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An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds

Published on May 2015 by A. Stanislas, L. Arockiam
An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds
Foundation of Computer Science USA
ICCTAC2015 - Number 1
May 2015
Authors: A. Stanislas, L. Arockiam
817e4860-f161-481d-95c8-a09c38a14ae6

A. Stanislas, L. Arockiam . An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds. An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds. ICCTAC2015, 1 (May 2015), 5-9.

@article{
author = { A. Stanislas, L. Arockiam },
title = { An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds },
journal = { An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds },
issue_date = { May 2015 },
volume = { ICCTAC2015 },
number = { 1 },
month = { May },
year = { 2015 },
issn = 0975-8887,
pages = { 5-9 },
numpages = 5,
url = { /proceedings/icctac2015/number1/20917-2003/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds
%A A. Stanislas
%A L. Arockiam
%T An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds
%J An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds
%@ 0975-8887
%V ICCTAC2015
%N 1
%P 5-9
%D 2015
%I International Journal of Computer Applications
Abstract

Cloud computing is a wonderful paradigm which assures the customers of providing computing resources instantly whenever they are in need. It is the virtualization technology that makes this paradigm a reality. But the present technology which is used for provisioning virtual machines is not adequate. Thus, there is latency in service provisioning and the long waiting time of virtual machine provisioning hampers the future popularity of cloud computing. So, high scalability which is the key factor of cloud computing is not easily possible. Therefore, there is a need for a mechanism to enable the service provisioning effectively with high scalability. In view of that, this paper presents a system which predicts the workload demands of the service requests automatically so as to prepare the virtual machines in advance in order to ensure the customers with instant services efficiently without much delay. Trend value analysis using various methods is carried out in the prediction system.

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Index Terms

Computer Science
Information Sciences

Keywords

Scalability Workload Virtualization Technology Autopred Prediction System