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primary topic: Manifold learning in SLA violation detection and prediction for cloud-based system

Manifold learning in SLA violation detection and prediction for cloud-based system
type: AcademicArticle
type: Article
abstract: SLA is a contract between service providers and consumers, mandating specific numerical target values which the service needs to achieve. For service providers, detecting and preventing SLA violation becomes very important to enhance customer trust and avoid penalty charges. Therefore, it is necessary for providers to detect and forecast possible service violations. However, this is difficult to achieve when dealing with service violation in a cloudbased system due to multiple Quality of Service (QoS) parameters. In this work, manifold learning is used to reduce the high dimensionality problem arising from multiple QoS data into 1-D output of violation level (low, medium, high) data. From the transformed data, service violation will be detected as well as predicted based on violation level data. The violation level is obtained from aggregate value of each QoS weightage. Based on QoS data of 14 days, manifold learning is able to scale down 5 various parameters into a single parameter before detection and prediction process is performed. The prediction accuracy of Support Vector Regression as the time series analysis technique used is able to achieve 80. © 2017 ACM.
date: 2017
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ConferenceItemEPrint
is part of: repository

Association for Computing Machinery
type: Organization

ACM International Conference Proceeding Series
type: Collection

I.V. Paputungan
type: Person
given name: I.V.
family name: Paputungan

S.A. Vijanth
type: Person
given name: S.A.
family name: Vijanth

A.F.M. Hani
type: Person
given name: A.F.M.
family name: Hani

H. M Fadzil
type: Person
given name: H.
family name: M Fadzil

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primary topic: Manifold learning in SLA violation detection and prediction for cloud-based system