• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.4
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• pp.51-59
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• 2009

Many failures are due to incorrectly programmed transactions and data entry errors. System failure causes the loss or corruption of the contents of volatile storage. Although global processing protects data values to detect direct or indirect information effluence, security environments are very important in the recovery management of heterogeneous systems. Although transaction can't control system fault, the restart for the system can cause information effluence by low bandwith. From various faults, it is not easy to maintain the consistency and security of data. This paper proposes recovery management protocols to assure global multilevel secure one-copy quasi-serializability in security environments of heterogeneous systems with replicated data and proves its correctness. The proposed secure protocols guarantee the reliability and security of system when the system fault is happened.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2019.05a
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• pp.55-56
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• 2019

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.1
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• pp.1-6
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• 2017

This paper presents an optimized adoption of NVM for the storage system of heterogeneous applications. Our analysis shows that a bulk of I/O does not happen on a single storage partition, but it is varied significantly for different application categories. In particular, journaling I/O accounts for a dominant portion of total I/O in DB applications like OLTP, whereas swap I/O accounts for a large portion of I/O in graph visualization applications, and file I/O accounts for a large portion in web browsers and multimedia players. Based on these observations, we argue that maximizing the performance gain with NVM is not obtained by fixing it as a specific storage partition but varied widely for different applications. Specifically, for graph visualization, DB, and multimedia player applications, using NVM as a swap, a journal, and a file system partitions, respectively, performs well. Our optimized adoption of NVM improves the storage performance by 10-61%.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.143-150
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• 2009

Spatial data warehouses are a system managing manufactured data through ETL step with extracted spatial data from spatial DBMS or various data sources. In load period, duplicated spatial data in the same subject are not useful in extracted spatial data dislike aspatial data and waste the storage space by the feature of spatial data. Also, in case of extracting source data on heterogeneous system, as those have different spatial type and schema, the spatial extract method is required for them. Processing a step matching address about extracted spatial data using a standard Geocoding DB, the exiting methods load formal data set. However, the methods cause the comparison operation of extracted data with Geocoding DB, and according to integrate spatial data by subject it has problems which do not consider duplicated data among heterogeneous spatial DBMS. This paper proposes efficient extracting method to integrate update query extracted from heterogeneous source systems in data warehouse constructer. The method eliminates unnecessary extracting operation cost to choose related update queries like insertion or deletion on queries generated from loading to current point. Also, we eliminate and integrate extracted spatial data using update query in source spatial DBMS. The proposed method can reduce wasting storage space caused by duplicate storage and support rapidly analyzing spatial data by loading integrated data per loading point.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.803-812
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• 2022

With the increasing number of mobile device users worldwide, utilizing mobile edge computing (MEC) devices close to users for content caching can reduce transmission latency than receiving content from a server or cloud. However, because MEC has limited storage capacity, it is necessary to determine the content types and sizes to be cached. In this study, we investigate a caching strategy that increases the hit ratio from small base stations (SBSs) for mobile users in a heterogeneous network consisting of one macro base station (MBS) and multiple SBSs. If there are several SBSs that users can access, the hit ratio can be improved by reducing duplicate content and increasing the diversity of content in SBSs. We propose a Deep Q-Network (DQN)-based caching strategy that considers time-varying content popularity and content redundancy in multiple SBSs. Content is stored in the SBS in a divided form using maximum distance separable (MDS) codes to enhance the diversity of the content. Experiments in various environments show that the proposed caching strategy outperforms the other methods in terms of hit ratio.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1129-1137
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• 2013

Cloud storage system that cloud computing providers provides large amounts of data storage and processing of cloud computing is a key component. Large vendors (such as Facebook, YouTube, Google) in the mass sending of data through the network quickly and easily share photos, videos, documents, etc. from heterogeneous devices, such as tablets, smartphones, and the data that is stored in the cloud storage using was approached. At time, growth and development of the globally data, the cloud storage business model emerging is getting. Analysis new network storage cloud storage services concepts and technologies, including data manipulation, storage virtualization, data replication and duplication, security, cloud computing core.

