• Journal of Information Processing Systems
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• 제9권2호
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• pp.189-204
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• 2013

The confinement problem was first noted four decades ago. Since then, a huge amount of efforts have been spent on defining and mitigating the problem. The evolution of technologies from traditional operating systems to mobile and cloud computing brings about new security challenges. It is perhaps timely that we review the work that has been done. We discuss the foundational principles from classical works, as well as the efforts towards solving the confinement problem in three domains: operating systems, mobile computing, and cloud computing. While common issues exist across all three domains, unique challenges arise for each of them, which we discuss.

• International Journal of Contents
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• 제1권1호
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• pp.50-58
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• 2005

Generally, multidimensional data such as image and spatial data require large amount of storage space. There is a limit to store and manage those large amounts of data in single workstation. If we manage the data on parallel computing environment which is being actively researched these days, we can get highly improved performance. In this paper, we propose a parallel multidimensional index structure that exploits the parallelism of the parallel computing environment. The proposed index structure is nP(processor)-nxmD(disk) architecture which is the hybrid type of nP-nD and 1P-nD. Its node structure in-creases fan-out and reduces the height of an index. Also, a range search algorithm that maximizes I/O parallelism is devised, and it is applied to k-nearest neighbor queries. Through various experiments, it is shown that the proposed method outperforms other parallel index structures.

• Journal of Computing Science and Engineering
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• 제5권4호
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• pp.346-353
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• 2011

With the advent of ubiquitous computing, a massive amount of trajectory data has been published and shared in many websites. This type of computing also provides motivation for online mining of trajectory data, to fit user-specific preferences or context (e.g., time of the day). While many trajectory clustering algorithms have been proposed, they have typically focused on offline mining and do not consider the restrictions of the underlying road network and selection conditions representing user contexts. In clear contrast, we study an efficient clustering algorithm for Boolean + Clustering queries using a pre-materialized and summarized data structure. Our experimental results demonstrate the efficiency and effectiveness of our proposed method using real-life trajectory data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권12호
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• pp.4950-4966
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• 2015

Cloud providers now face the problem of estimating the amount of computing resources required to satisfy a future workload. In this paper, a virtual machine provisioning (VMP) mechanism is designed to adapt workload fluctuation. The arrival rate of forthcoming jobs is predicted for acquiring the proper service rate by adopting an exponential smoothing (ES) method. The proper service rate is estimated to guarantee the service level agreement (SLA) constraints by using a diffusion approximation statistical model. The VMP problem is formulated as a facility location problem. Furthermore, it is characterized as the maximization of submodular function subject to the matroid constraints. A greedy-based VMP algorithm is designed to obtain the optimal virtual machine provision pattern. Simulation results illustrate that the proposed mechanism could increase the average profit efficiently without incurring significant quality of service (QoS) violations.

• Journal of Computing Science and Engineering
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• 제11권1호
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• pp.1-8
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• 2017

Naive Bayes nearest neighbor (NBNN) is a simple image classifier based on identifying nearest neighbors. NBNN uses original image descriptors (e.g., SIFTs) without vector quantization for preserving the discriminative power of descriptors and has a powerful generalization characteristic. However, it has a distinct disadvantage. Its memory requirement can be prohibitively high while processing a large amount of data. To deal with this problem, we apply a spherical hashing binary code embedding technique, to compactly encode data without significantly losing classification accuracy. We also propose using an inverted index to identify nearest neighbors among binarized image descriptors. To demonstrate the benefits of our method, we apply our method to two existing NBNN techniques with an image dataset. By using 64 bit length, we are able to reduce memory 16 times with higher runtime performance and no significant loss of classification accuracy. This result is achieved by our compact encoding scheme for image descriptors without losing much information from original image descriptors.

• 전자통신동향분석
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• 제38권5호
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• pp.23-33
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• 2023

With the widespread demand from data-intensive tasks such as machine learning and large-scale databases, the amount of data processed in modern computing systems is increasing exponentially. Such data-intensive tasks require large amounts of memory to rapidly process and analyze massive data. However, existing computing system architectures face challenges when building large-scale memory owing to various structural issues such as CPU specifications. Moreover, large-scale memory may cause problems including memory overprovisioning. The Compute Express Link (CXL) allows computing nodes to use large amounts of memory while mitigating related problems. Hence, CXL is attracting great attention in industry and academia. We describe the overarching concepts underlying CXL and explore recent research trends in this technology.

• 한국군사과학기술학회지
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• 제27권3호
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• pp.346-354
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• 2024

Deep learning models require high computing power due to a substantial amount of computation. It is difficult to use them in devices with limited computing environments, such as coastal surveillance equipments. In this study, a lightweight model is constructed by analyzing the weight changes of the convolutional layers during the training process based on MobileNet and then pruning the layers that affects the model less. The performance comparison results show that the lightweight model maintains performance while reducing computational load, parameters, model size, and data processing speed. As a result of this study, an effective pruning method for constructing lightweight deep learning models and the possibility of using equipment resources efficiently through lightweight models in limited computing environments such as coastal surveillance equipments are presented.

• International Journal of Computer Science & Network Security
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• 제24권8호
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• pp.105-118
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• 2024

The decision to integrate mobile cloud computing (MCC) in higher education without first defining suitable usage scenarios is a global issue as the usage of such services becomes extensive. Consequently, this study investigates the security determinants of the educational use of mobile cloud computing among universities students. This study proposes and develops a theoretical model by adopting and modifying the Protection Motivation Theory (PMT). The studys findings show that a significant amount of variance in MCC adoption was explained by the proposed model. MCC adoption intention was shown to be highly influenced by threat appraisal and coping appraisal factors. Perceived severity alone explains 37.8% of students "Intention" to adopt MCC applications, which indicates the student's perception of the degree of harm that would happen can hinder them from using MCC. It encompasses concerns about data security, privacy breaches, and academic integrity issues. Response cost, perceived vulnerability and response efficacy also have significant influence on students "intention" by 18.8%, 17.7%, and 6.7%, respectively.

• 서비스연구
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• 제2권2호
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• pp.91-98
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• 2012

새로운 유형의 임베디드 컴퓨팅 디바이스와 무선 랜의 발달으로 인하여 컴퓨팅의 영역이 직장, 가정부터 일반 생활까지 넓어지고 있다. 이런 트렌드는 사물 인터넷(Internet of Things, IoT) 환경의 발달을 가져오고 있다. 사물 인터넷 환경에서는 비싸지 않고 서로 연결되는 컴퓨팅 디바이스가 사용자의 다양한 작업을 자율적으로 지원한다. 이런 스마트 서비스를 개발하기 위해서는 컨텍스트 인지 컴퓨팅이 핵심 기술이다. 그러나 이런 서비스를 스마트하게 하기 위해서는 다양한 어려움이 있다. 대표적인 어려움으로는 다양한 컴퓨팅 환경, IoT 장비의 자원 제약사항, 다량의 IoT 장비로부터 생산되는 데이터, 컨텍스트 해석에 있어서의 다른 요구 사항 등이 있다. 이로 인하여 IoT 장비를 활용하여 스마트 서비스를 제공하는데에 어려움이 있다. 이런 상황을 해결하기 위하여 본 논문에서는 재사용 가능한 프레임워크를 제안한다.

• Journal of Information Processing Systems
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• 제20권3호
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.