• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.579-582
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• 2004

HomePNA(Home Phoneline Networking Alliance)는 가정에서 전화선을 이용하여 2대 이상의 통신기기들을 서로 공유할 수 있도록 하는 네트웍 솔루션으로, HomePNA2.0은 기존의 HomePNAl.0과 호환성을 유지하면서도 10Mbps로 전송 가능한 새로운 규격이다. 1999년에 규격이 발표되었으며, CSMA/CD를 기반으로 한 DFPQ (Distribute Fair Priority Queueing)방식의 충돌해결 방법을 사용하고 있다. 본 논문에서는 기존 DFPQ에 기반을 둔 새로운 알고리즘을 제안하고, 기존 DFPQ와 비교 및 분석한다.

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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• pp.953-961
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• 2000

In this paper, we propose the algorithm which solve synchronization problem happened migrating service objects. Load imbalance is occurred due to allocate different amount of load to be proposed to each node in distribute system. To solve this problem, the service objects have to be migrated from heavily loaded node to lightly loaded node. And in such a process, the synchronization problem may occur when client request a service object migrated incompletely. Therefore, we describe the environment of executing service objects, the importer/exporter/trader model compatible with migration of service objects, and appropriate migration algorithm, Finally, we analyze the conditions of problems, and propose the solution of each situations. Also, the performance advantages of using proposed algorithms are quantified through a simulation study.

• Journal of Information Processing Systems
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• v.10 no.2
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• pp.240-255
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• 2014

Cloud computing has increasingly been drawing attention these days. Each big company in IT hurries to get a chunk of meat that promises to be a whopping market in the future. At the same time, information is always associated with security and risk problems. Nowadays, the handling of these risks is no longer just a technology problem, with a good deal of literature focusing on risk or security management and framework in the information system. In this paper, we find the specific business meaning of the BMIS model and try to apply and leverage this model to cloud risk. Through a previous study, we select and determine the causal risk factors in cloud service, which are also known as CSFs (Critical Success Factors) in information management. Subsequently, we distribute all selected CSFs into the BMIS model by mapping with ten principles in cloud risk. Finally, by using the leverage points, we try to leverage the model factors and aim to make a resource-optimized, dynamic, general risk control business model for cloud service providers.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.297-305
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• 2021

Large amount of data is being generated in gaming servers due to the increase in the number of users and the variety of game services being provided. In particular, load balancing schemes for gaming servers are crucial consideration. The existing literature proposes algorithms that distribute loads in servers by mostly concentrating on load balancing and cooperative offloading. However, many proposed schemes impose heavy restrictions and assumptions, and such a limited service classification method is not enough to satisfy the wide range of service requirements. We propose a load balancing agent that combines the dynamic allocation programming method, a type of greedy algorithm, and proximal policy optimization, a reinforcement learning. Also, we compare performances of our proposed scheme and those of a scheme from previous literature, ProGreGA, by running a simulation.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.109-110
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• 2009

최근 IT산업의 화두인 클라우드 컴퓨팅에서, HDFS는 널리 사용되고 있는 분산 파일 시스템이다. HDFS는 분산된 데이터의 저장과 검색의 장점이 있는 반면, 대용량 파일처리를 목적으로 설계되었기 때문에 실시간 파일처리와 저용량 데이터 처리에 비효율적이다. 본 논문에서는 이러한 문제를 해결하기 위해 HDFS의 파일 처리 과정을 개선하여 저용량 파일 처리를 향상시키는 방법을 제안한다. 본 기법은 데이터 블록에 저용량 파일들을 병합함으로써 데이터 처리의 효율성을 높이는 결과를 보였다.

• Plant Resources
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• v.6 no.3
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• pp.170-174
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• 2003

The important component for medical effect in ginseng is ginsenoside. Korea Ginseng & Tobacco Research Institute contains approximately 200 lines produced by inbred selection. It is assumed that ginseng lines containing high level of ginsenoside should be included in those lines. Besides, new breeding methods such as cell line selection in vitro and hairy root were recently developed. Therefore, this study was carried out to detect genes related to ginsenoside, and to use it for selection marker to select and distribute lines containing high level of ginsenoside. DNA was extracted from both ginseng roots and hairy roots, and the difference between the line containing high ginsenoside(KG101) and normal ginsenoside(KG103) were analysed. As a result, 28 out of 36 primers showed bands, and many primers showed band difference between ginseng lines. It is considered that the bands should be analysed using DNA sequence comparison to check if those are related to ginsenoside. In case of hairy roots of ginseng, almost no differences were found between two lines.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.163-173
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• 2016

Recently, massive amounts of stream data have been studied for distributed processing. In this paper, we propose an incremental stream data processing method based on in-memory in big data environments. The proposed method stores input data in a temporary queue and compare them with data in a master node. If the data is in the master node, the proposed method reuses the previous processing results located in the node chosen by the master node. If there are no previous results of data in the node, the proposed method processes the data and stores the result in a separate node. We also propose a job scheduling technique considering the load and performance of a node. In order to show the superiority of the proposed method, we compare it with the existing method in terms of query processing time. Our experimental results show that our method outperforms the existing method in terms of query processing time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3B
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• pp.105-113
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• 2005

In this paper, through the implementation of the application service, we evaluated the feasibility and availability of the staged application service platform, which is based on the sharing of the PVM cluster. Application service platform provides three managers for dividing the request processing steps into two stages, such as a request processing stage and a service providing stage. Three managers and its relation to the divided stages are as follows, service manager and load manager to distribute the request in front-end server for a request processing stage, job manager in clustered(back-end) servers for a service providing stage. The experiment shows that the staged application service platform provides more stable and scalable characteristics and better performance improvement on the dynamic load changes than the single server system. And also it shows that real application service system can be implemented easily without modification of the proposed service platform.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.5
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• pp.1142-1148
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• 2005

We proposes an algorithm of measuring computation loads to distribute workload of clients. The key of the algorithm is to transfer a suitable amount of processing demand from senders to receivers in relocating intrinsically. This amount is determined dynamically during sender-receiver negotiations. Factors considered when this amount is determined include processing speeds of different nodes, the current load state of both sender and receiver, and the processing demands of tasks eligible for relocation. We also propose a load state measurement scheme which is designed particularly for heterogeneous systems. Based on this analysis, we design new algorithm for supporting heterogeneous distributed web server system, and compare their performance against other existing algorithms. We show that the new algorithm improve the performance of CPU utilization and response time.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.477-482
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• 2016

As being the advanced analytics indispensable on big data for agile decision-making and tactical planning in enterprises, distributed processing platforms, such as Hadoop and Spark which distribute and handle the large volume of data on multiple nodes, receive great attention in the field. In Spark platform stack, Spark SQL unveiled recently to make Spark able to support distributed processing framework based on SQL. However, Spark SQL cannot effectively handle advanced analytics that involves machine learning and graph processing in terms of iterative tasks and task allocations. Motivated by these issues, this paper proposes the design of SQL-based big data optimal processing engine and processing framework to support advanced analytics in Spark environments. Big data optimal processing engines copes with complex SQL queries that involves multiple parameters and join, aggregation and sorting operations in distributed/parallel manner and the proposing framework optimizes machine learning process in terms of relational operations.