• Journal of Information Technology and Architecture
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• v.10 no.1
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• pp.1-11
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• 2013

Various computing systems are used in naval ships. Since each system has a single purpose and its applications are tightly coupled with the physical machine, applications cannot share physical resources with each other. It is hard to utilize resources efficiently in conventional naval shipboard computing environment. In this paper, we present an integration architecture for virtualized naval shipboard computing systems based on open architecture. Our proposed architecture integrates individual computing resources into one single integrated hardware pool so that the OS and applications are encapsulated as a VM. We consider the issue of varying needs of all applications in a naval ship that have different purposes, priorities and requirements. We also present parallel VM migration algorithm that improves the process time of resource reallocation of given architecture. The evaluation results with the prototype system show that our algorithm performs better than conventional resource reallocation algorithm in process time.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.1
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• pp.111-117
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• 2011

In this paper, proposed client virtualization technology to minimize degradation of the local computing environment, efficient and qualified users in the area of virtual functions needed to enable the user to provide important information in the local computing environment protection and performance, stability and continuity was important to keep. As well as the local computing environment from malicious code attacks such as methods for protecting virtualized domain also can not be overlooked as a major problem area in a virtualized, virtualized data through the encryption of user-space security, maximized. In addition, through virtualization using local computing resources efficiently while still a local computing system separate from the computing resources to a single user can get the same effect.

• Journal of KIISE
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• v.44 no.5
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• pp.439-448
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• 2017

The workloads of large high-performance computing (HPC) scientific applications are steadily becoming "bursty" due to variable resource demands throughout their execution life-cycles. However, the over-provisioning of virtual resources for optimal performance during execution remains a key challenge in the scheduling of scientific HPC applications. While over-provisioning of virtual resources guarantees peak performance of scientific application in virtualized environments, it results in increased amounts of idle resources that are unavailable for use by other applications. Herein, we proposed a memory resource reconfiguration approach that allows the quick release of idle memory resources for new applications in OS-level virtualized systems, based on the applications resource-usage pattern profile data. We deployed a scientific workflow application in Docker, a light-weight OS-level virtualized system. In the proposed approach, memory allocation is fine-tuned to containers at each stage of the workflows execution life-cycle. Thus, overall memory resource utilization is improved.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.1-10
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• 2014

The cloud computing paradigm introduced pay-per-use models in which IT services can be created and scaled on-demand. However, service providers are still concerned about the constraints imposed by their physical infrastructures. In order to keep the required QoS and achieve the goal of upholding the SLA, virtualized resources must be efficiently consolidated to maximize system throughput while keeping energy consumption at a minimum. Using ANN, we propose a predictive SLA-aware approach for consolidating virtualized resources in a cloud environment. To maintain the QoS and to establish an optimal trade-off between performance and energy efficiency, the server's utilization threshold dynamically adapts to the physical machine's resource consumption. Furthermore, resource-intensive VMs are prevented from getting underprovisioned by assigning them to hosts that are both capable and reputable. To verify the performance of our proposed approach, we compare it with non-optimized conventional approaches as well as with other previously proposed techniques in a heterogeneous cloud environment setup.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.13-19
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• 2019

SDN is one of the most actively researched topics to solve traffic problems in communication. SDN implements multiple networks in a single physical network by virtualizing network resources through an advanced API. Network Function Virtualized (NFV) distributes network functions from hardware using software instant, virtualization technology to VNF. These features make network management easier and improve performance by virtualizing IP, routers, and so on. In this paper, we propose a method to control the traffic and provide the distributed controller effect of SDN through SDN distribution in the virtualized industrial network. It is expected that SDN distribution will be able to manage traffic more efficiently when using the proposed scheme.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.4
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• pp.177-187
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• 2020

