• Proceedings of the Korea Database Society Conference
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• 1994.09a
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• pp.105-129
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• 1994

The information system of corporations are facing a new environment expressed by miniaturization, decentralization and Open System. It is therefore of utmost importance for corporations to adapt flexibly th such new environment by providing for corresponding changes to their existing information systems. The objectives of this study are to identify this new environment faced by today′s information system and develop effective methods for data resource management under this new environment. In this study, it is assumed that the new environment faced by information systems can be specified as Distributed Computing Environment, and in order to achieve such system, presents Client/server architecture as its representative computing structure, This study defines Client/server architecture as a computing architecture which specialize the fuctionality of the client system and the server system in order to have an application distribute and perform cooperative processing at the best platform. Furthermore, from among the five structures utilized in Client/server architecture for distribution and cooperative processing of application between server and client this study presents two different data management methods under the Client/server environment; one is "Remote Data Management Method" which uses file server or database server and. the other is "Distributed Data Management Method" using distributed database management system. The result of this study leads to the conclusion that in the client/server environment although distributed application is assumed, the data could become centralized (in the case of file server or database server) or decentralized (in the case of distributed database system) and the data management method through a distributed database system where complete responsibility and powers with respect to control of data used by the user are given not only is it more adaptable to modern flexible corporate environment, but in terms of system operation, it presents a more efficient data management alternative compared to existing data management methods in terms of cutting costs.

• Journal of Internet Computing and Services
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• v.11 no.3
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• pp.121-129
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• 2010

In recent years, Cloud computing is gaining much popularity as it can efficiently utilize the computing resources and hence can contribute to the issue of green IT. So to make the Cloud services commercialized, Cloud markets are necessary and are being developed. As the increasing numbers of various Cloud services are rapidly evolving in the Cloud market, how to select the best and optimal services will be a great challenge. In this paper we present a Cloud service selection framework in the Cloud market that uses a recommender system (RS) which helps a user to select the best services from different Cloud providers (CP) that matches his/her requirements. The RS recommends a service based on the QoS and Virtual Machine (VM) factors of difference CPs. The experimental results show that our Cloud service recommender system (CSRS) can effectively recommend a good combination of Cloud services to consumers.

• The Journal of Korean Association of Computer Education
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• v.11 no.3
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• pp.81-89
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• 2008

The failure occurrence of resources in the grid computing is higher than in a tradition parallel computing. Since the failure of resources affects job execution fatally, fault tolerance service is essential in computational grids. And grid services are often expected to meet some minimum levels of quality of service (QoS) for desirable operation. However Globus toolkit does not provide fault tolerance service that supports fault detection service and management service and satisfies QoS requirement. Thus this paper proposes fault tolerance service to satisfy QoS requirement in computational grids. In order to provide fault tolerance service and satisfy QoS requirements, we expand the definition of failure, such as process failure, processor failure, and network failure. And we propose resource scheduling service, fault detection service and fault management service and show implement and experiment results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2670-2677
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• 2014

The technique of virtual machine construction using hypervisor such as Xen and KVM is mainly used for implementation of cloud computing infrastructure. This technique is efficient in allocating and managing resources compared to the existing operation methods. However it requires high resource usage when constructing virtual machines and results in wasting of resources when not using the allocated resources. Docker is a technique based on the container method to resolve such a problem. This paper shows the container method such as Docker is efficient as a web construction technique by comparing virtual machine method to container method. It is shown to be especially useful when storing data into DB or storage devices in such environments of web server or program development. In the upcoming cloud computing environment the container method such as Docker is expected to improve the resource efficiency and the convenience of management.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.2
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• pp.37-52
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• 2005

Traditional access control models like role-based access control model are insufficient in security needs in ubiquitous computing environment because they take no thought of access control based on user's context or environment condition. In these days, although researches on context-aware access control using user's context or environment conditions based on role-based access control are emerged, they are on the primary stage. We present context definitions md an access control model to provide more flexible and dynamic context-aware access control based on role-based access control. Specially, we describe the conflict problems occurred in the middle of making an access decision. After classifying the conflict problems, we show some resolutions to solve them. In conclusion, we will lay the foundations of the development of security policy and model assuring right user of right object(or resource) and application service through pre-defined context and context classification in ubiquitous computing environments. Beyond the simplicity of access to objects by authorized users, we assure that user can access to the object, resource, or service anywhere and anytime according to right context.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.162-169
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• 2022

In a dynamically changing time-varying network environment, the optimal moving pattern of edge devices can be applied to distributing computing resources to edge cloud servers or deploying new edge servers in the FEC(Fog/Edge Computing) environment. In addition, this can be used to build an environment capable of efficient computation offloading to alleviate latency problems, which are disadvantages of cloud computing. This paper proposes an algorithm to extract the optimal moving pattern by analyzing the moving path of multiple edge devices requiring application services in an arbitrary spatio-temporal environment based on frequency. A comparative experiment with A^* and Dijkstra algorithms shows that the proposed algorithm uses a relatively fast execution time and less memory, and extracts a more accurate optimal path. Furthermore, it was deduced from the comparison result with the A^* algorithm that applying weights (preference, congestion, etc.) simultaneously with frequency can increase path extraction accuracy.

