TY  - CONF
A1  - Hani, A.F.M.
A1  - Paputungan, I.V.
A1  - M Fadzil, H.
A1  - Vijanth, S.A.
N2  - 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.
Y1  - 2017///
N1  - 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
TI  - Manifold learning in SLA violation detection and prediction for cloud-based system
SN  - 9781450347747
PB  - Association for Computing Machinery
AV  - none
ID  - scholars8771
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044657329&doi=10.1145%2f3018896.3056800&partnerID=40&md5=d9849939d7fb039696c214d8c7e3b8af
KW  - Cloud computing; Forecasting; Internet of things; Internet service providers; Time series analysis
KW  -  High dimensionality; Manifold; Prediction accuracy; Prediction process; Service violations; SLA Violation; Support vector regression (SVR); Violation detections
KW  -  Quality of service
ER  -