• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.4
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• pp.241-255
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• 2015

Recent network environment has been rapidly evolved to cloud computing environment based on the development of the Internet technologies. Furthermore there is an increasing demand on mobile cloud computing due to explosive growth of smart devices and wide deployment of LTE-based cellular networks. Thus mobile Desktop-as-a-Service(DaaS) could be a pervasive service for nomadic users. In addition, video streaming traffic is currently more than two-thirds of mobile traffic and ever increasing. All such trends enable that video streaming in mobile DaaS could be an important concern for mobile cloud computing. It should be noted that the performance of the Transmission Control Protocol(TCP) on cloud host servers greatly affects Quality of Service(QoS) of video streams for mobile users. With widely deployed Linux server platforms for cloud computing, in this paper, we conduct an experimental analysis of the twelve Linux TCP variants in mobile DaaS environments. The results of our experiments show that the TCP Illinois outperforms the other TCP variants in terms of wide range of packet loss rate and propagation delay over LTE-based wireless links between cloud servers and mobile devices, even though TCP CUBIC is usually used in default in the current Linux systems.

• Journal of Internet Computing and Services
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• v.8 no.5
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• pp.11-20
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• 2007

This paper designs and implements a message passing library called JMPI (Java Message Passing Interface) which complies with MPJ (Message Passing in Java), the MPI standard Specification for Java language, This library provides some graphic user interface tools to enable parallel computing environments to be configured very simply by their administrators and JMPI applications to be executed very conveniently. Also in this paper, we implement two versions of systems using Socket and RPC which are both typical distributed system communication mechanisms and with three benchmark applications, compare performance of these systems with that of an existing system JPVM depending on the increasing number of the computers. Experimental results show that our systems outperform JPVM system in terms of various aspects and that the most efficient processing speedup can be obtained by increasing the number of the computers in consideration of network traffic through processing evaluation. Finally, we can see that, as the number of computers increases, using RMI to transmit a message is more effective than using object streams attached to sockets to transmit a message.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3378-3393
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• 2014

We have witnessed the rapid development of information technology in recent years. One of the key phenomena is the fast, near-exponential increase of data. Consequently, most of the traditional data classification methods fail to meet the dynamic and real-time demands of today's data processing and analyzing needs--especially for continuous data streams. This paper proposes an improved incremental learning algorithm for a large-scale data stream, which is based on SVM (Support Vector Machine) and is named DS-IILS. The DS-IILS takes the load condition of the entire system and the node performance into consideration to improve efficiency. The threshold of the distance to the optimal separating hyperplane is given in the DS-IILS algorithm. The samples of the history sample set and the incremental sample set that are within the scope of the threshold are all reserved. These reserved samples are treated as the training sample set. To design a more accurate classifier, the effects of the data volumes of the history sample set and the incremental sample set are handled by weighted processing. Finally, the algorithm is implemented in a cloud computing system and is applied to study user behaviors. The results of the experiment are provided and compared with other incremental learning algorithms. The results show that the DS-IILS can improve training efficiency and guarantee relatively high classification accuracy at the same time, which is consistent with the theoretical analysis.

• Journal of Computing Science and Engineering
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• v.9 no.3
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• pp.134-141
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• 2015

In on-demand multimedia streaming systems, streaming techniques are usually combined with proxy caching to obtain better performance. The patch streaming technique has no start-up latency inherent to it, but requires extra bandwidth to deliver the media data in patch streams. This paper proposes a proxy caching technique which aims at reducing the bandwidth cost of the patch streaming technique. The proposed approach determines media prefixes with high patching cost and caches the appropriate media prefix at the proxy/local server. Herein the scheme is evaluated using a synthetically generated media access workload and its performance is compared with that of the popularity and prefix-aware interval caching scheme (the prefix part) and with that of patch streaming with no caching. The bandwidth saving, hit ratio and concurrent number of clients are used to compare the performance, and the proposed scheme is found to perform better for different caching capacities of the proxy server.

