• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.32-32
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• 1994

In this example, a message passing based multicomputer system with general interdonnedtion network is considered. After multicomputer systems are developed with morm-hole routing network, topologies of interconecting network are not major considertion for process management and resource sharing. Tehre is an independeent operating system kernel oneach node. It communicates with other kernels using message passingmechanism. Based on this architecture, the problem is how mech does performance degradation will occur in the case of processor sharing on multicomputer systems. Processor sharing between application programs is veryimprotant decision on system performance. In almost cases, application programs running on massively parallel computer systems are not so much user-interactive. Thus, the main performance index is system throughput. Each application program has various communication patterns. and the sharing of processors causes serious performance degradation in hte worst case such that one processor is shared by two processes and another processes are waiting the messages from those processes. As a result, considering this problem is improtant since it gives the reason whether the system allows processor sharingor not. Input data has many parameters in this simulation . It contains the number of threads per task , communication patterns between threads, data generation and also defects in random inupt data. Many parallel aplication programs has its specific communication patterns, and there are computation and communication phases. Therefore, this phase informatin cannot be obtained random input data. If we get trace data from some real applications. we can simulate the problem more realistic . On the other hand, simualtion results will be waseteful unless sufficient trace data with varisous communication patterns is gathered. In this project , random input data are used for simulation . Only controllable data are the number of threads of each task and mapping strategy. First, each task runs independently. After that , each task shres one and more processors with other tasks. As more processors are shared , there will be performance degradation . Form this degradation rate , we can know the overhead of processor sharing . Process scheduling policy can affects the results of simulation . For process scheduling, priority queue and FIFO queue are implemented to support round-robin scheduling and priority scheduling.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.5
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• pp.903-909
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• 2010

ADAS is the air defense control system performing air surveillance and identification of ROK and near air. This system is self-developed by Air Force, currently operated successfully as the alternative system of MCRC. ADAS processes converting and combining transferred the real time radar data detected by radars. additionally, it displays significant radar data as producing in tracks. Then, it uses the message queue for IPC(Inter Process Communication). the various tactical data processed in the server is ultimately send to the network management process through the message queue for transmitting to the weapon director console. the weapon director receives this transmitted tactical data through the console to execute air defense operations. However, there is a problem that data packet is delayed or lost since the weapon Director does not receive as the amount of tactical data from the server overflowed with air tracks and missions increased. This paper improved the algorism to display and transmit the various tactical data processed from ADAS server to numbers of the weapon director console in the real time without any delay or lost. Improved the algorism, established at exercise, the development server in the real operation network and the weapon director console, is proved by comparing the number of sending tactical data packets in the server and receiving packets in the weapon director.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.1C
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• pp.9-13
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• 2003

We consider a two-phase queueing system with Bernoulli feedback. Customers arrive at the system according to a Poison process and receive batch service in the first phase followed by individual services in the second phase. Each customer who completes the individual service returns to the tail of the second phase service queue with probability 1 -$\sigma$. This type of queueing problem cad be easily found in computer and telecommunication systems. By deriving a relationship between the generating functions for system size at various embedded epochs, we obtain the system size distribution. The exhaustive and gated cases for the batch service are considered.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.4
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• pp.15-22
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• 2011

U-Healthcare service is a real-time service using the vital signs which are continuously transferred from monitoring sensors attached to mobile patients under the wireless network environments. It should monitor the health condition of mobile patients everywhere at any time. In this paper, we have improved two features of the three layered mobile patient monitoring system with load balancing ability. First, the simulation process has been improved by allowing the number of related nodes to be changed. Secondly, we have modified S node to which queue is added to reduce the loss rate of collecting data from patients during the delay of S node process. And the data from the patient with high priority can be transferred to the server immediately through the filtering function. Furthermore, we have solved the problem of redundancy in sharing information among S nodes by differentiating process time to each S node. By performing a DEVS Java-based system simulation, we have verified the efficiency of this improved system.

