TY  - JOUR
T1  - Dovetail Usage Prediction Model for Resource-Efficient Virtual Machine Placement in Cloud Computing Environment
AU  - Kang, Hyeongbin 
AU  - Yu, Hyeon-Jin 
AU  - Kim, Jungbin 
AU  - Jeong, Heeseok 
AU  - Shin, Jae-Hyuck 
AU  - Noh, Seo-Young 
JO  - Journal of KIISE, JOK
PY  - 2023
DA  - 2023/1/14
DO  - 10.5626/JOK.2023.50.12.1041
KW  - cloud computing
KW  - virtual machine placement
KW  - usage prediction
KW  - load balancing
AB  - 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.