• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.6
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• pp.21-30
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• 2017

Physical computing is performed through interaction with the real world making it suitable for cultivating student abilities in computing knowledge and thought processes. Furthermore, if users can develop programs under block-coding environment, it will be more easy and more intuitive. However, the existing block coding environment has a problem that the physical devices must be continuously connected to the computer. Blockly is an open source library that adds a visual code editor linked with graphic blocks to demonstrate coding concepts through web and mobile apps. Using Blockly, we describe a development environment for physical computing on mobile platform, which combines physical computing with an established block-coding environment, and activates it through wireless communication.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.15-31
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• 2009

The next generation IT environment is expected to be an open computing environment based on Grid computing technologies, which allow users to access to any type of computing resources through networks. The open computing environment has benefits in aspects of resource utilization, collaboration, flexibility and cost reduction. Due to the variation in performance of open computing resources, however, resource allocation simply based on users' budget and time constraints often fails to meet the Service Level Agreement(SLA). This paper proposes the Mean-Absolute Deviation(MAD) portfolio optimization approach, in which service brokers consider the uncertainty of performance of resources, and compose resource portfolios that minimize the uncertainty. In order to investigate the effect of this approach, we simulate an open computing environment with varying uncertainty levels, users' constraints, and brokers' optimization strategies. The simulation result concludes threefolds. First, the MAD portfolio optimization improves the success ratio of delivering the required performance to users. Second, the success ratio depends on the accuracy in predicting the variability of performance. Thirdly, the measured variability can also help service brokers expand their service to cost-critical users by discounting the access cost of open computing resources.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.4
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• pp.141-152
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• 2013

The applications and concerned technologies of cloud computing services, one of major trends in the information communication technology, are widely progressing and advancing. OpenStack, one of open source cloud computing platforms, is comprised of several service components; using these, it can be possible to build public or private cloud computing service for a given target application. In this study, a remote sensing image analysis processing service on cloud computing environment has designed and implemented as an operational test application in the private cloud computing environment based on OpenStack. The implemented service is divided into instance server, web service, and mobile app. A instance server provides remote sensing image processing and database functions, and the web service works for storage and management of remote sensing image from user sides. The mobile app provides functions for remote sensing images visualization and some requests.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.8
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• pp.245-252
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• 2015

Recently, parallel processing methods with accelerator have been introduced into a high performance computing and a mobile computing. The photomosaic application can be parallelized by using inherent data parallelism and accelerator. In this paper, we propose a way to distribute the workload of the photomosaic application into a CPU and GPU heterogeneous computing environment. That is, the photomosaic application is parallelized using both CPU and GPU resource with the asynchronous mode of OpenCL, and then the optimal workload distribution rate is estimated by measuring the execution time with CPU-only and GPU-only distribution rates. The proposed approach is simple but very effective, and can be applied to parallelize other applications on a CPU and GPU heterogeneous computing environment. Based on the experimental results, we confirm that the performance is improved by 141% into a heterogeneous computing environment with the optimal workload distribution compared with using GPU-only method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.270-276
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• 2018

Apache Spark is one of the high performance in-memory computing frameworks for big-data processing. Recently, to improve the performance, general-purpose computing on graphics processing unit(GPGPU) is adapted to Apache Spark framework. Previous Spark-GPGPU frameworks focus on overcoming the difficulty of an implementation resulting from the difference between the computation environment of GPGPU and Spark framework. In this paper, we propose a Spark framework based on a heterogenous pipeline computing with OpenCL to further improve the performance. The proposed framework overlaps the Java-to-Native memory copies of CPU with CPU-GPU communications(DMA) and GPU kernel computations to hide the CPU idle time. Also, CPU-GPU communication buffers are implemented with switching dual buffers, which reduce the mapped memory region resulting in decreasing memory mapping overhead. Experimental results showed that the proposed Spark framework based on a heterogenous pipeline computing with OpenCL had up to 2.13 times faster than the previous Spark framework using OpenCL.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.3
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• pp.190-195
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• 2001

The advance of computer, network, and Internet technology enables the control systems to process the massive data in the distributed computing environments. To implement and maintain the software in distributed environment, the component-based methodology is widely used. This paper proposes the middleware architecture for the distributed computer control system. With the proposed middleware services, it is relatively easy to maintain compatibility between products and to implement a portable control application. To achieve the compatibility between heterogeneous systems, the proposed architecture provides the communication protocols based on the XML with lightweight event-based service.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.235-238
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• 2016

With the vision of Internet of Things (IoT), physical world itself is becoming a connected information system on the Internet and cyber world is computing as a physical act to sense and respond to real-world events collaboratively. The systems that tightly interlink the cyber and physical worlds are often referred to as Smart Systems or Cyber-Physical Systems. Smart IoT Clouds aim to provide a cyber-physical infrastructure for utility (pay-as-you-go) computing to easily and rapidly build, modify and provision auto-scale smart systems that continuously monitor and collect data about real-world events and automatically control their environment. Developing specifications for service interoperability is critical to enable to achieve this vision. In this paper, we bring an issue to extend Open Cloud Computing Interface for uniform, interoperable interfaces for Smart IoT Cloud Services to access services and build a smart system through orchestrating the cloud services.

• 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.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.6
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• pp.325-334
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• 2016

Recently, Open Computing Language (OpenCL) has been proposed to provide a framework that supports heterogeneous computing platforms. By using an OpenCL framework, digital communication systems can support various protocols in a unified computing environment to achieve both high portability and high performance. This article introduces a parallel software decoder of Low Density Parity Check (LDPC) codes for China Multimedia Mobile Broadcasting (CMMB) on a heterogeneous platform. Each step of LDPC decoding has different parallelization characteristics. In this paper, steps suitable for task-level parallelization are executed on the CPU, and steps suitable for data-level parallelization are processed by the GPU. To improve the performance of the proposed OpenCL kernels for LDPC decoding operations, explicit thread scheduling, loop-unrolling, and effective data transfer techniques are applied. The proposed LDPC decoder achieves high performance by using heterogeneous multi-core processors on a unified computing framework.

• Journal of Digital Contents Society
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• v.14 no.4
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• pp.505-511
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• 2013

Recently, cloud computing is being emerged paradigm that a support computing resource flexible and scalable to users as the want in distributed computing environment. Actually, cloud computing can be implemented and provided by virtualization technology. In this paper, we studied open source based cloud computing and built a educational practice system through cloud computing. Virtualization-based cloud computing provides optimized computing resources, as well as easy to manage practical resource and result. Therefore, we can save the time for configuration of practice environment. In the view of faculty, they can easily handle the practice result. Also, those practice condition reuse comfortably and apply to various configuration simply. And then we can increase capabilities and availabilities of limited resources.