• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2011년도 한국컴퓨터종합학술대회논문집 Vol.38 No.1(C)
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• pp.60-63
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• 2011

인터넷은 단순한 정보습득을 위한 공간을 넘어 사람들이 모여 교류하는 '소통의 장'으로서 역할을 하고 있다. 최근에는 타인과 끊임없이 대화를 나누고 소통하려는 인간의 기본 욕구를 반영한 서비스인 SNS(Social Network Service)가 전 세계의 주목을 받고 있다. 또한 LBS(Location-Based Service)는 GPS, Wi-Fi를 통한 위치정보를 활용하여 업무생산성 개선 및 다양한 생활 편의를 제공하는 서비스이다. 본 논문에서는 안드로이드 OS를 기반으로 LBS와 SNS를 통합한 어플리케이션을 제안한다. 개인용 모바일 정보기기인 스마트폰을 활용하여, 위치정보에 이용자 정보, SNS를 결합하여 서비스를 고도화 한다. 지인들의 위치를 기반으로 현재 상태, 트윗(Twit)을 통해 정보의 공유 및 활용을 극대화 할 수 있다. 이를 통해 사용자들이 적극적이고 용이하게 온라인 Identity를 표현하는 것을 목적으로 본 시스템을 제안한다.

• Journal of information and communication convergence engineering
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• 제13권1호
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• pp.15-20
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• 2015

The limited battery power in the mobile environment, lack of sufficient wireless bandwidth, limited resources of mobile terminals, and frequent breakdowns of the wireless network have become major hurdles in the development of mobile cloud computing (MCC). In order to solve the abovementioned problems, This paper propose a proxy-based MCC framework by adding a proxy server between mobile devices and cloud services to optimize the access to cloud services by mobile devices on the network transmission, application support, and service mode levels. Finally, we verify the effectiveness of the developed framework through an experimental analysis. This framework can ensure that mobile users have efficient access to cloud services.

• 디지털콘텐츠학회 논문지
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• 제11권4호
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• pp.519-527
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• 2010

유비쿼터스 센서 네트워크(Ubiquitous Sensor Network) 기술은 새로운 컴퓨팅 패러다임인 유비쿼터스 컴퓨팅의 핵심 분야로 센서 네트워크를 이용한 상황정보 모니터링 시스템에 적합한 기술이다. 최근 대학들은 유비쿼터스 컴퓨팅을 기반으로 하는 유비쿼터스 캠퍼스 환경을 구축하기 위해 힘을 기울이고 있다. 본 논문에서는 각종 센서를 통해 상황인식을 위한 컨텍스트 정보를 획득하고, 장애학생의 모바일 기기를 통하여 장애유형별 학사 및 안전서비스를 제공할 수 있는 상황인지 모니터링 시스템을 구축한다. 유비쿼터스 센서 시스템을 기반으로 한 u-캠퍼스 기술은 특히 장애인과 비장애인을 위한 맞춤형 정보 제공에 활용될 수 있다.

• ETRI Journal
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• 제40권1호
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• pp.61-71
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• 2018

In this study, we developed hybrid control algorithms in smart base stations (SBSs) along with devised communication, caching, and computing techniques. In the proposed scheme, SBSs are equipped with computing power and data storage to collectively offload the computation from mobile user equipment and to cache the data from clouds. To combine in a refined manner the communication, caching, and computing algorithms, game theory is adopted to characterize competitive and cooperative interactions. The main contribution of our proposed scheme is to illuminate the ultimate synergy behind a fully integrated approach, while providing excellent adaptability and flexibility to satisfy the different performance requirements. Simulation results demonstrate that the proposed approach can outperform existing schemes by approximately 5% to 15% in terms of bandwidth utilization, access delay, and system throughput.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.95-100
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• 2022

Cloud computing platform is a shared pool of resources and services with various kind of models delivered to the customers through the Internet. The methods include an on-demand dynamically-scalable form charged using a pay-per-use model. The main problem with this model is the allocation of resource in dynamic. In this paper, we have proposed a mechanism to optimize the resource provisioning task by reducing the job completion time while, minimizing the associated cost. We present the Cooperative Agents Dynamic Resource Allocation and Monitoring in Cloud Computing CADRAM system, which includes more than one agent in order to manage and observe resource provided by the service provider while considering the Clients' quality of service (QoS) requirements as defined in the service-level agreement (SLA). Moreover, CADRAM contains a new Virtual Machine (VM) selection algorithm called the Node Failure Discovery (NFD) algorithm. The performance of the CADRAM system is evaluated using the CloudSim tool. The results illustrated that CADRAM system increases resource utilization and decreases power consumption while avoiding SLA violations.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.119-130
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• 2023

