• Annual Conference of KIPS
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• 2012.11a
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• pp.239-240
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• 2012

When users access to use the computing resources in the cloud, they expect specific quality of service (QoS) which should be guaranteed by the service provider. Meanwhile, the service provider should adopt proper schemes to enhance the resource utilization. In this thesis, we propose the MapChem-Broker which aims to satisfy users' QoS requirements as well as enhance the resource utilization by controlling the provision of VM resources in the cloud. On the experimental cloud testbed, we compare the proposed scheme with an existing one for VM resource provisioning. Results show that the proposed scheme outperforms the existing one.

• The KIPS Transactions:PartA
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• v.14A no.4
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• pp.197-202
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• 2007

This study focused on design and implementation of Mobile 3D Bluetooth Game Engine based on OpenGL-ES. In Mobile 3D network game so far, there is a form the mainstream of wireless inter-net game using WAP and VM. But, VM game we popular because of an excessive communication expense problem for this mobile network game that occur when connect to wireless internet as point out to problem by it, that is, stand-alone game are very popular. This study introduce a mobile 3D Bluetooth Game Engine which is based on mobile 3D standard using OpenGL-ES to solve a problem like mobile network game generally that occur when connect to take pleasure a wireless internet from some people into a short distance. When the number of concurrent packet datum by Bluetooth terminal transfers to each other, we shows that the proposed scheduling scheme for enhancing the process speed up on Bluetooth.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.464-464
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• 2010

A few years ago, for maintaining high stability and production yield of production equipment in a semiconductor fab, on-line monitoring of wafers is required, so that semiconductor manufacturers are investigating a software based process controlling scheme known as virtual metrology (VM). As semiconductor technology develops, the cost of fabrication tool/facility has reached its budget limit, and reducing metrology cost can obviously help to keep semiconductor manufacturing cost. By virtue of prediction, VM enables wafer-level control (or even down to site level), reduces within-lot variability, and increases process capability, $C_{pk}$. In this research, we have practiced VM on $SiO_2$ etch rate with optical emission spectroscopy(OES) data acquired in-situ while the process parameters are simultaneously correlated. To build process model of $SiO_2$ via, we first performed a series of etch runs according to the statistically designed experiment, called design of experiments (DOE). OES data are automatically logged with etch rate, and some OES spectra that correlated with $SiO_2$ etch rate is selected. Once the feature of OES data is selected, the preprocessed OES spectra is then used for in-situ sensor based VM modeling. ICP-RIE using 葰.56MHz, manufactured by Plasmart, Ltd. is employed in this experiment, and single fiber-optic attached for in-situ OES data acquisition. Before applying statistical feature selection, empirical feature selection of OES data is initially performed in order not to fall in a statistical misleading, which causes from random noise or large variation of insignificantly correlated responses with process itself. The accuracy of the proposed VM is still need to be developed in order to successfully replace the existing metrology, but it is no doubt that VM can support engineering decision of "go or not go" in the consecutive processing step.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1554-1566
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• 2014

Cloud Computing allows application providers seamlessly scaling their services and enables users scaling their usage according to their needs. In this paper, using queuing game model, we present service scheduling schemes which are used in software as a service (SaaS). The object is maximizing the Cloud Computing platform's (CCP's) payoff via controlling the service requests whether to join or balk, and controlling the value of CCP's admission fee. Firstly, we treat the CCP as one virtual machine (VM) and analyze the optimal queue length with a fixed admission fee distribution. If the position number of a new service request is bigger than the optimal queue length, it balks. Otherwise, it joins in. Under this scheme, the CCP's payoff can be maximized. Secondly, we extend this achievement to the multiple VMs situation. A big difference between single VM and multiple VMs is that the latter one needs to decide which VM the service requests turn to for service. We use a corresponding algorithm solve it. Simulation results demonstrate the good performance of our schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1265-1278
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• 2017

Dynamic task scheduling is one of the most popular research topics in the cloud computing field. The cloud scheduler dynamically provides VM resources to variable cloud tasks with different scheduling strategies in cloud computing. In this study, we utilized a valid model to describe the dynamic changes of both computing facilities (such as hardware updating) and request task queuing. We built a novel approach called Policy Iteration Scheduling (PIS) to globally optimize the independent task scheduling scheme and minimize the total execution time of priority tasks. We performed experiments with randomly generated cloud task sets and varied the performance of VM resources using Poisson distributions. The results show that PIS outperforms other popular schedulers in a typical cloud computing environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4759-4775
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• 2015

