eprintid: 8771
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/87/71
datestamp: 2023-11-09 16:20:41
lastmod: 2023-11-09 16:20:41
status_changed: 2023-11-09 16:13:29
type: conference_item
metadata_visibility: show
creators_name: Hani, A.F.M.
creators_name: Paputungan, I.V.
creators_name: M Fadzil, H.
creators_name: Vijanth, S.A.
title: Manifold learning in SLA violation detection and prediction for cloud-based system
ispublished: pub
keywords: Cloud computing; Forecasting; Internet of things; Internet service providers; Time series analysis, High dimensionality; Manifold; Prediction accuracy; Prediction process; Service violations; SLA Violation; Support vector regression (SVR); Violation detections, Quality of service
note: cited By 0; Conference of 2nd International Conference on Internet of Things and Cloud Computing, ICC 2017 ; Conference Date: 22 March 2017 Through 23 March 2017; Conference Code:134890
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
publisher: Association for Computing Machinery
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044657329&doi=10.1145%2f3018896.3056800&partnerID=40&md5=d9849939d7fb039696c214d8c7e3b8af
id_number: 10.1145/3018896.3056800
full_text_status: none
publication: ACM International Conference Proceeding Series
refereed: TRUE
isbn: 9781450347747
citation:   Hani, A.F.M. and Paputungan, I.V. and M Fadzil, H. and Vijanth, S.A.  (2017) Manifold learning in SLA violation detection and prediction for cloud-based system.  In: UNSPECIFIED.