relation: https://khub.utp.edu.my/scholars/3820/
title: Support Vector regression for Service Level Agreement violation prediction
creator: Hani, A.F.M.
creator: Paputungan, I.V.
creator: Hassan, M.F.
description: 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.
publisher: IEEE Computer Society
date: 2013
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Hani, A.F.M. and Paputungan, I.V. and Hassan, M.F.  (2013) Support Vector regression for Service Level Agreement violation prediction.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902456085&doi=10.1109%2fIC3INA.2013.6819192&partnerID=40&md5=fde49ccd71be781be29287f067428774
relation: 10.1109/IC3INA.2013.6819192
identifier: 10.1109/IC3INA.2013.6819192