens in the later period, Markov theory is introduced to correct its error value, and an improved prediction model combining inverse exponential smoothing and Markov chain is further established. The improved model combines the advantages of index model trend prediction and Markov fluctuation prediction, and the prediction accuracy and stability of the model are significantly improved through case tests.

scholarly journals Study of Economic Management Forecast and Optimized Resource Allocation Based on Cloud Computing and Neural Network

2020 ◽  
Author(s):  
Pinzhen He
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Cloud Computing ◽  
Linear Mapping ◽  
Global Market ◽  
Operating Efficiency ◽  
Economic Forecast ◽  
Economic Management ◽  
Trend Prediction ◽  
Allocation Model

Abstract As various factors which affect the development of global market economy become increasingly uncertain, economy and commodity will become more and more fluctuating in economic operation. With its strong non-linear mapping capacity, artificial neural network has already been applied in many fields, time series analysis and trend prediction. Cloud computing can interact fast with service provider at the minimum management cost. This paper proposes an economic forecast and optimized resource allocation model based on cloud computing and BP neural network. Its main goal is to break down a complex prediction task into several sub-tasks, effectively reduce the workload of a single computer and enhance the operating efficiency. Simulation results show that the proposed method does not rely on gradient information, and has strong optimization calculation ability. At the same time, it can analyze and predict economic management, so as to provide strong decision support for decision makers.

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