A performance comparison of linux containers and virtual machines using Docker and KVM

Title : A performance comparison of linux containers and virtual machines using Docker and KVM
Journal : Cluster Computing
Authors : MinSu Chae, HwaMin Lee, Kiyeol Lee
Corresponding author : HwaMin Lee
DOI : https://doi.org/10.1007/s10586-017-1511-2

Abstract
Virtualization is a foundational element of cloud computing. Since cloud computing is slower than a native system, this study analyzes ways to improve performance. We compared the performance of Docker and Kernel-based virtual machine (KVM). KVM uses full virtualization, including ×86 hardware virtualization extensions. Docker is a solution provided by isolation in userspace instead of creating a virtual machine. The performance of KVM and Docker was compared in three ways. These comparisons show that Docker is faster than KVM.

Acknowledgments
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (Ministry of Science and ICT) (No. NRF-2017R1A2B4010570) and the Soonchunhyang University Research Fund.

Design of middleware to support auto-scaling in Docker-based multi host environment

Title : Design of middleware to support auto-scaling in Docker-based multi host environment
Published in : The 14th KIPS International Conference on Ubiquitous Information Technologies and Applications (CUTE 2019)
Author : Minsu Chae, Sangwook Han, HwaMin Lee
Corresponding author : HwaMin Lee
Location : University of Macau, Macau, China

Abstract
With the spread of smart devices, the use of big data, and the proliferation of the Internet of Things, virtualization technology for cloud servers have become important worldwide. Also, research has been conducted to efficiently manage the resources of hosts in VMs. Container-based virtualization has less performance degradation than VMs because there is no emulation for the operating system. Using the Docker API is slow to measure. In this paper, we implement the resource measurement module of Job nodes and design middleware that supports auto-scaling in auto-scaling module and Docker-based multi-host environment.

Acknowledgments
This research supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2019-2014-1-00720 & IITP-2019-2015-0-00403) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation).

A Design of Cardiac Arrest Early Warning System in Hospital

Title : A Design of Cardiac Arrest Early Warning System in Hospital
Published in : The 3rd International Conference on Interdisciplinary Research on Computer Science, Psychology, and Education (ICICPE’ 2019)
Author : SangWook Han, MinSu Chae, Min Hong, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Vinoasis Phu Quoc Hotel, Phu Qoc, Vietnam

Abstract
Recently, increasing heart failure patients due to the increase in elderly patients, high probability for these patients, the arrhythmia. The number of cardiac arrests in hospitals is also increasing, and mortality rates are more than twice as high as those transferred from general wards to the intensive care unit through the emergency room or other routes. Existing patient monitoring methods in hospitals are measured using 24-hour Holter monitoring. This method requires carrying the device and measuring up to 48 hours. Also, the patient’s activities are very uncomfortable because of the communication line. Currently, many hospitals use the Modified Early Warning Score (MEWS) as a standard for early detection of deterioration in general ward patients. However, the prediction rate is very low and there is little help. Many people are working hard to detect heart attacks early because computerized systems vary from hospital to hospital, it is difficult to apply the developed system to various hospital environments. This study discusses the factors and methods necessary to solve these problems.

Acknowledgments
This research was supported by MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2019-2014-1-00720) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation) and the Bio & Medical Technology Development Program of the National Research Foundation (NRF)(No.NRF-2019M3E5D1A02069073).

CNN and RNN models for predicting particulate matter in South Korea

Title : CNN and RNN models for predicting particulate matter in South Korea
Published in : The 11th International Conference on Internet (ICONI 2019)
Author : Guang Yang, Thanongsak Xayasouk, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Lotte Hotels & Resorts Hanoi, Hanoi, Vietnam

Abstract
Particulate matter proved to have severe effects on human health. To implement a real-time predicting system for is indispensable for every country face air pollution problem. Based on the previous study, predicting models for local areas are implemented. In this paper, a convolutional network, a recurrent neural network and a hybrid model combines the convolutional layers with the recurrent layers apply to predict future one weeks PM10 and PM2.5 concentration in seven urban cities in South Korea. The main contribution in this paper is: proposed three predicting models, make a comparison of three different models for predicting the concentration of particulate matter, all models are proved capable to obtain reliable predicting results. Experiments show GRU archive best performance for most cities both for PM10 and PM2.5, CNN models speed up the training process with least time consumption. The hybrid model performance stable with less time for training.

Acknowledgments
This research supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2019-2015-0-00403) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation) and Basic Science Research Program through the National Research Foundation of Korea (NRF-2017R1A2B4010570).