• Journal of Korean Library and Information Science Society
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• v.34 no.2
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• pp.187-207
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• 2003

With the rapidly evolving information technologies and the Internet, a network-based storage management system is required that can efficiently save, search, modify, and manage digital contents which are increasing by geometric progression. Recently, the SAN became highlighted as a solution to such issues as the integrated data management for heterogeneous systems, the effective utilization of storage systems, and the limitation of data transfer. SAN allows for effective management and sharing of bulk data by directly connecting the storage systems, which used to be independently connected to servers, to the high-speed networks such as optical channels. In this study, I researched on the technologies, standardization and market trends of storage management systems, and performed comparative analyses of the structures, components, and performances for achieving the integration of diverse system storage systems as well as the virtualization of storage systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1249-1272
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• 2016

In this paper, we propose a symmetric key establishment scheme for wireless sensor networks which tries to minimize the resource usage while satisfying the security requirements. This is accomplished by taking advantage of the communication pattern of wireless sensor networks and adopting heterogeneous wireless sensor networks. By considering the unique communication pattern of wireless sensor networks due to the nature of information gathering from the physical world, the number of keys to be established is minimized and, consequently, the overhead spent for establishing keys decreases. With heterogeneous wireless sensor networks, we can build a hybrid scheme where a small number of powerful nodes do more works than a large number of resource-constrained nodes to provide enhanced security service such as broadcast authentication and reduce the burden of resource-limited nodes. In addition, an on-demand key establishment scheme is introduced to support extra communications and optimize the resource usage. Our performance analysis shows that the proposed scheme is very efficient and highly scalable in terms of storage, communication and computation overhead. Furthermore, our proposed scheme not only satisfies the security requirements but also provides resilience to several attacks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4742-4770
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• 2019

Cloud computing provides a broad range of services like operating systems, hardware, software and resources. Availability of these services encourages data owners to outsource their intensive computations and massive data to the cloud. However, considering the untrusted nature of cloud server, it is essential to encrypt the data before outsourcing it to the cloud. Unfortunately, this leads to a challenge when it comes to providing search functionality for encrypted data located in the cloud. To address this challenge, this paper presents a public key encryption with equality test for heterogeneous systems (PKE-ET-HS). The PKE-ET-HS scheme simulates certificateless public encryption with equality test (CLE-ET) with the identity-based encryption with equality test (IBE-ET). This scheme provides the authorized cloud server the right to actuate the equivalence of two messages having their encryptions performed under heterogeneous systems. Basing on the random oracle model, we construct the security of our proposed scheme under the bilinear Diffie-Hellman (BDH) assumption. Eventually, we evaluate the size of storage, computation complexities, and properties with other related works and illustrations indicate good performance from our scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4063-4086
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• 2016

Cloud computing is a robust technology, which facilitate to resolve many parallel distributed computing issues in the modern Big Data environment. Hadoop is an ecosystem, which process large data-sets in distributed computing environment. The HDFS is a filesystem of Hadoop, which process data blocks to the cluster nodes. The data block placement has become a bottleneck to overall performance in a Hadoop cluster. The current placement policy assumes that, all Datanodes have equal computing capacity to process data blocks. This computing capacity includes availability of same storage media and same processing performances of a node. As a result, Hadoop cluster performance gets effected with unbalanced workloads, inefficient storage-tier, network traffic congestion and HDFS integrity issues. This paper proposes a storage-tier-aware Robust Data Placement (RDP) scheme, which systematically resolves unbalanced workloads, reduces network congestion to an optimal state, utilizes storage-tier in a useful manner and minimizes the HDFS integrity issues. The experimental results show that the proposed approach reduced unbalanced workload issue to 72%. Moreover, the presented approach resolve storage-tier compatibility problem to 81% by predicting storage for block jobs and improved overall data block placement by 78% through pre-calculated computing capacity allocations and execution of map files over respective Namenode and Datanodes.