eprintid: 3820
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/38/20
datestamp: 2023-11-09 15:52:05
lastmod: 2023-11-09 15:52:05
status_changed: 2023-11-09 15:47:41
type: conference_item
metadata_visibility: show
creators_name: Hani, A.F.M.
creators_name: Paputungan, I.V.
creators_name: Hassan, M.F.
title: Support Vector regression for Service Level Agreement violation prediction
ispublished: pub
keywords: Cloud computing; Information science; Support vector machines; Time series; Time series analysis, Customer trust; Historical data; Prediction accuracy; Service Level Agreements; Service performance; Service provider; SLA; Support vector regression (SVR), Forecasting
note: cited By 12; Conference of 2013 International Conference on Computer, Control, Information and Its Applications, IC3INA 2013 ; Conference Date: 19 November 2013 Through 21 November 2013; Conference Code:105680
abstract: SLA is a contract between service providers and consumers, mandating specific numerical target values which the service needs to achieve. For service providers, preventing SLA violation becomes very important to enhance customer trust and avoid penalty charging. Therefore, it is necessary for providers to forecast possible violations as much as possible before they actually happen. Time series analysis based on Support Vector Machine for regression is proposed for predicting SLA violations. It will analyse historical data of performance to provide estimated upcoming data. A validation using 120 days sample data shows that Support Vector Machine could predict service performance data in cloud database. The prediction accuracy is considerably high in this particular case; it is more than 80. © 2013 IEEE.
date: 2013
publisher: IEEE Computer Society
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902456085&doi=10.1109%2fIC3INA.2013.6819192&partnerID=40&md5=fde49ccd71be781be29287f067428774
id_number: 10.1109/IC3INA.2013.6819192
full_text_status: none
publication: Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications: "Recent Challenges in Computer, Control and Informatics", IC3INA 2013
place_of_pub: Jakarta
pagerange: 307-311
refereed: TRUE
isbn: 9781479910786
citation:   Hani, A.F.M. and Paputungan, I.V. and Hassan, M.F.  (2013) Support Vector regression for Service Level Agreement violation prediction.  In: UNSPECIFIED.