Predicting QoS of virtual machines via Bayesian network with XGboost-induced classes
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Predicting QoS of virtual machines via Bayesian network with XGboost-induced classes Jia Hao1 • Kun Yue1
•
Liang Duan1 • Binbin Zhang1 • Xiaodong Fu2
Received: 7 February 2020 / Revised: 27 August 2020 / Accepted: 3 September 2020 Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Quality of Service (QoS) of virtual machines (VMs) is guaranteed by the Service Level Agreements (SLAs) signed between users and service providers during the renting of VMs. A typical idea to ensure the SLAs being reached is to predict the QoS of VMs accurately and then take the appropriate measures according to the prediction results timely. However, the QoS is affected by multiple VM-related features, among which the uncertain and non-linear relationships are challenging to represent and analyze. Thus, in this paper, we construct a class parameter augmented Bayesian Network (CBN) to overcome the difficulties and then predict the QoS of VMs accurately. Specifically, we first cluster multiple VMrelated features based on the Euclidean distance, and then use XGboost to classify the different VM configurations within each cluster. Then, we construct the CBN based on the classification results as well as the corresponding QoS values. Consequently, we predict the QoS of VMs via the variable elimination (VE) with CBN. Experimental results show the efficiency and effectiveness of our proposed method on predicting the QoS of VMs. Keywords Virtual machine Quality of service QoS prediction Bayesian network XGboost
1 Introduction Infrastructure as a Service (IaaS) is an on-demand resource usage mode for cloud computing [1]. Through virtualization technology, IaaS servers offer the physical resources in form of virtual machines (VMs) for users to rent and then pay [2, 3], like Amazon Elastic Cloud Computing (EC2).1 During the provision of VMs, servers should sign the Service Level Agreements (SLAs) with users to guarantee the Quality of Service (QoS) to be satisfied, and then any violations of the SLAs will bring the economic or reputation damage to the former [4, 5]. Generally, the service providers often take the following two methods to avoid the SLA violations. First, when the violation occurs, such as the response time of a VM & Kun Yue [email protected] 1
School of Information Science and Engineering, Yunnan University, Kunming, China
2
College of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
exceeding the prescribed 0.1 s, the VMs are replaced immediately. This mode is known as reactive. Second, the cloud providers predict the QoS of the VMs accurately, and then replace the VMs that may cause the SLAs violations in advance. This mode is known as proactive. There is no doubt that proactive mode is more reasonable since it avoids violations in advance and does not waste extra resources. Obviously, accurate prediction of QoS of VMs is a prerequisite to ensure the proactive mode operating smoothly [6]. As p
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