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Dovetail Usage Prediction Model for Resource-Efficient Virtual Machine Placement in Cloud Computing Environment
Hyeongbin Kang, Hyeon-Jin Yu, Jungbin Kim, Heeseok Jeong, Jae-Hyuck Shin, Seo-Young Noh
http://doi.org/10.5626/JOK.2023.50.12.1041
As IT services have migrated to the cloud, efficient resource management in cloud computing environments has become an important issue. Consequently, research has been conducted on virtual machine placement(VMP), which can increase resource efficiency without the need for additional equipment in data centers. This paper proposes the use of a usage prediction model as a method for selecting and deploying hosts suitable for virtual machine placement. The dovetail usage prediction model, which improves the shortcomings of the existing usage prediction models, measures indicators such as CPU, disk, and memory usage of virtual machines running on hosts and extracts features using a deep learning model by converting them into time series data. By utilizing this approach in virtual machine placement, hosts can be used efficiently while ensuring appropriate load balancing of the virtual machines.
Performance Analysis of CERN EOS Distributed File System under Bare-metal and Virtualization Environments
Jun-Yeong Lee, Moon-Hyun Kim, Kyeong-Jun Kim, Seo-Young Noh
http://doi.org/10.5626/JOK.2022.49.3.189
To store large amounts of data, the distributed file system has been used in many research facilities and large-scale data centers. Traditional distributed file systems were configured by installing a distributed file system which is referred to as “bare-metal”, directly on server. Recently, with easy management and fast failover capabilities, these systems have been configured and delivered through a virtual environment. In this paper, we analyzed the EOS distributed file system developed and used by CERN(Conseil Européen pour la Recherche Nucléaire), which produces the largest amount of experimental data in the world And using both Bare-Metal environment and KVM(Kernel-based Virtual Machine)-based virtual environment, we analyzed the file system performance of these two environments. We compared the performances and analyzed the different environmental characteristics and presented the advantages of the I/O performance of the distributed file system in the virtual environment from our experimental results.
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