• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2851-2873
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• 2016

Cloud storage as a service provides high scalability and availability as per need of user, without large investment on infrastructure. However, data security risks, such as confidentiality, privacy, and integrity of the outsourced data are associated with the cloud-computing model. Over the year's techniques such as, remote data checking (RDC), data integrity protection (DIP), provable data possession (PDP), proof of storage (POS), and proof of retrievability (POR) have been devised to frequently and securely check the integrity of outsourced data. In this paper, we improve the efficiency of PDP scheme, in terms of computation, storage, and communication cost for large data archives. By utilizing the capabilities of JAR and ZIP technology, the cost of searching the metadata in proof generation process is reduced from O(n) to O(1). Moreover, due to direct access to metadata, disk I/O cost is reduced and resulting in 50 to 60 time faster proof generation for large datasets. Furthermore, our proposed scheme achieved 50% reduction in storage size of data and respective metadata that result in providing storage and communication efficiency.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.1-7
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• 2013

Big data management is becoming increasingly important in both industry and academia of information science community. One of the important categories of big data generated from software systems is log data. Log data is generally used for better services in various service providers and can also be used as information for qualification. This paper presents a big data management architecture specialized for log data. Specifically, it provides the aggregation of log messages sent from multiple clients and provides intelligent functionalities such as analyzing log data. The proposed architecture supports an asynchronous process in client-server architectures to prevent the potential bottleneck of accessing data. Accordingly, it does not affect the client performance although using remote data store. We implement the proposed architecture and show that it works well for processing big log data. All components are implemented based on open source software and the developed prototypes are now publicly available.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.740-743
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• 2012

The initial role of the operating system acts as an intermediary between the computer and the user, and, hardware and process management, and the convenience of your computer system is to use. Of these operating systems as well as servers and personal computers, smartphones and tablet mounted on mobile devices such as mobile operating system was born. Mobile Operating System has been expanded a TV or Car Area that built into a simple embedded operating system, is emergence of a variety of devices, cloud services, combined with the desire of users due to the high built-in simple embedded operating system that was working on a TV or a car is expanding to the area. The reason for the emergence of a variety of devices, cloud services, combined with the desire of users is high. In this paper, the mobile operating system, N-Screen, Smart TV to find out about and through the analysis of the major smart TV, the future Find out about trends in the mobile operating system.

• Journal of KIISE
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• v.43 no.8
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• pp.933-945
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• 2016

Sharing data by multiple users on the public storage, e.g., the cloud, is considered to be efficient because the cloud provides on-demand computing service at anytime and anywhere. Secure data sharing is achieved by fine-grained access control. Existing symmetric and public key encryption schemes are not suitable for secure data sharing because they support 1-to-1 relationship between a ciphertext and a secret key. Attribute based encryption supports fine-grained access control, however it incurs linearly increasing ciphertexts as the number of attributes increases. Additionally, the decryption process has high computational cost so that it is not applicable in case of resource-constrained environments. In this study, we propose an efficient attribute-based secure data sharing scheme with outsourceable decryption. The proposed scheme guarantees constant-size ciphertexts irrespective of the number of attributes. In case of static attributes, the computation cost to the user is reduced by delegating approximately 95.3% of decryption operations to the more powerful storage systems, whereas 72.3% of decryption operations are outsourced in terms of dynamic attributes.

• Journal of Communications and Networks
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• v.13 no.6
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• pp.591-601
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• 2011

Today's Internet which provides communication channels with best-effort end-to-end performance is rapidly evolving into an autonomic global computing platform. Achieving autonomicity in the Future Internet will require a performance architecture that (a) allows users to request and own 'slices' of geographically-distributed host and network resources, (b) measures and monitors end-to-end host and network status, (c) enables analysis of the measurements within expert systems, and (d) provides performance intelligence in a timely manner for application adaptations to improve performance and scalability. We describe the requirements and design of one such "Future Internet performance architecture" (FIPA), and present our reference implementation of FIPA called 'OnTimeMeasure.' OnTimeMeasure comprises of several measurement-related services that can interact with each other and with existing measurement frameworks to enable performance intelligence. We also explain our OnTimeMeasure deployment in the global environment for network innovations (GENI) infrastructure collaborative research initiative to build a sliceable Future Internet. Further, we present an applicationad-aptation case study in GENI that uses OnTimeMeasure-enabled performance intelligence in the context of dynamic resource allocation within thin-client based virtual desktop clouds. We show how a virtual desktop cloud provider in the Future Internet can use the performance intelligence to increase cloud scalability, while simultaneously delivering satisfactory user quality-of-experience.