• Journal of the Korea Society for Simulation
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• v.15 no.3
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• pp.11-21
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• 2006

Importance of and need for grid resource management have accelerated in accordance with increasing development of grid computing. However, it is very complex to distribute and utilize resources efficiently in geographically dispersed environments. This is due to the different access policies and constraints of grid resource owners. Users request resources according to their needs. Operators of a grid computing system need to be able to monitor the system states for reflecting these demands. So, a grid computing system needs a resource management policy that monitors states of resources and then allocates resources. This paper proposes a user demand-based grid trade management model that provides an efficient resource management by the trade allocation based on a users' demand and providers' supply strategy. To evaluate performance, this paper measures increasing rate of resource trades, average response time of trades, and processing time utilization. Firstly, the average increasing rates of trade are 585.7% and 322.6% higher than an auction model and a double auction model. Secondly, the average response time of the user demand-based grid trade management model is maintained between 3 and 5 simulation time. Finally, it is found that the processing time utilization is an average of 145.4% and 118.0% higher than an auction model and a double auction model. These empirical results demonstrate the usefulness of the user demand-based grid trade management model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.47-52
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• 2023

With the growing data size and the increased computing load in machine learning, energy-efficient resource planning in IoT systems is becoming increasingly important. In this paper, we suggest a new resource planning policy for real-time workloads that can be fluctuated over time in IoT systems. To handle such situations, we categorize real-time tasks into fixed tasks and variable tasks, and optimize the resource planning for various workload conditions. Based on this, we initiate the IoT system with the configuration for the fixed tasks, and when variable tasks are activated, we update the resource planning promptly for the situation. Simulation experiments show that the proposed policy saves the processor and memory energy significantly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1258-1275
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• 2023

Cloud computing is a new technology that has adapted to the traditional way of service providing. Service providers are responsible for managing the allocation of resources. Selecting suitable containers and bandwidth for job scheduling has been a challenging task for the service providers. There are several existing systems that have introduced many algorithms for resource allocation. To overcome these challenges, the proposed system introduces an Optimized Task Scheduling Algorithm with Deep Learning (OTS-DL). When a job is assigned to a Cloud Service Provider (CSP), the containers are allocated automatically. The article segregates the containers as' Long-Term Container (LTC)' and 'Short-Term Container (STC)' for resource allocation. The system leverages an 'Optimized Task Scheduling Algorithm' to maximize the resource utilisation that initially inquires for micro-task and macro-task dependencies. The bottleneck task is chosen and acted upon accordingly. Further, the system initializes a 'Deep Learning' (DL) for implementing all the progressive steps of job scheduling in the cloud. Further, to overcome container attacks and errors, the system formulates a Container Convergence (Fault Tolerance) theory with high-level security. The results demonstrate that the used optimization algorithm is more effective for implementing a complete resource allocation and solving the large-scale optimization problem of resource allocation and security issues.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.75-94
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• 2021

The 4th industrial revolution needs a fusion of artificial intelligence, robotics, the Internet of Things (IoT), edge computing, and other technologies. For the fusion of technologies, cloud computing technology can provide flexible and high-performance computing resources so that cloud computing can be the foundation technology of new emerging services. The emerging services become a global-scale, and require much higher performance, availability, and reliability. Public cloud providers already provide global-scale services. However, their services, costs, performance, and policies are different. Enterprises/ developers to come out with a new inter-operable service are experiencing vendor lock-in problems. Therefore, multi-cloud technology that federatively resolves the limitations of single cloud providers is required. We propose a software platform, denoted as Cloud-Barista. Cloud-Barista is a multi-cloud service common platform for federating multiple clouds. It makes multiple cloud services as a single service. We explain the functional architecture of the proposed platform that consists of several frameworks, and then discuss the main design and implementation issues of each framework. To verify the feasibility of our proposal, we show a demonstration which is to create 18 virtual machines on several cloud providers, combine them as a single resource, and manage it.