• The KIPS Transactions:PartA
• /
• v.13A no.4 s.101
• /
• pp.363-370
• /
• 2006

Currently, many people have been researching diverse mechansmims related to a resource access control in Grid environment. Mostly Grid user's resource access control was designed to authorize according to their attributes and roles. But, to provide Grid with resources continuously, a resource access based on utility computing must be controlled. So, in this paper we propose and implement mechanism that intergrates Grid accounting concept with resource access control. This mechanism calcuates costs of Grid service on the basis of accounting, and determines based on user's fund availibility whether they continue to make use of site resources or not. Grid jobs will be controlled according to a site resource access control policy only if the amount of available fund is less than its costs. If Grid job completed, resource consumer pays for the costs generated by using provider's idle resources. Therefore, this paper provides mechansim to be able to control user's resource access by Grid accounting, so that it is evaluated as the research to realize utility computing environment corresponding to economic principle.

• Knowledge Management Research
• /
• v.10 no.4
• /
• pp.115-131
• /
• 2009

This paper attempts to show that consumers' own information processing mode can play an important role in inducing favorable product evaluations, which is the most key goal of marketing. Th elaboration likelihood model contends that consumers' motivation and knowledge, in addition to the outside marketing information, affects the evaluation process. On the other hand, The resource matching hypothesis suggests that an excessively high level of information processing may lead to negative evaluations. In this study, Need for closure exacerbated overall evaluations of consumers. Such relationship was more salient in the condition of low severity that in the condition of high severity. Also under the situation of low severity, consumers with high level of relevant knowledge made evaluations more favorable, compared to the consumers of low knowledge. On contrast under the situation of high severity, relevant knowledge leaded to less favorable evaluations. This experiment identifies the appropriateness of the elaboration likelihood model and the resource matching hypothesis. Especially This study suggests an rare example that consumers' knowledge may not paly an desirable role in making their judgments.

• Journal of Information Processing Systems
• /
• v.10 no.1
• /
• pp.36-54
• /
• 2014

Resource sharing is a major advantage of distributed computing. However, a distributed computing system may have some physical or virtual resource that may be accessible by a single process at a time. The mutual exclusion issue is to ensure that no more than one process at a time is allowed to access some shared resource. The article proposes a token-based mutual exclusion algorithm for the clustered mobile ad hoc networks (MANETs). The mechanism that is adapted to handle token passing at the inter-cluster level is different from that at the intra-cluster level. It makes our algorithm message efficient and thus suitable for MANETs. In the interest of efficiency, we implemented a centralized token passing scheme at the intra-cluster level. The centralized schemes are inherently failure prone. Thus, we have presented an intra-cluster token passing scheme that is able to tolerate a failure. In order to enhance reliability, we applied a distributed token circulation scheme at the inter-cluster level. More importantly, the message complexity of the proposed algorithm is independent of N, which is the total number of nodes in the system. Also, under a heavy load, it turns out to be inversely proportional to n, which is the (average) number of nodes per each cluster. We substantiated our claim with the correctness proof, complexity analysis, and simulation results. In the end, we present a simple approach to make our protocol fault tolerant.

• Journal of Information Processing Systems
• /
• v.17 no.4
• /
• pp.721-736
• /
• 2021

For the mobile edge computing (MEC) system supporting dense network, a joint allocation algorithm of computing and communication resources based on reinforcement learning is proposed. The energy consumption of task execution is defined as the maximum energy consumption of each user's task execution in the system. Considering the constraints of task unloading, power allocation, transmission rate and calculation resource allocation, the problem of joint task unloading and resource allocation is modeled as a problem of maximum task execution energy consumption minimization. As a mixed integer nonlinear programming problem, it is difficult to be directly solve by traditional optimization methods. This paper uses reinforcement learning algorithm to solve this problem. Then, the Markov decision-making process and the theoretical basis of reinforcement learning are introduced to provide a theoretical basis for the algorithm simulation experiment. Based on the algorithm of reinforcement learning and joint allocation of communication resources, the joint optimization of data task unloading and power control strategy is carried out for each terminal device, and the local computing model and task unloading model are built. The simulation results show that the total task computation cost of the proposed algorithm is 5%-10% less than that of the two comparison algorithms under the same task input. At the same time, the total task computation cost of the proposed algorithm is more than 5% less than that of the two new comparison algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.10
• /
• pp.3419-3437
• /
• 2022

Anonymization technology is an important technology for privacy protection in the process of data release. Usually, before publishing data, the data publisher needs to use anonymization technology to anonymize the original data, and then publish the anonymized data. However, for data publishers who do not have or have less anonymized technical knowledge background, how to configure appropriate parameters for data with different characteristics has become a more difficult problem. In response to this problem, this paper adds a historical configuration scheme resource pool on the basis of the traditional anonymization process, and configuration parameters can be automatically recommended through the historical configuration scheme resource pool. On this basis, a privacy model hybrid recommendation algorithm for user satisfaction is formed. The algorithm includes a forward recommendation process and a reverse recommendation process, which can respectively perform data anonymization processing for users with different anonymization technical knowledge backgrounds. The privacy model hybrid recommendation algorithm for user satisfaction described in this paper is suitable for a wider population, providing a simpler, more efficient and automated solution for data anonymization, reducing data processing time and improving the quality of anonymized data, which enhances data protection capabilities.

