• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2649-2666
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• 2015

In the dynamic cloud computing environment, to ensure, under the terms of service-level agreements, the maximum efficiency of resource utilization, it is necessary to investigate the online dynamic management of virtual machine resources and their operational application systems/components. In this study, the feasibility and properties of the division of virtual machine resources on the cloud platform, using the virtual machine cluster as the management unit, are investigated. First, the definitions of virtual machine clusters are compared, and our own definitions are presented. Then, the feasibility of division using the virtual machine cluster as the management unit is described, and the isomorphism and reconfigurability of the clusters are proven. Lastly, from the perspectives of clustering and cluster segmentation, the dynamics of virtual machines are described and experimentally compared. This study aims to provide novel methods and approaches to the optimization management of virtual machine resources and the optimization configuration of the parameters of virtual machine resources and their application systems/components in large-scale cloud computing environments.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.1-7
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• 2014

In this paper, we propose a virtual machine(VM) remapping scheme that reduces VM relocation time on a cloud cluster. The proposed scheme finds VMs that should be migrated in sequence from a given VM map, and exchanges destinations of some VMs among them to reduce the VM relocation time. The VMs, the destinations of which will be exchanged, are chosen based on the amount of physical machine's available resources and migration completion time. The exchange of destinations is repeated until the VM relocation time cannot be shortened any further. Through a simulation, we show that the proposed scheme reduces VM relocation time by 42.7% in maximum.

• Journal of Korea Multimedia Society
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• v.15 no.12
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• pp.1499-1505
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• 2012

Recently, the distributed processing system for big data has been actively investigated owing to the development of high speed network and storage technologies. In addition, virtual system that can provide efficient use of system resources through the consolidation of servers has been increasingly recognized. But, when we configure distributed processing system for big data in virtual machine environments, many problems occur. In this paper, we did an experiment on the optimization of I/O bandwidth according to the creation and placement of VMs and tasks with composing Hadoop cluster in virtual environments and evaluated the results of an experiment. These results conducted by this paper will be used in the study on the development of Hadoop Scheduler supporting I/O bandwidth balancing in virtual environments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1146-1154
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• 2010

A virtual machine lease service in the cloud computing environment has been implemented using the open source cloud computing platform, OpenNebula. In addition, a web-based cloud user interface is developed for both convenient resource management and efficient service access. The present virtual machine allocation scheme adopted in OpenNebula has performance reduction problem because of not considering CPU allocation scheduler of the virtualization software. In order to address this problem we have considered both the priority of the idle CPU resources of the cluster and credit scheduler of Xen, which resulted in performance improvement of the OpenNebula virtual machine scheduler. The experimental results showed that the proposed allocation scheme provided more virtual machine creations and more CPU resource allocations for cloud service.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.270-278
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• 2023

We propose a container orchestration system for process workloads that combines the potential of big data and machine learning technologies to integrate enterprise process-centric workloads. This proposed system analyzes big data generated from industrial automation to identify hidden patterns and build a machine learning prediction model. For each machine learning case, training data is loaded into a data store and preprocessed for model training. In the next step, you can use the training data to select and apply an appropriate model. Then evaluate the model using the following test data: This step is called model construction and can be performed in a deployment framework. Additionally, a visual hierarchy is constructed to display prediction results and facilitate big data analysis. In order to implement parallel computing of PCA in the proposed system, several virtual systems were implemented to build the cluster required for the big data cluster. The implementation for evaluation and analysis built the necessary clusters by creating multiple virtual machines in a big data cluster to implement parallel computation of PCA. The proposed system is modeled as layers of individual components that can be connected together. The advantage of a system is that components can be added, replaced, or reused without affecting the rest of the system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1164-1183
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• 2019

