Vol. 45, No. 6, Jun. 2018
Digital Library


Boosting the Forwarding Performance of Virtual Switches through Kernel-level Memory Optimization

Heungsik Choi, Kyoungwoon Lee, Chuck Yoo

http://doi.org/10.5626/JOK.2018.45.6.511

A virtual switch enables network resources to be utilized by a wide range of virtual machines or containers. Many types of virtual switches have been developed to offer a variety of functions. However, due to the inefficient processing of existing virtual switches and the Linux networking stack, current high bandwidth requirements cannot be met. To solve this problem, various studies have been carried out to propose a method using a unique networking stack in a user-level rather than an existing kernel. However, various problems still exist such as reimplementation overhead, relatively low security, excessive memory usage, etc. This paper proposes kernel-level optimization techniques to improve network processing of the kernel networking stack as well as to overcome the limitations of existing techniques.

Streaming Compression Scheme for Reducing Network Resource Usage in Hadoop System

Seung Joon Noh, Young Ik Eom

http://doi.org/10.5626/JOK.2018.45.6.516

Recently, the Hadoop system has become one of the most popular large-scale distributed systems used in enterprises, and the amount of data on the system has been increasing continually. As the amount of data in the Hadoop system is increased, the scale of Hadoop clusters is also growing. Resources in a node, such as processor, memory, and storage, are isolated from other nodes, and hence, even though resource usage is increased by data processing requests from clients, it doesn’t affect the performance of other nodes. However, all the nodes in a Hadoop cluster are connected to the network resource, a shared resource in the Hadoop cluster, and so, if some nodes dominate the network resource, other nodes would experience less network resources, which could cause overall performance degradation in the Hadoop system. In this paper, we propose a streaming compression scheme that can decrease the network traffic generated by write operations in the system. We also evaluate the performance of our streaming compression scheme and analyze the overhead of the proposed scheme. Our experimental results with a real-world workload show that our proposed scheme decreases the network traffic in a Hadoop cluster by 56% over the existing HDFS systems.

A Simplified Test Maturity Model (sTMM) for Small and Midsize Test Organization

Bo Kyung Park, Woo Sung Jang, Ki Du Kim, R. Young Chul Kim

http://doi.org/10.5626/JOK.2018.45.6.522

Software development and management system has been needed to systematically. Domestic companies in Korea want to improve their software quality with software certifications such as capability maturity model integration (CMMI) and test maturity model integration (TMMi). But current certification models must perform many activities on their process for software organizations. Even test organization also takes a lot of time, manpower and cost to prepare TMMi. For this reason, there is increasing a demand to make a slim certification model that reflects our domestic software industry environment. TTA in 2015/2016 asks us to develop a new refined model for a slim test organization of Korea’s software industry environment. In this paper, we suggest a light-weighted TMM for a slim test organization based on the original TMM. With this model, TTA can provide a guideline for improving the test maturity level through assessing two domestic test organizations. As a result, we expect to improve software quality with this model focused on a test organization.

A Recognition of Violence Using Mobile Sensor Fusion in Intelligent Video Surveillance Systems

HyunIn Cha, KwangHo Song, Yoo-Sung Kim

http://doi.org/10.5626/JOK.2018.45.6.533

In this paper, we propose a violence recognition model by reflecting features extracted by concurrent and continuous action in intelligent CCTV through detecting group ROI(Region of Interest) from image. And then, proposed model uses extracted motion information obtained by using Dense Optical Flow algorithm in ROI and fusing of the acceleration and angular velocity information obtained from the inertial measurement unit of the mobile device possessed by actor. Experiments were performed to evaluate the reduction of the computation time of the proposed model and improvement of the performance degradation due to the occlusion. Result of experiment, the execution time was about 51 times faster and the accuracy of recognition of violence was improved by 11% compared to previous research methods. Therefore, the proposed model can overcome the problem of real-time failure due to excessive computation and can solve the problem of invisibility due to occlusion by actor in the image in recognition of violence.

Quality Estimation of English-Korean Machine Translation using Neural Network based Predictor-Estimator Model

Hyun Kim, Jaehun Shin, Wonkee Lee, Seungwoo Cho, Jong-Hyeok Lee

http://doi.org/10.5626/JOK.2018.45.6.545

Quality Estimation (QE) for machine translation is an automatic method for estimating the quality of machine translation output without the need to use reference translations. QE has recently grown in importance in the field of machine translation (MT). Recent studies on QE have mainly focused on European languages, whereas fewer studies have been carried out on QE for Korean. In this paper, we create a new QE dataset for English to Korean translations and apply a neural network based Predictor-Estimator model to a QE task of English-Korean. Creating a QE dataset requires manual post-edited translations for MT outputs. Because Korean is a free word order language and allows various writing styles for translation, we provide guidance for creating manual post-edited Korean translations for English-Korean QE data. Also, we alleviate the imbalanced data problem of QE data. Finally, this paper reports on our experimental results of the QE task of English-Korean by using the Predictor-Estimator model trained from the created English-Korean QE data.

