• Proceedings of the Korea Information Processing Society Conference
• /
• 2013.11a
• /
• pp.131-134
• /
• 2013

최근 클라우드 컴퓨팅의 발전과 상업적인 성공과 함께 클라우드 자원의 이용률을 최대로 유지하면서 에너지를 효율적으로 사용하기 위한 연구에 대한 관심이 커지고 있다. 자원의 사용률이 최대로 높아지게 되면 에너지 소비량이 급격하게 증가하여 많은 에너지를 사용하게 되므로 자원의 사용율과 에너지 사용은 트레이드오프 관계를 가지게 된다. 따라서 본 논문에서는 자원의 최대 사용 및 효율적인 에너지 사용을 위해 에너지 소비가 최적이 되는 자원 이용률의 임계값을 찾기 위한 연구를 수행하였다. 실험을 위해 자원 중 가장 많은 에너지를 소비하는 CPU를 이용하였고, 전력 측정을 위해 KEM2500 전력계와 ThrottleStop_500 프로그램을 사용하였다. 실험 결과 CPU 사용률이 약 90%일 때 에너지 사용량이 급격하게 증가하였으며, 기존의 평균 자원 이용률과 비교했을 때 12.3% 정도의 전기량이 더 소모됨을 확인하였다. 따라서 클라우드 컴퓨팅에서 CPU 자원의 이용률이 90%일 때 에너지가 최적이라고 할 수 있다.

• 한국정보통신설비학회:학술대회논문집
• /
• 2009.08a
• /
• pp.131-135
• /
• 2009

This paper is the introduction of our work on distributed load scheduling in single-level tree network. In this paper, we derive a new calculation model in single-level tree network and show a closed-form formulation of the time for computation system. There are so many examples of the application of this technology such as distributed database, biology computation on genus, grid computing, numerical computing, video and audio signal processing, etc.

• Journal of Digital Contents Society
• /
• v.17 no.1
• /
• pp.51-57
• /
• 2016

Distributed Computing attempts to harness a massive computing power using a great numbers of idle PCs resource distributed linked to the internet and processes a variety of applications parallel way such as bio, climate, cryptology, and astronomy. In this paper, we develop internet-distributed computing environment, so that we can analyze High Resolution Rainfall Prediction application in meteorological field. For analyze the rainfall forecast in Korea peninsula, we used QPM(Quantitative Precipitation Model) that is a mesoscale forecasting model. It needs to a lot of time to construct model which consisted of 27KM grid spacing, also the efficiency is degraded. On the other hand, based on this model it is easy to understand the distribution of rainfall calculated in accordance with the detailed topography of the area represented by a small terrain model reflecting the effects 3km radius of detail and terrain can improve the computational efficiency. The model is broken down into detailed area greater the required parallelism and increases the number of compute nodes that efficiency is increased linearly.. This model is distributed divided in two sub-grid distributed units of work to be done in the domain of $20{\times}20$ is networked computing resources.

• The Journal of the Korea Contents Association
• /
• v.12 no.2
• /
• pp.30-39
• /
• 2012

In these day, some of IT venders already were going to cloud computing market, as well they are going to expand their territory for the cloud computing market through that based on their hardware and software technology, making collaboration between hardware and software vender. Distributed file system is very mainly technology for the cloud computing that must be protect performance and safety for high levels service requests as well data store. This paper introduced distributed file system for cloud computing and how to use this theory such as memory database, Hadoop file system, high availability database system. now In the market, this paper define a very large distributed processing architect as a reference by kind of distributed file systems through using technology in cloud computing market.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.3
• /
• pp.263-274
• /
• 2010

Mutual exclusion that applied on existing systems was designed for static distributed systems. but now computing environments are going to mobile computing environments. Therefore a mutual exclusion algorithm in static distributed environments should be designed for new computing environments. So this paper proposes a mobile mutual exclusion algorithm to support the mutual exclusion of shared resources in mobile computer environments. Mobile computing resources as wireless hosts cause new issues because of their mobility and weakness and made mutual exclusion problem more complex than stationary distributed environments. So we proposed a new mobile token mutual exclusion algorithm with deadlock-free and starvation-free in mobile computing environments based on spanning tree topology and extend for mobile computing environments. The proposed algorithm minimizes message complexity in case of free hopping in cellular networks.

