Predicting the Total Workload in Telecommunications by SVMs

The contributions include: (a) Building a predicted model of the total workload in telecommunications and predicting using it; (b)Analyzing the parameter of ...

(PDF) Predicting the Total Workload in Telecommunications by SVMs

PDF | As a learning mechanic, support vector machine (SVMs) has been studied and applied in a wide area. This study deals with the special futures of.

Predicting the Total Workload in Telecommunications by SVMs. Mingfang Zhu1,2,Changjie Tang1, Shucheng Dai1,Yong Xiang1,Shaojie Qiao1. ,Chen Yu1. (1. School of ...

Predicting the Total Workload in Telecommunications by SVMs ...

As a learning mechanic, support vector machine (SVMs) has been studied and applied in a wide area. This study deals with the special futures of SVM in ...

SVM BASED CHURN ANALYSIS FOR TELECOMMUNICATION

The model aim to analysis total number of subscriber in voice call routing along the period. The churn analysis can predict the churn rate and the probability ...

Using support vector machines for time series prediction

In this paper, time series prediction is performed by support vector machines (SVMs), Elman recurrent neural networks, and autoregressive moving average (ARMA) ...

Effective priority-based resource allocation for proactive auto-scaling ...

To optimize resource allocation, this paper proposes a priority-based auto-scaling framework using machine learning (ML) for workload prediction ...

A Low-Overhead Online Workload Prediction Framework for Cloud ...

2 illustrates the overall workflow of CloudBruno. As. Fig. 2 shows, for every nS interval, a SVM model will be retrained (Step 1). In the current CloudBruno ...

Workload Prediction Using VMD and TCN in Cloud Computing

Regression (LR), Neural Network (NN) and Support. Vector Machines (SVM) in the multi-tier web application environment. Results showed that SVM predicts better.

Workload Failure Prediction for Data Centers - arXiv

We further analyze the CPU time consumed by failed workloads and find that failed workloads cost 21.1% of the total CPU time. The proportion of ...

A survey on predicting workloads and optimizing QoS in the cloud ...

The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the ...

Workload Prediction for IoT Data Management Systems

Communication between containers is established using the. Containernet network emulation [PKvR16]. In total, the topology consists of one coordinator, four ...

Learning-based Phase-aware Multi-core CPU Workload Forecasting

Finally, with knowledge of future phase behavior, phase-specific forecasts are concatenated and assembled to reconstruct the forecast for the overall time ...

Machine Learning Based Workload Prediction in Cloud Computing

A clustering based workload prediction method, which first clusters all the tasks into several categories and then trains a prediction model for each ...

Churn prediction in telecommunication industry using kernel ...

In this study, we proposed an advanced churn prediction model using kernel Support Vector Machines (SVM) algorithm for a telecom company.

A Self-Optimized Generic Workload Prediction Framework for Cloud ...

(b) Prediction errors for the Wikipedia (Wiki) and Google (GL) workloads, as well as the overall average MAPE for all workload configurations. Fig. 9.

A workload prediction model for reducing service level agreement ...

A GAN/LSTM deep learning architecture is proposed to predict each sub-band workload time series individually. LSTM has been widely used for prediction purposes ...

An adaptive prediction approach based on workload pattern ...

However, because the network traffics are usually mixed and inseparable, it is hard to get the satisfactory prediction performance by means of a single model.

Support vector machine - Wikipedia

It is not clear that SVMs have better predictive performance than other linear models, such as logistic regression and linear regression.

Multi-Level Driver Workload Prediction using Machine Learning and ...

The second step was to estimate the model performance within the entire group. The dataset containing data from all drivers was split into ...