An ensemble CPU load prediction algorithm using a Bayesian information criterion and smooth filters in a cloud computing environment

2018 ◽  
Vol 48 (12) ◽  
pp. 2257-2277 ◽  
Author(s):  
Sajjad Tofighy ◽  
Ali A. Rahmanian ◽  
Mostafa Ghobaei-Arani
Keyword(s):  
Cloud Computing ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Prediction Algorithm ◽  
Load Prediction ◽  
Computing Environment ◽  
Cloud Computing Environment

scholarly journals Performance of Noise Map Service Working in Cloud Computing Environment

2016 ◽  
Vol 41 (2) ◽  
pp. 297-302 ◽  
Author(s):  
Karolina Marciniuk ◽  
Maciej Szczodrak ◽  
Bożena Kostek
Keyword(s):  
Cloud Computing ◽  
Web Application ◽  
Prediction Models ◽  
Traffic Noise ◽  
Prediction Algorithm ◽  
Noise Prediction ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Map Service ◽  
Noise Map

Abstract In the paper, a noise map service designated for the user interested in environmental noise is presented. Noise prediction algorithm and source model, developed for creating acoustic maps, are working in the cloud computing environment. In the study, issues related to the noise modelling of sound propagation in urban spaces are discussed with a particular focus on traffic noise. Examples of results obtained through a web application created for that purpose are shown. In addition, these are compared to results obtained from the commercial software simulations based on two road noise prediction models. Moreover, the computing performance of the developed application is investigated and analyzed. In the paper, a flowchart simulating the operation of the noise web-based service is presented showing that the created application is easy to use even for people with little experience in computer technology.

scholarly journals Load Forecasting Method Based on Improved Deep Learning in Cloud Computing Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Kai Zhang ◽  
Wei Guo ◽  
Jian Feng ◽  
Mei Liu
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Clustering Algorithm ◽  
Load Forecasting ◽  
Load Prediction ◽  
Computing Environment ◽  
Density Peak ◽  
Data Set ◽  
Cloud Computing Environment ◽  
Forecasting Method

For the problems of low accuracy and low efficiency of most load forecasting methods, a load forecasting method based on improved deep learning in cloud computing environment is proposed. Firstly, the preprocessed data set is divided into several data partitions with relatively balanced data volume through spatial grid, so as to better detect abnormal data. Then, the density peak clustering algorithm based on spark is used to detect abnormal data in each partition, and the local clusters and abnormal points are merged. The parallel processing of data is realized by using spark cluster computing platform. Finally, the deep belief network is used for load classification, and the classification results are input into the empirical mode decomposition-gating recurrent unit network model, and the load prediction results are obtained through learning. Based on the load data of a power grid, the experimental results demonstrate that the mean prediction error of the proposed method is basically controlled within 3% in the short term and 0.023 MW, 19.75%, and 2.76% in the long term, which are better than other comparison methods, and the parallel performance is good, which has a certain feasibility.

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

2017 ◽  
Vol 7 (7) ◽  
pp. 137
Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Computing Environment ◽  
Scientific Application ◽  
Cloud Computing Environment ◽  
Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

Sign in / Sign up

Export Citation Format

Share Document