• Korean Medical Education Review
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• v.18 no.2
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• pp.51-57
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• 2016

This literature review explores artificial intelligence (AI) technology trends and IBM Watson health and medical references. This study explains how healthcare will be changed by the evolution of AI technology, and also summarizes key technologies in AI, specifically the technology of IBM Watson. We look at this issue from the perspective of 'information overload,' in that medical literature doubles every three years, with approximately 700,000 new scientific articles being published every year, in addition to the explosion of patient data. Estimates are also forecasting a shortage of oncologists, with the demand expected to grow by 42%. Due to this projected shortage, physicians won't likely be able to explore the best treatment options for patients in clinical trials. This issue can be addressed by the AI Watson motivation to solve healthcare industry issues. In addition, the Watson Oncology solution is reviewed from the end user interface point of view. This study also investigates global company platform business to explain how AI and machine learning technology are expanding in the market with use cases. It emphasizes ecosystem partner business models that can support startup and venture businesses including healthcare models. Finally, we identify a need for healthcare company partnerships to be reviewed from the aspect of solution transformation. AI and Watson will change a lot in the healthcare business. This study addresses what we need to prepare for AI, Cognitive Era those are understanding of AI innovation, Cloud Platform business, the importance of data sets, and needs for further enhancement in our knowledge base.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1164-1183
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• 2019

The rapid development of cloud computing and high requirements of operators requires strong support from the underlying Data Center Networks. Therefore, the effectiveness of using resources in the data center networks becomes a point of concern for operators and material for research. In this paper, we discuss the online virtual-cluster provision problem for multiple tenants with an aim to decide when and where the virtual cluster should be placed in a data center network. Our objective is maximizing the total revenue for the data center networks under the constraints. In order to solve this problem, this paper divides it into two parts: online multi-tenancy scheduling and virtual cluster placement. The first part aims to determine the scheduling orders for the multiple tenants, and the second part aims to determine the locations of virtual machines. We first approach the problem by using the variational inequality model and discuss the existence of the optimal solution. After that, we prove that provisioning virtual clusters for a multi-tenant data center network that maximizes revenue is NP-hard. Due to the complexity of this problem, an efficient heuristic algorithm OMS (Online Multi-tenancy Scheduling) is proposed to solve the online multi-tenancy scheduling problem. We further explore the virtual cluster placement problem based on the OMS and propose a novel algorithm during the virtual machine placement. We evaluate our algorithms through a series of simulations, and the simulations results demonstrate that OMS can significantly increase the efficiency and total revenue for the data centers.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.49-54
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• 2022

With the recent widespread adoption of general-purpose GPUs (GPGPUs) in cloud systems, maximizing the resource utilization through multitasking in GPGPU has become an important issue. In this article, we show that resource allocation based on the workload classification of computing-bound and memory-bound is not sufficient with respect to resource utilization, and present a new thread block scheduling policy for GPGPU that makes use of fine-grained resource utilizations of each workload. Unlike previous approaches, the proposed policy reduces scheduling overhead by separating profiling and scheduling, and maximizes resource utilizations by co-locating workloads with different bottleneck resources. Through simulations under various virtual machine scenarios, we show that the proposed policy improves the GPGPU throughput by 130.6% on average and up to 161.4%.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.51-58
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• 2021

Microservices are not only developed independently, but can also be run and deployed independently, ensuring more flexible scaling and efficient collaboration in a cloud computing environment. This impact has led to a surge in migrating to microservices-oriented application environments in recent years. In order to introduce microservices, the problem of identifying microservice units in a single application built with a single architecture must first be solved. In this paper, we propose an algorithm-based approach to identify microservices from legacy systems. A graph is generated using the meta-information of the legacy code, and a microservice candidate is extracted by applying a clustering algorithm. Modularization quality is evaluated using metrics for the extracted microservice candidates. In addition, in order to validate the proposed method, candidate services are derived using codes of open software that are widely used for benchmarking, and the level of modularity is evaluated using metrics. It can be identified as a smaller unit of microservice, and as a result, the module quality has improved.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.175-186
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• 2019

Efforts have been made to introduce the climate smart agriculture (CSA) for adaptation to future climate conditions, which would require collection and management of site specific meteorological data. The objectives of this study were to identify requirements for construction of agricultural meteorology information service system (AMISS) using technologies that lead to the fourth industrial revolution, e.g., internet of things (IoT), artificial intelligence, and cloud computing. The IoT sensors that require low cost and low operating current would be useful to organize wireless sensor network (WSN) for collection and analysis of weather measurement data, which would help assessment of productivity for an agricultural ecosystem. It would be recommended to extend the spatial extent of the WSN to a rural community, which would benefit a greater number of farms. It is preferred to create the big data for agricultural meteorology in order to produce and evaluate the site specific data in rural areas. The digital climate map can be improved using artificial intelligence such as deep neural networks. Furthermore, cloud computing and fog computing would help reduce costs and enhance the user experience of the AMISS. In addition, it would be advantageous to combine environmental data and farm management data, e.g., price data for the produce of interest. It would also be needed to develop a mobile application whose user interface could meet the needs of stakeholders. These fourth industrial revolution technologies would facilitate the development of the AMISS and wide application of the CSA.