• Journal of the Korea Society of Computer and Information
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• v.25 no.1
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• pp.21-27
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• 2020

In this paper, a hybrid scheduling algorithm is proposed for CMS(Combat Management System) to improve QoS(Quality of Service) based on DWDRR(Dynamic Weighted Deficit Round Robin) and priority-based scheduling method. The main proposed scheme, DWDRR is method of packet transmission through giving weight by traffic of queue and priority. To demonstrate an usefulness of proposed algorithm through simulation, efficiency in special section of the proposed algorithm is proved. Therefore, We propose hybrid algorithm between existing algorithm and proposed algorithm. Also, to prevent frequent scheme conversion, a hysteresis method is applied. The proposed algorithm shows lower packet loss rate and delay in the same traffic than existing algorithm.

• International Journal of Internet, Broadcasting and Communication
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• v.8 no.1
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• pp.33-41
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• 2016

Big data management is becoming an increasingly important issue in both industry and academia of information science community today. One of the important categories of big data generated from software systems is log data. Log data is generally used for better services in various service providers and can also be used to improve system reliability. In this paper, we propose a novel big data management architecture specialized for log data. The proposed architecture provides a scalable log management system that consists of client and server side modules for efficient handling of log data. To support large and simultaneous log data from multiple clients, we adopt the Hadoop infrastructure in the server-side file system for storing and managing log data efficiently. We implement the proposed architecture to support various client environments and validate the efficiency through measurement studies. The results show that the proposed architecture performs better than the existing logging architecture by 42.8% on average. All components of the proposed architecture are implemented based on open source software and the developed prototypes are now publicly available.

• Journal of Applied Reliability
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• v.16 no.4
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• pp.313-322
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• 2016

Purpose: We introduce ways to employ Markov chain model to evaluate the effect of preventive maintenance process. While the preventive maintenance process decreases the failure rate of each subsystems, it increases the downtime of the system because the system can not work during the maintenance process. The goal of this paper is to introduce ways to analyze this trade-off. Methods: Markov chain models are employed. We derive the availability of the system consisting of N repairable subsystems by the methods under various maintenance policies. Results: To validate our methods, we apply our models to the real maintenance data reports of military truck. The error between the model and the data was about 1%. Conclusion: The models developed in this paper fit real data well. These techniques can be applied to calculate the availability under various preventive maintenance policies.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Asia pacific journal of information systems
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• v.26 no.3
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• pp.427-448
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• 2016

The stochastic phenomena of traffic network condition, such as traffic speed and density, are affected not only by exogenous traffic control but also by endogenous changes in service time during congestion. In this paper, we propose a mixed M/G/1 queuing model by introducing a condition-varying parameter of traffic congestion to reflect structural changes in the traffic network. We also develop congestion indices to evaluate network efficiency in terms of traffic flow and economic cost in traffic operating system using structure-changing queuing model, and perform scenario analysis according to various traffic network improvement policies. Empirical analysis using Korean highway traffic operating system shows that our suggested model better captures structural changes in the traffic queue. The scenario analysis also shows that occasional reversible lane operation during peak times can be more efficient and feasible than regular lane extension in Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4342-4366
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• 2016

Congestion control in Cluster Wireless Sensor Networks (CWSNs) has drawn widespread attention and research interests. The increasing number of nodes and scale of networks cause more complex congestion control and management. Active Queue Management (AQM) is one of the major congestion control approaches in CWSNs, and Random Early Detection (RED) algorithm is commonly used to achieve high utilization in AQM. However, traditional RED algorithm depends exclusively on source-side control, which is insufficient to maintain efficiency and state stability. Specifically, when congestion occurs, deficiency of feedback will hinder the instability of the system. In this paper, we adopt the Additive-Increase Multiplicative-Decrease (AIMD) adjustment scheme and propose an improved RED algorithm by using neighbor feedback and scheduling scheme. The congestion control model is presented, which is a linear system with a non-linear feedback, and modeled by Lur'e type system. In the context of delayed Lur'e dynamical network, we adopt the concept of cluster synchronization and show that the congestion controlled system is able to achieve cluster synchronization. Sufficient conditions are derived by applying Lyapunov-Krasovskii functionals. Numerical examples are investigated to validate the effectiveness of the congestion control algorithm and the stability of the network.