• The KIPS Transactions:PartA
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• v.11A no.4
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• pp.243-250
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• 2004

Monitoring is used to see if a real-time system provides a service on time. Generally, monitoring for real-time focuses on investigating the current status of a real-time system. To support a stable performance of a real-time system, it should have not only a function to see the current status of real-time process but also a function to predict executions of real-time processes, however. The legacy prediction model has some limitation to apply it to a real-time monitoring. First, it performs a static prediction after a real-time process finished. Second, it needs a statistical pre-analysis before a prediction. Third, transition probability and data about clustering is not based on the current data. We propose the execution prediction model based on learning algorithm to solve these problems and apply it to real-time monitoring. This model gets rid of unnecessary pre-processing and supports a precise prediction based on current data. In addition, this supports multi-level prediction by a trend analysis of past execution data. Most of all, We designed the model to support dynamic prediction which is performed within a real-time process' execution. The results from some experiments show that the judgment accuracy is greater than 80% if the size of a training set is set to over 10, and, in the case of the multi-level prediction, that the prediction difference of the multi-level prediction is minimized if the number of execution is bigger than the size of a training set. The execution prediction model proposed in this model has some limitation that the model used the most simplest learning algorithm and that it didn't consider the multi-regional space model managing CPU, memory and I/O data. The execution prediction model based on a learning algorithm proposed in this paper is used in some areas related to real-time monitoring and control.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.377-386
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• 2016

As the number of malware has been increased, it is necessary to analyze malware rapidly against cyber attack. Additionally, Dynamic malware analysis has been widely studied to overcome the limitation of static analysis such as packing and obfuscation, but still has a problem of exploring multiple execution path. Previous works for exploring multiple execution path have several problems that it requires much time to analyze and resource for preparing analysis environment. In this paper, we proposed efficient exploring approach for multiple execution path in a single analysis environment by pipelining processes and showed the improvement of speed by 29% in 2-core and 70% in 4-core through experiment.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.5
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• pp.271-281
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• 2007

Real-time support of embedded OS is not optional, but essential in contemporary embedded systems. In order to achieve these system#s real-time property, it is crucial that schedulability analysis for tasks having its property have been accomplished before system execution. Acquiring Worst-Case Execution Time(WCET) of task is a core part of schedulability analysis. Because traditional WCET tools analyze only its estimation of application task(i.e. program), it is not considered that application tasks are affected by scheduling primitives(e.g. scheduler, interrupt service routine, etc.) of OS when it schedules them. In this paper, we design and implement WCET analysis tool which deliberates on scheduling primitives of system using embedded Linux widely used in embedded OSes. This tool can estimate either WCET of normal application programs or corresponding primitives which have an influence on schduling property in embedded Linux kernel. Therefore, precision of estimation about schedulability analysis is improved. We develop this tool as Eclipse#s plug-in to work properly in any platform and support convenient interface or functionality for user.

• The Journal of Bigdata
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• v.3 no.1
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• pp.47-54
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• 2018

Previous studies of business process analysis have analyzed various factors such as task, customer service, operator convenience, and execution time prediction. To accurately analyze these factors, it is effective to utilize actual historical data recorded in information systems. Process mining is a technique for analyzing various elements of a business process from event log data. In this case study, process mining was applied to the transaction data of a purchase agency to analyze the business process of their procurement process, the execution time, and the operators.

• Journal of Computing Science and Engineering
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• v.3 no.4
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• pp.195-215
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• 2009

Worst-case execution time (WCET) analysis is critical for hard real-time systems to ensure that different tasks can meet their respective deadlines. While significant progress has been made for WCET analysis of instruction caches, the data cache timing analysis, especially for set-associative data caches, is rather limited. This paper proposes an approach to safely and tightly bounding data cache performance by computing the worst-case stack distance of data cache accesses. Our approach can not only be applied to direct-mapped caches, but also be used for set-associative or even fully-associative caches without increasing the complexity of analysis. Moreover, the proposed approach can statically categorize worst-case data cache misses into cold, conflict, and capacity misses, which can provide useful insights for designers to enhance the worst-case data cache performance. Our evaluation shows that the proposed data cache timing analysis technique can safely and accurately estimate the worst-case data cache performance, and the overestimation as compared to the observed worst-case data cache misses is within 1% on average.

