• Proceedings of the Korea Society for Simulation Conference
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• 2005.11a
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• pp.45-50
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• 2005

Research and development of grid computing ware mainly focused on high performance computing field such as large-scale computing operation. Many companies and organizations concentrated on existing computational grid. However, service grid focusing on enterprise environments has been noticed gradually. Grid service providers of service grid construct diverse and specialized services and provide service resources that have economic feasibility to grid users. But, service resources are geographically dispersed and divided into many classes and have individual owners and management policies. In order to utilize and allocate resources effectively, service grid needs a resource management model that handles and manages heterogeneous resources of service grid. Therefore, this paper presents the third party grid service marketplace model using virtualization to solve problems of grid service resource management. Also, this paper proposes resource dealing mechanism and pricing algorithms applicable for service grid. Empirical results show usefulness and efficiency of the third party grid service marketplace model in comparison with typical economic models for grid resource management such as single auction model and double auction model.

• Journal of the Korea Society for Simulation
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• v.18 no.2
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• pp.29-38
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• 2009

In the computing environment with heterogeneous resources, a job scheduling model is necessary for effective resource utilization and high-speed data processing. And, the job scheduling model has to cope with a dynamic change in the condition of resources. There have been lots of researches on resource estimation methods and heuristic algorithms about how to distribute and allocate jobs to heterogeneous resources. But, existing researches have a weakness for system compatibility and scalability because they do not support the standard language. Also, they are impossible to process jobs effectively and deal with a variety of computing situations in which the condition of resources is dynamically changed in real-time. In order to solve the problems of existing researches, this paper proposes a semantic computing-based dynamic job scheduling model that defines various knowledge-based rules for job scheduling methods adaptable to changes in resource condition and allocate a job to the best suited resource through inference. This paper also constructs a resource ontology to manage information about heterogeneous resources without difficulty as using the OWL, the standard ontology language established by W3C. Experimental results shows that the proposed scheduling model outperforms existing scheduling models, in terms of throughput, job loss, and turn around time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.554-557
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• 2016

Recently, supercomputer has been increasingly adopted as a computing environment for scientific simulation as well as education, healthcare and national defence. Especially, supercomputing system with heterogeneous computing resources is gaining resurgence of interest as a next-generation problem solving environment, allowing theoretical and/or experimental research in various fields to be free of time and spatial limits. However, traditional supercomputing services have only been handled through a simple form of command-line based console, which leads to the critical limit of accessibility and usability of heterogeneous computing resources. To address this problem, in this paper, we provide the design and implementation of web-based HPC (High Performance Computing) job management framework for computational scientific simulation. The proposed framework has highly extensible design principles, providing the abstraction interfaces of job scheduler (as well as bundle scheduler plug-ins for LoadLeveler, Sun Grid Engine, OpenPBS scheduler) in order to easily incorporate the broad spectrum of heterogeneous computing resources such as cluster, computing cloud and grid. We also present the detailed specification of HTTP standard based RESTful endpoints, which manage simulation job's life-cycles such as job creation, submission, control and status monitoring, etc., enabling various 3rd-party applications to be newly created on top of the proposed framework.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2815-2839
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• 2020

In a cloud computing paradigm, allocation of various virtualized ICT resources is a complex problem due to the presence of heterogeneous application (MapReduce, content delivery and networks web applications) workloads having contentious allocation requirements in terms of ICT resource capacities (resource utilization, execution time, response time, etc.). This task of resource allocation becomes more challenging due to finite available resources and increasing consumer demands. Therefore, many unique models and techniques have been proposed to allocate resources efficiently. However, there is no published research available in this domain that clearly address this research problem and provides research taxonomy for classification of resource allocation techniques including strategic, target resources, optimization, scheduling and power. Hence, the main aim of this paper is to identify open challenges faced by the cloud service provider related to allocation of resource such as servers, storage and networks in cloud computing. More than 70 articles, between year 2007 and 2020, related to resource allocation in cloud computing have been shortlisted through a structured mechanism and are reviewed under clearly defined objectives. Lastly, the evolution of research in resource allocation techniques has also been discussed along with salient future directions in this area.