• Genomics & Informatics
• /
• v.17 no.2
• /
• pp.16.1-16.4
• /
• 2019

Medical Subject Headings (MeSH), a medical thesaurus created by the National Library of Medicine (NLM), is a useful resource for natural language processing (NLP). In this article, the current status of the Japanese version of Medical Subject Headings (MeSH) is reviewed. Online investigation found that Japanese-English dictionaries, which assign MeSH information to applicable terms, but use them for NLP, were found to be difficult to access, due to license restrictions. Here, we investigate an open-source Japanese-English glossary as an alternative method for assigning MeSH IDs to Japanese terms, to obtain preliminary data for NLP proof-of-concept.

• Journal of the Korea Society of Computer and Information
• /
• v.13 no.3
• /
• pp.169-177
• /
• 2008

Grid resources are composed of various communication networks and operation systems. When a grid system searches and selects grid resources, which meet requirements of a grid user, existing grid resource selection systems are limited due to their storage methods for resource information. In order to select grid resources suitable for requirements of a grid user and characteristics of data, this paper constructs an ontology for grid resources and proposes an ontology-based grid resource selection system. This system provides an inference engine based on rules defined by SWRL to create a resource list. Experimental results comparing the proposed system with existing grid resource selection systems, such as the Condor-G and the Nimrod-G, verify the effectiveness of the ontology-based grid resource selection system with improved job throughput and resource utilization and reduced job loss and job processing time.

• Journal of Information Processing Systems
• /
• v.11 no.2
• /
• pp.280-294
• /
• 2015

This paper presents the infectious watermarking model (IWM) for the protection of video contents that are based on biological virus modeling by the infectious route and procedure. Our infectious watermarking is designed as a new paradigm protection for video contents, regarding the hidden watermark for video protection as an infectious virus, video content as host, and codec as contagion medium. We used pathogen, mutant, and contagion as the infectious watermark and defined the techniques of infectious watermark generation and authentication, kernel-based infectious watermarking, and content-based infectious watermarking. We experimented with our watermarking model by using existing watermarking methods as kernel-based infectious watermarking and content-based infectious watermarking medium, and verified the practical applications of our model based on these experiments.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.04a
• /
• pp.26-29
• /
• 2017

최근 중앙 집중화된 대규모 클라우드 시스템의 증가로 인해 가상화 환경에서 수행되는 성능 최적화 작업에 대한 많은 연구가 진행되고 있다. 그러나 기존 연구에서는 자원 분배의 공정성을 위해 가상머신 단위로 컴퓨팅 자원을 격리한 정책 내에서 이루어지고 있어 유연한 자원관리에 한계를 가지고 있다. 본 연구에서는 워크로드의 특징을 기반으로 과학적 연산을 수행하는 가상머신과 일반적인 서비스를 수행하는 가상머신을 분류하여 성능 최적화 작업을 수행하는 동적 자원 관리자를 제안한다. 실험을 통하여 제안하는 동적 자원 관리자가 KVM 기본 스케줄링에 비해 49%의 성능 향상을 보였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2016.10a
• /
• pp.363-366
• /
• 2016

추천 기법은 개인의 관심사와 상황을 고려한 개인화된 아이템을 제공함으로써 아이템의 소비과정에서 발생하는 부하를 줄여주고 정보 소비의 효율성을 증대시키는데 중요한 역할을 한다. 본 연구에서는 전통적인 추천 기법인 Content-Based(CB)기법과 최근 온라인 소셜 네트워크의 경향을 반영한 Social Network-based(SN)기법을 접목하여 새로운 복합방식의 정보 추천 기법을 제시한다. CB 기법의 대표적인 한계점인 cold start problem과 SN 기법의 추천 아이템의 전문성 문제를 상호 보완하며, 특히 최근 소셜 네트워크의 특징인 비신뢰 (non-trust) 기반의 영향력 있는 정보 확산자가 존재하는 환경에서 기법을 적용할 수 있도록 하였다. 또한 대부분 사람 추천 중심인 기존의 SN 기법들과는 달리 사람에게 제공할 정보의 추천에 초점을 두며, 정보 선정과정에서 개인의 온라인과 현실(real world)에서의 사회 활동 정보를 모두 활용하여 더육 더 개인화된 가치 정보를 제공하고자 한다.