VDI Real-Time Monitoring System for KVM-Based Virtual Machine Resource Usage Analysis

김태훈,; 김현지,; 노재춘,

doi:10.5573/ieie.2015.52.1.069

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…하지만 가상머신에 할당되어 있는 서버의 자원량은 설정된 크기로 고정되어 있기 때문에 사용 자가 사용하는 패턴에 따라 가상머신의 자원이 유휴 상태가 될 수 있다[4].이러한 문제를 해결하 기 위해서는 가상머신마다 자원사용량을 모니터링할 수 있어야 한다. 또한, 설치된 가상머신마다 시스템에 문제가 있는지 개별적으로 확인할 필요가 있는데, 로그 서 버를 이용해 로그를 확인하거나, 자동화 스크립트 툴을 이용해 메일 체크 등과 같은 방법으로 점 검한다[5].…”

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Web Monitoring System for Cloud Desktop Virtualization OS

Kwang¹,

Kim²

2017

AJMAHS

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In recent years, VDI (Virtual Desktop Infrastructure) Market is rapidly growing due to the proliferation of cloud services. VDI is a technology that enables you to access your personal desktop environment anytime, anywhere through the Internet. Considerations for deploying these environments include monitoring resource usage for performance optimization and collection and analysis of system logs to detect system anomalies. Using traditional tools, however, resource usage is easy to monitor, whereas each OS log collection method for each OS is fragmented and analyzed separately from each OS. Therefore, it is difficult to monitor and manage the system efficiently. To address these problems, this paper has designed and implemented a Web Monitoring System that enables the integration of resource usage information and system logs collected from each virtualization OS on the KVM. This system can be used as an effective means of effectively detecting and responding to problems that arise from assigned virtual machines and effectively redistribute resources.

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Web Monitoring System for Cloud Desktop Virtualization OS

Kwang¹,

Kim²

2017

AJMAHS

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A Data-Driven Resource Allocation Method for Personalized Container Based Desktop as a Service

Baek¹,

Huh

2017

2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing,

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Median Filtering Detection using Latent Growth Modeling

Rhee¹

2015

Journal of the Institute of Electronics and Information Enginee

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In recent times, the median filtering (MF) detector as a forensic tool for the recovery of forgery images' processing history has concerned broad interest. For the classification of MF image, MF detector should be designed with smaller feature set and higher detection ratio. This paper presents a novel method for the detection of MF in altered images. It is transformed from BMP to several kinds of MF image by the median window size. The difference distribution values are computed according to the window sizes and then the values construct the feature set same as the MF window size. For the MF detector, the feature set transformed to the model specification which is computed using latent growth modeling (LGM). Through experiments, the test image is classified by the discriminant into two classes: the true positive (TP) and the false negative (FN). It confirms that the proposed algorithm is to be outstanding performance when the minimum distance average is 0.119 in the confusion of TP and FN for the effectivity of classification.

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VDI Real-Time Monitoring System for KVM-Based Virtual Machine Resource Usage Analysis

Cited by 4 publications

References 4 publications

Web Monitoring System for Cloud Desktop Virtualization OS

Web Monitoring System for Cloud Desktop Virtualization OS

A Data-Driven Resource Allocation Method for Personalized Container Based Desktop as a Service

Median Filtering Detection using Latent Growth Modeling

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