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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.
Power Consumption Forecasting Scheme for Educational Institutions Based on Analysis of Similar Time Series Data
Jihoon Moon, Jinwoong Park, Sanghoon Han, Eenjun Hwang
http://doi.org/10.5626/JOK.2017.44.9.954
A stable power supply is very important for the maintenance and operation of the power infrastructure. Accurate power consumption prediction is therefore needed. In particular, a university campus is an institution with one of the highest power consumptions and tends to have a wide variation of electrical load depending on time and environment. For this reason, a model that can accurately predict power consumption is required for the effective operation of the power system. The disadvantage of the existing time series prediction technique is that the prediction performance is greatly degraded because the width of the prediction interval increases as the difference between the learning time and the prediction time increases. In this paper, we first classify power data with similar time series patterns considering the date, day of the week, holiday, and semester. Next, each ARIMA model is constructed based on the classified data set and a daily power consumption forecasting method of the university campus is proposed through the time series cross-validation of the predicted time. In order to evaluate the accuracy of the prediction, we confirmed the validity of the proposed method by applying performance indicators.
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