The reduction of power consumption in large-scale datacenters is highly-dependent on the use of virtualization to consolidate multiple workloads. However, these consolidation strategies must also take into account additional important parameters such as performance, reliability, and profitability. Resolving these conflicting goals is often the major challenge encountered in the design of optimization strategies for cloud data centers. In this paper, we put forward a data center monitoring strategy which dynamically alters its approach depending on the cloud system's current state. Results show that our proposed scheme outperformed strategies which only focus on a single metric such as SLA-Awareness and Energy Efficiency.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.7
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• pp.219-230
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• 2014

In the cloud computing environment, cloud service users purchase and use the virtualized resources from cloud resource providers on a pay as you go manner. Typically, there are two billing plans for computing resource allocation adopted by large cloud resource providers such as Amazon, Gogrid, and Microsoft, on-demand and reserved plans. Reserved Virtual Machine(VM) instance is provided to users based on the lengthy allocation with the cheaper price than the one of on-demand VM instance which is based on shortly allocation. With the proper mixture allocation of reserved and on-demand VM corresponding to users' requests, cloud service providers are able to reduce the resource allocation cost. To do this, prior researches about VM allocation scheme have been focused on the optimization approach with the users' request prediction techniques. However, it is difficult to predict the expected demands exactly because there are various cloud service users and the their request patterns are heavily fluctuated in reality. Moreover, the previous optimization processing techniques might require unacceptable huge time so it is hard to apply them to the current cloud computing system. In this paper, we propose the cloud brokering system with the adaptive VM allocation schemes called A3R(Adaptive 3 Resource allocation schemes) that do not need any optimization processes and kinds of prediction techniques. By using A3R, the VM instances are allocated to users in response to their service demands adaptively. We demonstrate that our proposed schemes are able to reduce the resource use cost significantly while maintaining the acceptable Quality of Service(QoS) of cloud service users through the evaluation results.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1633-1636
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• 2003

In this paper, we developed the scheme to grow to use newly added disk space without having to kill the application, unmount file system. This scheme, called online resizing, can resize the file system layout with the advent of Logical Volume Manager. The online resizing scheme is designed and implemented in linux cluster system where multiple hosts share the disk data in storage area network environment. It is incorporated with SANfs shared file system and can perform resizing technique with SANfs-VM volume manager. The experimental result shows that it can maximize the availability and capacity of the SANfs system which are important for modem servers where must not lose their customer.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.775-783
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• 2017

As interest in cloud computing grows, the number of users using cloud computing services is increasing. However, cloud computing technology has been steadily challenged by security concerns. Therefore, various security breaches are springing up to enhance the system security for cloud services users. In particular, research on detection of malicious VM (Virtual Machine) is actively underway through the introspecting virtual machines on the cloud platform. However, memory analysis technology is not used as a monitoring tool in the environments where multiple virtual machines are run on a single server platform due to obstructive monitoring overhead. As a remedy to the challenging issue, we proposes a computationally efficient instance memory introspection scheme to minimize the overhead that occurs in memory dump and monitor it through a partial memory monitoring based on the well-defined kernel memory map library.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.3
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• pp.137-148
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• 2012

Cloud service users request dedicated virtual computing resource from the cloud service provider to process jobs in independent environment from other users. To optimize this process with automated method, in this paper we proposed a framework for workflow scheduling in the cloud environment, in which the core component is the middleware called broker mediating the interaction between users and cloud service providers. To process jobs in on-demand and virtualized resources from cloud service providers, many papers propose scheduling algorithms that allocate jobs to virtual machines which are dedicated to one machine one job. With this method, the isolation of being processed jobs is guaranteed, but we can't use each resource to its fullest computing capacity with high efficiency in resource utilization. This paper therefore proposed a cost-efficient job scheduling algorithm which maximizes the utilization of managed resources with increasing the degree of multiprogramming to reduce the number of needed virtual machines; consequently we can save the cost for processing requests. We also consider the performance degradation in proposed scheme with thrashing and context switching. By evaluating the experimental results, we have shown that the proposed scheme has better cost-performance feature compared to an existing scheme.