Virtualization with hypervisors is one of emerging topics in multicore processors for space. Hypervisors are software layers to make several independent virtualized environments on one processor. Since all hardware resources are virtualized and distributed only by hypervisors, overall performance of processors can be improved by fully utilizing the resources. However at the same time, there are overheads for virtualizing and distributing hardware resources. Satellites are one of hard real time systems, and performance degradation with overheads should be analyzed thoroughly. Previous research on the overheads focused on single core systems. Even the overheads were analyzed in multicore systems, SMP environment was not fully included. This paper builds SMP environment with XtratuM, one of hypervisors for space missions, and analyzes performance degradation with overheads. Two boards of GR712RC with 2 LEON3FT CPUs and GR740 with 4 LEON4 CPUs are used in experiments. On each board, SMP benchmark functions are executed on SMP environment with XtratuM and on that without XtratuM respectively. Results are analyzed to find timing characteristics including overheads. Finally, applicability of the XtratuM to flight software in SMP is also reviewed.

• Journal of Information Technology and Architecture
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• v.11 no.4
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• pp.449-462
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• 2014

In big data era, there are a number of considerable parts in processing systems for capturing, storing, and analyzing stored or streaming data. Unlike traditional data handling systems, a big data processing system needs to concern the characteristics (format, velocity, and volume) of being handled data in the system. In this situation, virtualized computing platform is an emerging platform for handling big data effectively, since virtualization technology enables to manage computing resources dynamically and elastically with minimum efforts. In this paper, we analyze resource utilization of virtualized computing resources to discover suitable deployment models in Apache Hadoop and HBase-based big data processing environment. Consequently, Task Tracker service shows high CPU utilization and high Disk I/O overhead during MapReduce phases. Moreover, HRegion service indicates high network resource consumption for transfer the traffic data from DataNode to Task Tracker. DataNode shows high memory resource utilization and Disk I/O overhead for reading stored data.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.235-243
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• 2016

Legacy data centers are transforming toward cloud data centers according to the advance of mobile and Internet of Things technology, processing of big data, and development of cloud computing technology. The goal of cloud data centers is to efficiently manage energy and facility, and to rapidly provide service demands to users by operating virtualized ICT(Information and Communication Technology) resources. Accordingly, it requires to configure and operate networks for efficiently providing virtualized ICT resources. This paper analyzes networking technologies suitable for cloud data centers and presents ways to efficiently operate the data center.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.242-245
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• 2011

Cloud Computing is a fast developing domain in computer system architecture which enables dynamically scalable and virtualized resources to its users. Spot Cloud is the next evolutionary step in this field which allows the cloud computing resources to be treated as a market commodity. Cloud computing vendors will now be able to put their un used computational resources for sale using the singular access platform provided by Spot Cloud. Meanwhile customers will be able to buy/sell these resources according to their requirements. This paper analyzes the idea of Spot Cloud and the anticipated impact it will have on Cloud Computing globally. The paper also presents the risks and inherent barriers associated with this idea and how they might hinder the development of Spot Cloud to its full potential.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1712-1732
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• 2011

Cloud provides dynamically scalable virtualized computing resources as a service over the Internet. To achieve higher resource utilization over virtualization technology, an optimized strategy that deploys virtual machines on physical machines is needed. That is, the total number of active physical host nodes should be dynamically changed to correspond to their resource usage rate, thereby maintaining optimum utilization of physical machines. In this paper, we propose a pattern-based prediction model for resource provisioning which facilitates best possible resource preparation by analyzing the resource utilization and deriving resource usage patterns. The focus of our work is on predicting future resource requests by optimized dynamic resource management strategy that is applied to a virtualized data center in a Cloud computing environment. To this end, we build a prediction model that is based on user request patterns and make a prediction of system behavior for the near future. As a result, this model can save time for predicting the needed resource amount and reduce the possibility of resource overuse. In addition, we studied the performance of our proposed model comparing with conventional resource provisioning models under various Cloud execution conditions. The experimental results showed that our pattern-based prediction model gives significant benefits over conventional models.