• ETRI Journal
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• v.44 no.6
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• pp.1020-1033
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• 2022

In this paper, we propose novel disaggregated memory module (dMM) architecture and memory access control schemes to solve the collision and contention problems of memory disaggregation, reducing the average memory access time to less than 1 ㎲. In the schemes, the distributed scheduler in each dMM determines the order of memory read/write access based on delay-sensitive priority requests in the disaggregated memory access frame (dMAF). We used the memory-intensive first (MIF) algorithm and priority-based MIF (p-MIF) algorithm that prioritize delay-sensitive and/or memory-intensive (MI) traffic over CPU-intensive (CI) traffic. We evaluated the performance of the proposed schemes through simulation using OPNET and hardware implementation. Our results showed that when the offered load was below 0.7 and the payload of dMAF was 256 bytes, the average round trip time (RTT) was the lowest, ~0.676 ㎲. The dMM scheduling algorithms, MIF and p-MIF, achieved delay less than 1 ㎲ for all MI traffic with less than 10% of transmission overhead.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.1037-1044
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• 2008

In this paper, we show the performance of the power transformation GARCH(PGARCH) model to analyze the internet traffic data. The long memory property which is the typical characteristic of internet traffic data can be explained by the PGARCH model rather than the linear GARCH model. Small simulation and the analysis of the real internet traffic show the out-performance of the PARCH MODEL over the linear GARCH one.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.1053-1061
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• 2013

In this paper, we propose the time series forecasting models for internet traffic with long memory and heteroscedasticity. To control and forecast traffic volume, we first introduce the traffic forecasting models which are determined by the volatility and heteroscedasticity of the traffic. We then analyze and predict the heteroscedasticity and the long memory properties for forecasting traffic volume. Depending on the characteristics of the traffic, Fractional ARIMA model, Fractional ARIMA-GARCH model are applied and compared with the MAPE(Mean Absolute Percentage Error) Criterion.

• Journal of Oriental Neuropsychiatry
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• v.13 no.1
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• pp.117-125
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• 2002

Objectives: The purpose of this study was to investigate the clinical application of oriental medical therapy to a patient with memory disorders and quadriparesis caused by traffic accident. Methods: This study was carried out on a patient who was admitted to the Sangji oriental medical hospital, from January 21st in 2002 to May 2nd in 2002. We used 4 kinds of diagnosis(watching, asking, hearing, and toughing) and treated the patient with herbal medication and acupuncture therapy. Then we estimated the effect of memory disorders through MMSE-K(Mini mental State Examination-Korea) and K-DRS(Korean-Dementia Rating Scale). The numerical effect demonstrated ability of movement through range of motion. Results: Following the treatment the patient's mental state and the ability of movement improved. Conclusions: The present results suggest that oriental medical therapy has the positive effects on a patient with memory disorders and quadriparesis which were caused by traffic accident.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.307-313
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• 2011

In this paper, we compare performance of three models. The Holt-Winters, FARIMA and ARGARCH models, are used in predicting internet traffic data for analysis of traffic characteristics. We first introduce the time series models and apply them to real traffic data to forecast. Finally, we examine which model is the most suitable for explaining the long memory, the characteristics of the traffic material, and compare the respective prediction performance of the models.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.57-62
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• 2022

Recently, fast storage media such as phage-change memory (PCM) emerge, and memory management policies for slow disk storage need to be revisited. In this paper, we propose a new page replacement policy that makes use of PCM as a swap device of virtual memory systems. The proposed policy aims at reducing write traffic to the swap device as well as reducing the number of page faults pursued by traditional page replacement policies. This is because a write operation in PCM is slow and PCM has limited write endurances. Specifically, the proposed policy focuses on the reduction of page faults when the memory load of the system is high, but it aims at reducing write traffic to storage when free memory space is sufficient. Simulation experiments with various memory reference traces show that the proposed policy reduces write traffic to PCM without performance degradations.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.127-135
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• 2009

Most traffic information that drivers receive while driving are stored in their short-term memory and disappear within a few seconds. Contemporary modeling approaches using a dummy variable can't fully explain this phenomenon. As such, this study proposes to use utility functions of real-time in-vehicle traffic safety information (IVTSI), analyzing its safety impacts based on empirical data from an on-site driving experiment at signalized intersection approach with a limited visibility. For this, a driving stability evaluation model is developed based on driver's driving speed choice, applying an ordered probit model. To estimate the specified utility functions, the model simultaneously accounts for various factors, such as traffic operation, geometry, road environment, and driver's characteristics. The results show three significant facts. First, a normal density function (exponential function) is appropriate to explain the utility of IVTSI proposed under study over time. Second, the IVTSI remains in driver's short-term memory for up to nearly 22 second after provision, decreasing over time. Three, IVTSI provision appears more important than the geometry factor but less than the traffic operation factor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4246-4267
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• 2020

As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.73-80
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• 2021

In this paper, we propose a packet processing method capable of handling large-capacity traffic over 20Gbps using multi-core and huge page memory approaches. As ICT technology advances, the global average monthly traffic is expected to reach 396 exabytes by 2022. With the increase in network traffic, cyber threats are also increasing, increasing the importance of traffic analysis. Traffic analyzed as an existing high-cost foreign product simply stores statistical data and visually shows it. Network administrators introduce and analyze many traffic analysis systems to analyze traffic in various sections, but they cannot check the aggregated traffic of the entire network. In addition, since most of the existing equipment is of the 10Gbps class, it cannot handle the increasing traffic every year at a fast speed. In this paper, as a method of processing large-capacity traffic over 20Gbps, the process of processing raw packets without copying from single-core and basic SMA memory approaches to high-performance packet reception, packet detection, and statistics using multi-core and NUMA memory approaches suggest When using the proposed method, it was confirmed that more than 50% of the traffic was processed compared to the existing equipment.

• ETRI Journal
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• v.41 no.5
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• pp.560-573
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• 2019

Two supervised learning algorithms, a basic neural network and a long short-term memory recurrent neural network, are applied to traffic including DDoS attacks. The joint effects of preprocessing methods and hyperparameters for machine learning on performance are investigated. Values representing attack characteristics are extracted from datasets and preprocessed by two methods. Binary classification and two optimizers are used. Some hyperparameters are obtained exhaustively for fast and accurate detection, while others are fixed with constants to account for performance and data characteristics. An experiment is performed via TensorFlow on three traffic datasets. Three scenarios are considered to investigate the effects of learning former traffic on sequential traffic analysis and the effects of learning one dataset on application to another dataset, and determine whether the algorithms can be used for recent attack traffic. Experimental results show that the used preprocessing methods, neural network architectures and hyperparameters, and the optimizers are appropriate for DDoS attack detection. The obtained results provide a criterion for the detection accuracy of attacks.