• Journal of KIISE:Computer Systems and Theory
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• v.34 no.1
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• pp.1-9
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• 2007

At present various methods relating resource access control in grid environment are being studied. Most of the access authorization to grid resource is designed fit to the attributes and the role of user. But resource access control is to be made in the respect of business model to activate grid. Therefore this study suggests a model that can operate resource access control driven by grid accounting information. On the base of collection of accounting information about grid job, processing cost is yielded. If the user's available fund is less than processing cost, it gets to control grid job by the resource access control policy. Finally when grid job is completed, user is assigned to pay the charges for using resource of supplier. Then resource provider gets to supply stable resource in grid by participating it voluntarily to use idle resource. This study is esteemed to realize utility computing environment correspondent to economic principle by ensuring resource access policy of organizations which participate in grid.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.7
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• pp.219-230
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• 2014

In the cloud computing environment, cloud service users purchase and use the virtualized resources from cloud resource providers on a pay as you go manner. Typically, there are two billing plans for computing resource allocation adopted by large cloud resource providers such as Amazon, Gogrid, and Microsoft, on-demand and reserved plans. Reserved Virtual Machine(VM) instance is provided to users based on the lengthy allocation with the cheaper price than the one of on-demand VM instance which is based on shortly allocation. With the proper mixture allocation of reserved and on-demand VM corresponding to users' requests, cloud service providers are able to reduce the resource allocation cost. To do this, prior researches about VM allocation scheme have been focused on the optimization approach with the users' request prediction techniques. However, it is difficult to predict the expected demands exactly because there are various cloud service users and the their request patterns are heavily fluctuated in reality. Moreover, the previous optimization processing techniques might require unacceptable huge time so it is hard to apply them to the current cloud computing system. In this paper, we propose the cloud brokering system with the adaptive VM allocation schemes called A3R(Adaptive 3 Resource allocation schemes) that do not need any optimization processes and kinds of prediction techniques. By using A3R, the VM instances are allocated to users in response to their service demands adaptively. We demonstrate that our proposed schemes are able to reduce the resource use cost significantly while maintaining the acceptable Quality of Service(QoS) of cloud service users through the evaluation results.

• 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.

• 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.

• The KIPS Transactions:PartA
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• v.13A no.6 s.103
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• pp.533-540
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• 2006

This paper describes the design and implementation of a grid system META (Metacomputing Environment using Test-run of Application) which facilitates the execution of a CFD (Computational Fluid Dynamics) analysis program on distributed environment. The grid system META allows the CFD program developers can access the computing resources distributed over the network just like one computer system. The research issues involved in the grid computing include fault-tolerance, computing resource selection, and user-interface design. In this paper, we exploits an automatic resource selection scheme for executing the parallel SPMD (Single Program Multiple Data) application written in MPI (Message Passing Interface). The proposed resource selection scheme is informed from the network latency time and the elapsed time of the kernel loop attained from test-run. The network latency time highly influences the executional performance when a parallel program is distributed and executed over several systems. The elapsed time of the kernel loop can be used as an estimator of the whole execution time of the CFD Program due to a common characteristic of CFD programs. The kernel loop consumes over 90% of the whole execution time of a CFD program.

• Journal of KIISE
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• v.42 no.5
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• pp.573-579
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• 2015

Nationwide supercomputing infrastructures in Korea consist of geographically distributed supercomputing clusters. We developed High-Throughput Computing as a Service(HTCaaS) based on these distributed national supecomputing clusters to facilitate the ease at which scientists can explore large-scale and complex scientific problems. In this paper, we present our mechanism for dynamically managing computing resources and show its effectiveness through a case study of a real scientific application called drug repositioning. Specifically, we show that the resource utilization, accuracy, reliability, and usability can be improved by applying our resource management mechanism. The mechanism is based on the concepts of waiting time and success rate in order to identify valid computing resources. The results show a reduction in the total job completion time and improvement of the overall system throughput.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.450-470
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• 2023

Traditional cloud computing faces some challenges such as huge energy consumption, network delay and single point of failure. Edge computing is a typical distributed processing platform which includes multiple edge servers closer to the users, thus is more robust and can provide real-time computing services. Although outsourcing data to edge servers can bring great convenience, it also brings serious security threats. In order to provide image retrieval while ensuring users' data privacy, a privacy preserving image retrieval scheme in edge environment is proposed. Considering the distributed characteristics of edge computing environment and the requirement for lightweight computing, we present a privacy-preserving image retrieval scheme in edge computing environment, which two or more "honest but curious" servers retrieve the image quickly and accurately without divulging the image content. Compared with other traditional schemes, the scheme consumes less computing resources and has higher computing efficiency, which is more suitable for resource-constrained edge computing environment. Experimental results show the algorithm has high security, retrieval accuracy and efficiency.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.516-519
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• 2014

Cloud computing is an up-and-coming paradigm shift transforming computing models from a technology to a utility. However, security concerns related to privacy, confidentiality and trust are among the issues that threaten the wide deployment of cloud computing. With the advancement of ubiquitous mobile-based clients, the ubiquity of the model suggests a higher integration in our day to day life and this leads to a rise in security issues. To strengthen the access control of cloud resources, most organizations are acquiring Identity Management Systems (IDM). This paper presents one of the most popular IDM systems, specifically OAuth, working in the scope of Mobile Cloud Computing which has many weaknesses in its protocol flow. OAuth is a Delegated Authorization protocol, and not an Authentication protocol and this is where the problem lies. This could lead to very poor security decisions around authentication when the basic OAuth flow is adhered to. OAuth provides an access token to a client, so that it can access a protected resource, based on the permission of the resource owner. Many researchers have opted to implement OpenlD alongside OAuth so as to solve this problem. But OpenlD similarly has several security flows. This paper presents scenarios of how insecure implementations of OAuth can be abused maliciously. We incorporate an authentication protocol to verify the identities before authorization is carried out.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.1063-1075
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• 2022

In recent years, container techniques have been broadly applied to cloud computing systems to maximize their efficiency, flexibility, and economic feasibility. Concurrently, studies have also been conducted to ensure the security of cloud computing. Among these studies, moving-target defense techniques using the high agility and flexibility of cloud-computing systems are gaining attention. Moving-target defense (MTD) is a technique that prevents various security threats in advance by proactively changing the main attributes of the protected target to confuse the attacker. However, an analysis of existing MTD techniques revealed that, although they are capable of deceiving attackers, MTD techniques have practical limitations when applied to an actual cloud-computing system. These limitations include resource wastage, management complexity caused by additional function implementation and system introduction, and a potential increase in attack complexity. Accordingly, this paper proposes a software-defined MTD system that can flexibly apply and manage existing and future MTD techniques. The proposed software-defined MTD system is designed to correctly define a valid mutation range and cycle for each moving-target technique and monitor system-resource status in a software-defined manner. Consequently, the proposed method can flexibly reflect the requirements of each MTD technique without any additional hardware by using a software-defined approach. Moreover, the increased attack complexity can be resolved by applying multiple MTD techniques.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.169-171
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• 2013

Cloud computing provides access to big volumes of data and computational resources through various services. Cloud computing also supports to process these volumes of data using set of computers. Cloud computing can satisfy resource requirements through virtualization technology. Eucalyptus is an open source cloud computing environment helps the users to setup their own private cloud based on virtualization. In this paper, monitoring of virtual machines is explained with the eucalyptus cloud setup.