Worldwide organizations use the benefits offered by Cloud Computing (CC) to store data, software and programs. While running hugely complicated and sophisticated software on cloud requires more energy that causes global warming and affects environment. Most of the time energy consumption is wasted and it is required to explore opportunities to reduce emission of carbon in CC environment to save energy. Many improvements can be done in regard to energy efficiency from the software perspective by considering and paying attention on the energy consumption aspects of software's that run on cloud infrastructure. The aim of the current research is to propose a framework with an additional phase called parameterized development phase to be incorporated along with the traditional Software Development Life cycle (SDLC) where the developers need to consider the suggested techniques during software implementation to utilize low energy for running software on the cloud and contribute in green computing. Experiments have been carried out and the results prove that the suggested techniques and methods has enabled in achieving energy consumption.

• Advances in nano research
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• 제15권6호
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• pp.513-520
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• 2023

The exponential proliferation of cutting-edge computing technologies has spurred organizations to outsource their data and computational needs. In the realm of cloud-based computing environments, ensuring robust security, encompassing principles such as confidentiality, availability, and integrity, stands as an overarching imperative. Elevating security measures beyond conventional strategies hinges on a profound comprehension of malware's multifaceted behavioral landscape. This paper presents an innovative paradigm aimed at empowering cloud service providers to adeptly model user behaviors. Our approach harnesses the power of a Particle Swarm Optimization-based Probabilistic Neural Network (PSO-PNN) for detection and recognition processes. Within the initial recognition module, user behaviors are translated into a comprehensible format, and the identification of malicious nano-structures behaviors is orchestrated through a multi-layer neural network. Leveraging the UNSW-NB15 dataset, we meticulously validate our approach, effectively characterizing diverse manifestations of malicious nano-structures behaviors exhibited by users. The experimental results unequivocally underscore the promise of our method in fortifying security monitoring and the discernment of malicious nano-structures behaviors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권9호
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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.

• 한국멀티미디어학회논문지
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• 제20권12호
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• pp.1928-1939
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• 2017

Fog computing is another paradigm of the cloud computing, which extends the ubiquitous services to applications on many connected devices in the IoT (Internet of Things). In general, if we access a lot of IoT devices with existing cloud, we waste a huge amount of bandwidth and work efficiency becomes low. So we apply the paradigm called fog between IoT devices and cloud. The network architecture based on cloud and fog computing discloses the security and privacy issues according to mixed paradigm. There are so many security issues in many aspects. Moreover many IoT devices are connected at fog and they generate much data, therefore light and efficient security mechanism is needed. For example, with inappropriate encryption or authentication algorithm, it causes a huge bandwidth loss. In this paper, we consider issues related with data encryption and authentication mechanism in the network architecture for cloud and fog-based M2M (Machine to Machine) IoT services. This includes trusted encryption and authentication algorithm, and key generation method. The contribution of this paper is to provide efficient security mechanisms for the proposed service architecture. We implemented the envisaged conceptual security check mechanisms and verified their performance.

• 한국전산유체공학회지
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• 제11권2호
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• pp.19-24
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• 2006

A research to evaluate the efficiency of design optimization was carried out for aerodynamic design optimization problem in distributed computing environment. The aerodynamic analyses which take most of computational work during design optimization were divided into several jobs and allocated to associated PC clients through network. This is not a parallel process based on domain decomposition in a single analysis rather than a simultaneous distributed-analyses using network-distributed computers. GBOM(gradient-based optimization method), SAO(Sequential Approximate Optimization) and RSM(Response Surface Method) were implemented to perform design optimization of transonic airfoils and evaluate their efficiencies. dimensional minimization followed by direction search involved in the GBOM was found an obstacle against improving efficiency of the design process in the present distributed computing system. The SAO was found fairly suitable for the distributed computing environment even it has a handicap of local search. The RSM is apparently the most efficient algorithm in the present distributed computing environment, but additional trial and error works needed to enhance the reliability of the approximation model deteriorate its efficiency from the practical point of view.