• Journal of Korean Society for Quality Management
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• v.38 no.1
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• pp.64-74
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• 2010

Since management of information resources is getting more complicated in the distributed, heterogeneous computing environment, the capability of monitoring and controlling the dispersed information resources is perceived as a critical success factor for the effective enterprise-wide information resource management. Integrated Control Architecture(ICA) provides that capability. Utilizing such architecture, we can manage corporate information resources more efficiently, perform impact analysis for changes in information resources, and alleviate the human effort by automating the monitoring of critical information resources. In this paper, we propose a conceptual framework and metamodel of ICA.

• Current Optics and Photonics
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• v.2 no.5
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• pp.453-459
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• 2018

We have developed a high-performance signal-processing and image-rendering heterogeneous computation system for optical coherence tomography (OCT) on mobile processor. In this paper, we reveal it by demonstrating real-time OCT image processing using a Snapdragon 800 mobile processor, with the introduction of a heterogeneous image visualization architecture (HIVA) to accelerate the signal-processing and image-visualization procedures. HIVA has been designed to maximize the computational performances of a mobile processor by using a native language compiler, which targets mobile processor, to directly access mobile-processor computing resources and the open computing language (OpenCL) for heterogeneous computation. The developed mobile image processing platform requires only 25 ms to produce an OCT image from $512{\times}1024$ OCT data. This is 617 times faster than the naïve approach without HIVA, which requires more than 15 s. The developed platform can produce 40 OCT images per second, to facilitate real-time mobile OCT image visualization. We believe this study would facilitate the development of portable diagnostic image visualization with medical imaging modality, which requires computationally expensive procedures, using a mobile processor.

• Journal of the Korea Society for Simulation
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• v.15 no.3
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• pp.1-9
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• 2006

As an effective solution to resolve complex computing problems and to handle geographically dispersed data sets, grid computing has been noticed. Grid computing separates an application to several parts and executes on heterogeneous computing platforms simultaneously. The most important problem in grid computing environments is to manage grid resources and to schedule grid resources. This paper proposes a grid transaction network model that is applicable for resource management and scheduling in grid computing environment and presents a grid resource bidding algorithm for grid users and grid resource providers. Using DEVSJAVA modeling and simulation, this paper evaluates usefulness and efficiency of the proposed model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.283-290
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• 2016

As the development of recent science and technology, high-performance computing resources is needed to solve complex problems. To reach these requirements, it has been actively studied about grid computing that consist of a huge system which bind a heterogeneous high performance computing resources into on which are geographically dispersed. However, The current research situation which are the process to obtain the best results in the limited resources and the scheduling policy to accurately predict the total execution time of the real-time task are very poor. In this paper, in order to overcome these problems, we suggested a grid computing-based risk management system which derived from the system structure and the process for improving the efficiency of the system, grid computing-based working methodology, risk policy module which can manage efficiently the problem of the work of resources(Agent), scheduling technique and allocation method which can re-allocate the resource allocation and the resources in problem, and monitoring which can manage resources(Agent).

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.87-94
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• 2006

Grid computing has been developed for resolving large-scaled computing problems through geographically distributed heterogeneous resources. In order to guarantee effective and reliable job processing, grid computing needs resource scheduling model. So, we propose a resource performance measurement scheduling model which allocates job to resources with resource performance measurement. We assess resources using resource performance measurement formula, and implement the resource performance measurement scheduling model in DEVS simulation modeling.

• Journal of the Korea Society of Computer and Information
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• v.17 no.9
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• pp.1-8
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• 2012

Cloud computing has got great popularity in recent times because users can easily access its resources as well as service providers can use efficiently use its resources. By the way, cloud computing are composed of heterogeneous resources and workflows of user application have various characteristics. So, the main goal of this paper is to design new efficient workflow scheduling algorithm, which classifies workflows through their importance degree and allocates resources to each workflow based on QoS metrics such as responsibility, cost and load balancing. Simulation results show that the proposed scheme can improve the responsibility as well as availability of resource.