The rapid development of cloud computing and high requirements of operators requires strong support from the underlying Data Center Networks. Therefore, the effectiveness of using resources in the data center networks becomes a point of concern for operators and material for research. In this paper, we discuss the online virtual-cluster provision problem for multiple tenants with an aim to decide when and where the virtual cluster should be placed in a data center network. Our objective is maximizing the total revenue for the data center networks under the constraints. In order to solve this problem, this paper divides it into two parts: online multi-tenancy scheduling and virtual cluster placement. The first part aims to determine the scheduling orders for the multiple tenants, and the second part aims to determine the locations of virtual machines. We first approach the problem by using the variational inequality model and discuss the existence of the optimal solution. After that, we prove that provisioning virtual clusters for a multi-tenant data center network that maximizes revenue is NP-hard. Due to the complexity of this problem, an efficient heuristic algorithm OMS (Online Multi-tenancy Scheduling) is proposed to solve the online multi-tenancy scheduling problem. We further explore the virtual cluster placement problem based on the OMS and propose a novel algorithm during the virtual machine placement. We evaluate our algorithms through a series of simulations, and the simulations results demonstrate that OMS can significantly increase the efficiency and total revenue for the data centers.

• Journal of Digital Contents Society
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• v.10 no.2
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• pp.249-258
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• 2009

As wireless internet markets become larger than before, many mobile applications are also being developed actively. In this circumstances mobile devices such as cell phones, PDAs are playing an important role to satisfy the user's need of ubiquitous computing. Due to the hardware limitations, however, the mobile devices like PDA can not run large-scale softwares by itself. The main goal of this paper is to make large-scale applications runnable on PDA. To accomplish this, we used the PDA-JPVM cluster computing engine which has been already developed by us. We found out by running the applications and the performance evaluation that large-scale Java softwares can easily run on the hardware-limited PDA. And the performance evaluation results are also presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.6
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• pp.1398-1417
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• 2013

A method is proposed to reduce excess resources from a virtual machine(VM) while avoiding subsequent migrations for a computer cluster that provides cloud service. The proposed scheme cuts down on the resources of a VM based on the probability that migration may occur after a reduction. First, it finds a VM that can be scaled down by analyzing the history of the resource usage. Then, the migration probability is calculated as a function of the VM resource usage trend and the trend error. Finally, the amount of resources needed to eliminate from an underutilized VM is determined such that the migration probability after the resource reduction is less than or equal to an acceptable migration probability. The acceptable migration probability, to be set by the cloud service provider, is a criterion to assign a weight to the resource reduction either to prevent VM migrations or to enhance VM utilization. The results of simulation show that the proposed scheme lowers migration frequency by 31.6~60.8% depending on the consistency of resource demand while losing VM utilization by 9.1~21.5% compared to other known approaches, such as the static and the prediction-based methods. It is also verified that the proposed scheme extends the elapsed time before the first occurrence of migration after resource reduction 1.1~2.3-fold. In addition, changes in migration frequency and VM utilization are analyzed with varying acceptable migration probabilities and the consistency of resource demand patterns. It is expected that the analysis results can help service providers choose a right value of the acceptable migration probability under various environments having different migration costs and operational costs.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.13-18
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• 2015

Computational science simulations have been used to enable discovery in a broad spectrum of application areas, these simulations show irregular demanding characteristics of computing resources from time to time. The adoption of virtualized high performance cloud, rather than CPU-centric computing platform (such as supercomputers), is gaining interest of interests mainly due to its ease-of-use, multi-tenancy and flexibility. Basically, provisioning a virtual cluster, which consists of a lot of virtual machines, in a real-time has a critical impact on the successful deployment of the virtualized HPC clouds for computational science simulations. However, the cost of concurrently creating many virtual machines in constructing a virtual cluster can be as much as two orders of magnitude worse than expected. One of the main factors in this bottleneck is the time spent to create the virtual images for the virtual machines. In this paper, we propose a novel technique to minimize the creation time of virtual machine images and improve I/O performance of the provisioned virtual clusters. We also confirm that our proposed technique outperforms the conventional ones using various sets of experiments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.302-308
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• 2013

Internet is becoming more common with increasing cloud services have been spreading rapidly. A SLA is agreements between service providers and customers of service providers is how to ensure quality of service. SLA in cloud computing environments from the perspective of the IT service providers to customer satisfaction increase for service quality, it will need to differentiate between competing carriers QoS guarantees with SLA is a very important factor. However, the study of SLA in the cloud is staying in its infancy. In this paper, SLA indices for cloud services defining and use them SLA Management System for QoS guarantee has been implemented. The proposed system using monitoring-based migration policy an open source-based cloud computing platform which Cluster Nodes load distribution of virtual machines assigned to their availability, response time, throughput analysis of what affects and is compared with existing cloud.