• Journal of Information Processing Systems
• /
• v.5 no.4
• /
• pp.175-186
• /
• 2009

Power consumed by modern computer systems, particularly servers in data centers has almost reached an unacceptable level. However, their energy consumption is often not justifiable when their utilization is considered; that is, they tend to consume more energy than needed for their computing related jobs. Task scheduling in distributed computing systems (DCSs) can play a crucial role in increasing utilization; this will lead to the reduction in energy consumption. In this paper, we address the problem of scheduling precedence-constrained parallel applications in DCSs, and present two energy- conscious scheduling algorithms. Our scheduling algorithms adopt dynamic voltage and frequency scaling (DVFS) to minimize energy consumption. DVFS, as an efficient power management technology, has been increasingly integrated into many recent commodity processors. DVFS enables these processors to operate with different voltage supply levels at the expense of sacrificing clock frequencies. In the context of scheduling, this multiple voltage facility implies that there is a trade-off between the quality of schedules and energy consumption. Our algorithms effectively balance these two performance goals using a novel objective function and its variant, which take into account both goals; this claim is verified by the results obtained from our extensive comparative evaluation study.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2001.10a
• /
• pp.503-509
• /
• 2001

For efficiently providing distributed services, distributed computing environments are specified the requirements of various services and distributed object platforms applied an object-oriented technology by TINA Consortium and OMG CORBA. Because applications are becoming large and distributing, their servicing and managing interfaces among objects are being complicated. In order to solve these defects, it is necessary to suggest a new object grouping model and specify object service/management requirements can be introduced under the object groups.

• International Journal of Contents
• /
• v.14 no.1
• /
• pp.34-38
• /
• 2018

With the rapid growth of genomic data, new requirements have emerged that are difficult to handle with big data storage and analysis techniques. Regardless of the size of an organization performing genomic data analysis, it is becoming increasingly difficult for an institution to build a computing environment for storing and analyzing genomic data. Recently, cloud computing has emerged as a computing environment that meets these new requirements. In this paper, we analyze and compare existing distributed and parallel NGS (Next Generation Sequencing) analysis based on cloud computing environment for future research.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.10
• /
• pp.4948-4967
• /
• 2017

On mobile devices, image sequences are widely used for multimedia applications such as computer vision, video enhancement, and augmented reality. However, the real-time processing of mobile devices is still a challenge because of constraints and demands for higher resolution images. Recently, heterogeneous computing methods that utilize both a central processing unit (CPU) and a graphics processing unit (GPU) have been researched to accelerate the image sequence processing. This paper deals with various optimizing techniques such as parallel processing by the CPU and GPU, distributed processing on the CPU, frame buffer object, and double buffering for parallel and/or distributed tasks. Using the optimizing techniques both individually and combined, several heterogeneous computing structures were implemented and their effectiveness were analyzed. The experimental results show that the heterogeneous computing facilitates executions up to 3.5 times faster than CPU-only processing.

• Journal of Korean Society for Quality Management
• /
• v.48 no.4
• /
• pp.553-566
• /
• 2020

Purpose: The purpose of this study was to design a framework for generating one-class classification algorithm based on Hyper-Rectangle(H-RTGL) in a distributed environment connected by network. Methods: At first, we devised one-class classifier based on H-RTGL which can be performed by distributed computing nodes considering model and data parallelism. Then, we also designed facilitating components for execution of distributed processing. In the end, we validate both effectiveness and efficiency of the classifier obtained from the proposed framework by a numerical experiment using data set obtained from UCI machine learning repository. Results: We designed distributed processing framework capable of one-class classification based on H-RTGL in distributed environment consisting of physically separated computing nodes. It includes components for implementation of model and data parallelism, which enables distributed generation of classifier. From a numerical experiment, we could observe that there was no significant change of classification performance assessed by statistical test and elapsed time was reduced due to application of distributed processing in dataset with considerable size. Conclusion: Based on such result, we can conclude that application of distributed processing for generating classifier can preserve classification performance and it can improve the efficiency of classification algorithms. In addition, we suggested an idea for future research directions of this paper as well as limitation of our work.