An Automatic Segmentation Algorithm for Colonic Glandular Lesions

Migyung Cho, Hyekyung Lee, Hwan Gue Cho

http://doi.org/10.5626/JOK.2018.45.6.554

Adenoma and adenocarcinoma of the colon are one of the most common tumors, and diagnoses are based mainly on the structural appearances and changes in cell morphology of the glandular structures. Each diagnosis is based on subjectivity and objectivity of each pathologist, and many studies are under way to extract meaningful features from the glandular structure for better objective results and reproducibility. Passive segmentation of glandular cells to extract structural features is a labor-intensive task performed over many hours and with some difficulties. These problems require an automated approach to quantify the shapes of glandular cells. In this paper, we have developed an algorithm for segmentation of glandular cells to quantify their shapes in the benign and initial stages of deformation signifying the onset of disease. The algorithm sequentially applies adaptive thresholds obtained by k-means clustering and obtains binary images by thresholding and filtering methods. We extract boundary information from binary images and combine several boundary information, and then we search for glandular cells, both in the outward direction and inward direction from the boundary information. Applying the proposed algorithm to clinical images showed more than 95% accuracy. In addition, it is a very practical algorithm because it is much faster than the level-set based algorithms.

Activity Prediction from Sensor Data using Convolutional Neural Networks and an Efficient Compression Method

Woojeong Jin, Dongjin Choi, Youngjin Kim, U Kang

http://doi.org/10.5626/JOK.2018.45.6.564

The identification of the number of occupants and their activities using the IoT system in a building is an important task to improve the power efficiency and reduce the cost of using smart cooling/heating systems. In the actual building management system, it is possible to use equipment such as a camera to understand the current situation in the room, and to directly determine the number of occupants and their types of behavior. However, identifying the number of people and behavior types in this way is inefficient and requires a large amount of storage space for data. In this study, indoor sensor data were collected using an infrared Grid-Eye sensor and noise sensor. Based on this data, we also propose a deep learning model that captures the number of participants and behavior patterns and a deep learning model that considers the temporal characteristics of data. The proposed model identifies the number of people with an accuracy of about 95.3% and human activities with an accuracy of 90.9%. We also propose a method to reduce the storage space while minimizing the loss of accuracy using truncated SVD.

Regularizing Korean Conversational Model by Applying Denoising Mechanism

Tae-Hyeong Kim, Yunseok Noh, Seong-Bae Park, Se-Yeong Park

http://doi.org/10.5626/JOK.2018.45.6.572

A conversation system is a system that responds appropriately to input utterances. Recently, the sequence-to-sequence framework has been widely used as a conversation-learning model. However, the conversation model learned in such a way often generates a safe and dull response that does not provide appropriate information or sophisticated meaning. In addition, this model is also useless for input utterances appearing in various forms, such as with changed ending words or changed word order. To solve these problems, we propose a denoising response generation model applying a denoising mechanism. By injecting noise into original input, the proposed method creates a model that will stochastically experience new input made up of items that were not included in the original data during the training process. This data augmentation effect regularizes the model and allows the realization of a robust model. We evaluate our model using 90k input utterances-responses from Korean conversation pair data. The proposed model achieves better results compared to a baseline model on both ROUGE F1 score and qualitative evaluations by human annotators.

A Splitting Technique of Hardware Transactional Memory in Multicore In-Memory Databases

Munhwan Kang, Hyeongjin Kim, Hyeonkuk Ma, Jaewoo Chang

http://doi.org/10.5626/JOK.2018.45.6.582

Transactional Memory has greatly changed concurrency control paradigm by replacing locks, the conventional parallel programming mechanism. Especially, HTM(Hardware Transactional Memory) is the most promising scheme that is supported by hardware. However, the existing HTM techniques have a problem that they cannot overcome the resource limitations of HTM. To solve the problem, we propose a HTM-based transaction splitting technique to support large-sized transaction processing in multicore in-memory databases. First, the proposed technique can split a transaction into nested partition blocks when the transaction fails by resource limitation. Second, the proposed technique makes use of our adaptive split algorithm that computes the optimal size of partition blocks, according to the characteristic of a workload. Finally, through our experimental performance analysis using STAMP benchmark, the proposed technique shows about 70% better performance than the existing transaction splitting technique, i.e., Part-HTM.

A Competitive Multipath Routing Protocol for delay-sensitive applications in Wireless Sensor Networks

Kwansoo Jung

http://doi.org/10.5626/JOK.2018.45.6.589

The key performance of various data delivery applications studied in wireless sensor networks is the timeliness and reliability of transmission. Both performances may be required simultaneously depending on the type of application and information. However, because of the limited nature of resources in wireless sensor networks, these requirements are not easy to meet. One way to overcome this problem is by multipath routing method. Traditional multi-path routing protocols exploit a method of generating additional independent paths or branching an existing path to satisfy the required performance. These methods can waste too many network resources in an irregular network environment. In order to solve this problem, this paper proposes an energy-efficient multipath routing method that can satisfy the requirement of emergency application by using the competition and cooperation between paths in irregular wireless sensor networks. Finally, this paper compares and analyzes the routing performance of the proposed method by means of simulation.

Fog-Server Placement Technique Based on Network Edge Area Traffic for a Fog-Computing Environment

Min-Sik Son, Sang-Hwa Chung, Won-Suk Kim

http://doi.org/10.5626/JOK.2018.45.6.598

Cloud computing is now widely used. However, cloud computing alone cannot adequately respond to the traffic patterns generated by various objects in the new network environment of the internet of things (IoT). Fog computing is one method for overcoming and solving the problems of cloud computing. A fog-server placement method is needed, including a mechanism for determining the location for a fog-server that can provide services. However, most studies only consider the fog device’s computing resources, and the locations of the client and the data sources are not considered; therefore, the network situation becomes worse after the fog-server placement. In this paper, we propose a technique for fog-server placement that considers traffic generation in relation to the locations of the clients and data sources. In the experiment, clients and data sources are concentrated or distributed in the network topology, and their corresponding network-traffic patterns are considered. Experimental results show that, in terms of reducing core network traffic and memory usage, placing the fog server according to the proposed network traffic conditions is more efficient than placing fog servers in all of the fog devices.


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