Online QoS Prediction in the Cloud Environments Using Hybrid Time-Series Data Mining Approach
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RESEARCH PAPER
Online QoS Prediction in the Cloud Environments Using Hybrid TimeSeries Data Mining Approach Amin Keshavarzi1 • Abolfazl Toroghi Haghighat1 • Mahdi Bohlouli2,3,4 Received: 25 October 2019 / Accepted: 15 August 2020 Shiraz University 2020
Abstract Considering the diversity of proposed cloud computing services in federated clouds, users should be very well aware of their current required and future expected resources and values of the quality-of-service parameters to compose proper services from a pool of clouds. Various approaches and methods have been proposed to accurately address this issue and predict the quality-of-service parameters. The quality-of-service parameters are stored in the form of time series. Those works mostly discover patterns either between separate time series or inside specific time series and not both aspects together. The main research gap which is covered in this work is to make use of measuring similarities inside the current time series as well as between various time series. This work proposes a novel hybrid approach by means of time-series clustering, minimum description length, and dynamic time warping similarity to analyze user needs and provide the best-fit quality-of-service prediction solution to the users through the multi-cloud. We considered the time as one of our important factors, and the system analyzes the changes over time. Furthermore, our proposed method is a shape-based prediction that uses dynamic time warping for covering geographical time zone differences with the novel preprocessing method using statistically generated semi-real data to fulfill noisy data. The experimental results of the proposed approach show very close predictions to the real values from practices. We achieved about 0.5 mean absolute error rate on average. For this work, we used the WS-DREAM dataset which is widely used in this area. Keywords Service composition Quality of service Multi-cloud Exponentially weighted moving average Time-series clustering Minimum description length Dynamic time warping
1 Introduction & Abolfazl Toroghi Haghighat [email protected] Amin Keshavarzi [email protected] Mahdi Bohlouli [email protected] 1
Department of Computer Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
2
Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
3
Research and Innovation Department, Petanux GmbH, Bonn, Germany
4
Research Center for Basic Sciences and Modern Technologies (RBST), Institute for Advanced Studies in Basic Sciences, Zanjan, Iran
Today, cloud computing is used in various domains for many different goals and applications, ranging from simulations, design, and research activities (Bohlouli and Analoui 2008), healthcare (Bohlouli et al. 2011, 2014), and support services such as customer support or human resource management. Cloud computing has the following four deployment models: public, private, hybrid, and community (Mell and Grance 2011). I
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