• Convergence Security Journal
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• v.19 no.3
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• pp.37-41
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• 2019

Recently, due to the appearance of various types of malware, the existing static analysis exposes many limitations. Static analysis means analyzing the structure of a code or program with source code or object code without actually executing the (malicious) code. On the other hand, dynamic analysis in the field of information security generally refers to a form that directly executes and analyzes (malware) code, and compares and examines and analyzes the state before and after execution of (malware) code to grasp the execution flow of the program. However, dynamic analysis required analyzing huge amounts of data and logs, and it was difficult to actually store all execution flows. In this paper, we propose and implement a preprocessor architecture of a system that performs malware detection and real-time multi-dynamic analysis based on 2nd generation PT in Windows environment (Windows 10 R5 and above).

• The KIPS Transactions:PartC
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• v.11C no.5
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• pp.697-702
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• 2004

As for the digital content, a reproduction is easy and manuscript is identical with original copy. Because of these characteristics, there are difficulties on prevention of an illegal reproduction and an illegal currency. In recent days various digital content service systems based on a web are commercialized. An appropriate copyright protection technology is required so that these systems develop as a profit model. Generally we use encrypted digital content transmission method for the copyright protection on a web base system. At the time of this, it is increased sire of encrypted digital content. As for this, it be increased time required on an execution process. Therefore, a design of the system that considered a execution time and a security is required. In this study, we designed the digital content transmission system that considered execution time and a security through a partial encryption based on a digital content copyright management technique. Also we evaluated performance of a proposed system through analysis.

• Journal of the Korea Society for Simulation
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• v.20 no.1
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• pp.87-96
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• 2011

DEVS(Discrete Event Systems Specification) is a set theoretic formalism developed for specifying discrete event system. For execution of DEVS, we need an execution environment, which consists of simulation engine and models interpreted by the simulation engine. Common existing environments use hierarchical scheduling algorithm for DEVS execution. This hierarchical scheduling is a proper algorithm for DEVS execution because of hierarchical and modular characteristics. But this algorithm has overheads owing to message passing and time management. To overcome these overheads, we apply event-oriented simulation to DEVS execution and we remove hierarchical overheads. In eventoriented simulation, the scheduling of model execution is performed by events and event list. We propose three event-oriented execution environments for DEVS and experiment about the performance of our proposed environments in comparison with the existing execution environment using the hierarchical scheduling. The experimental results show our environments works better than existing environment using the hierarchical scheduling.

• The KIPS Transactions:PartA
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• v.13A no.6 s.103
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• pp.533-540
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• 2006

This paper describes the design and implementation of a grid system META (Metacomputing Environment using Test-run of Application) which facilitates the execution of a CFD (Computational Fluid Dynamics) analysis program on distributed environment. The grid system META allows the CFD program developers can access the computing resources distributed over the network just like one computer system. The research issues involved in the grid computing include fault-tolerance, computing resource selection, and user-interface design. In this paper, we exploits an automatic resource selection scheme for executing the parallel SPMD (Single Program Multiple Data) application written in MPI (Message Passing Interface). The proposed resource selection scheme is informed from the network latency time and the elapsed time of the kernel loop attained from test-run. The network latency time highly influences the executional performance when a parallel program is distributed and executed over several systems. The elapsed time of the kernel loop can be used as an estimator of the whole execution time of the CFD Program due to a common characteristic of CFD programs. The kernel loop consumes over 90% of the whole execution time of a CFD program.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.139-143
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• 2018

Arduino, based on open source hardware, is used in many IoT devices, and IoT devices require multitasking for various inputs and outputs. Among the several methods used for multitasking based on Arduino, we compare three methods: Timing Call by using millis(), Simple Timer library method, and Timer library method. In order to measure the execution error caused by measurement and time delay of each method, two situations are created and analyzed. In the first case, 10 random tasks of a certain size are generated to measure the time delay of each method. In the second situation, 10 random tasks of a certain size are generated to compare execution errors caused by the time lag of the Timer library. In the first case, the millis() timing call method and the Simple Timer library method have a similar time delay and the Timer library method has more time delay. In the second situation, an execution error occurred in which small-size tasks were not called back at the correct timing due to the time delay.