• Journal of ILASS-Korea
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• v.14 no.1
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• pp.1-7
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• 2009

This study carried out numerical analysis of flow field of combustion gas and sorbent to test sorbent efficiency of DSI process. To provide rapid mixing for increase utilization rate of sorbent, streamwise vorticity can be introduced into the flowing streams by other means; for example, by installing vortex generators immediately downstream of the wavy trailing edge. Computing results show that the degree of sorbent dispersion depends strongly on duct structure. Highest dispersion efficiency received when vortex generator was installed inside of duct. The results presented in this study a optimum condition for the development of practical DSI process.

• Journal of IKEEE
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• v.24 no.4
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• pp.1011-1016
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• 2020

We propose a framework called Stacked Gated Recurrent Unit - Infrequent Residual Analysis (SG-IRA) that detects anomalies in time-series data that can be trained on streams of raw sensor data without any pre-labeled dataset. To enable such unsupervised learning, SG-IRA includes an estimation model that uses a stacked Gated Recurrent Unit (GRU) structure and an analysis method that detects anomalies based on the difference between the estimated value and the actual measurement (residual). SG-IRA's residual analysis method dynamically adapts the detection threshold from the population using frequency analysis, unlike the baseline model that relies on a constant threshold. In this paper, SG-IRA is evaluated using the industrial control systems (ICS) datasets. SG-IRA improves the detection performance (F1 score) by 5.9% compared to the baseline model.

• ETRI Journal
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• v.45 no.6
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• pp.1035-1045
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• 2023

We propose a novel graphics processing unit (GPU) algorithm that can handle a large-scale 3D fast Fourier transform (i.e., 3D-FFT) problem whose data size is larger than the GPU's memory. A 1D FFT-based 3D-FFT computational approach is used to solve the limited device memory issue. Moreover, to reduce the communication overhead between the CPU and GPU, we propose a 3D data-transposition method that converts the target 1D vector into a contiguous memory layout and improves data transfer efficiency. The transposed data are communicated between the host and device memories efficiently through the pinned buffer and multiple streams. We apply our method to various large-scale benchmarks and compare its performance with the state-of-the-art multicore CPU FFT library (i.e., fastest Fourier transform in the West [FFTW]) and a prior GPU-based 3D-FFT algorithm. Our method achieves a higher performance (up to 2.89 times) than FFTW; it yields more performance gaps as the data size increases. The performance of the prior GPU algorithm decreases considerably in massive-scale problems, whereas our method's performance is stable.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.1
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• pp.370-381
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• 2012

Multi-criteria result extraction is crucial in many scientific applications that support real-time stream processing, such as habitat research and disaster monitoring. Skyline evaluation is computational intensive especially over continuous time-interval data streams where each object has its own customized expiration time. In this work, we propose TI-Sky - a continuous skyline evaluation framework. To ensure correctness, the result space needs to be continuously maintained as new objects arrive and older objects expire. TI-Sky strikes a perfect balance between the costs of continuously maintaining the result space and the costs of computing the final skyline result from this space whenever a pull-based user query is received. Our key principle is to incrementally maintain a partially precomputed skyline result space - however doing so efficiently by working at a higher level of abstraction. TI-Sky's algorithms for insertion, deletion, purging and result retrieval exploit both layers of granularity. Our experimental study demonstrates the superiority of TI-Sky over existing techniques to handle a wide variety of data sets.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.406-412
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• 2022

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.43-54
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• 2009

As R&D(Research and Development) is going on actively to develop technologies for the ubiquitous computing environment, which Is the human-oriented future computing environment, GIS dealing with spatio-temporal data is emerging as a promising technology. This also increases the necessity of the middleware for providing services to give interoperability in various heterogeneous environments. The core technologies of the middleware are real-time processing technology of data streams coming unceasingly from positioning systems and data stream processing technology developed for non-spatio-temporal data. However, it has problems in processing queries on spatio-temporal data efficiently. Accordingly, this paper designed and implemented the spatio-temporal middleware that provides interoperability between a mobile spatio-temporal DBMS(DataBase Management System) and a server spatio-temporal MMDBMS(Main Memory DataBase Management System). The spatio-temporal middleware maintains interoperability among heterogeneous devices and guarantees data integrity in query processing through real-time processing of unceasing spatio-temporal data streams and two way synchronization of spatio-temporal DBMSs. In addition, it manages session for the connection of each spatio-temporal DBMS and manages resources for its stable operation. Finally, this paper proved the usability of the spatio-temporal middleware by applying it to a real-time position tracking system.