• Journal of KIISE:Computer Systems and Theory
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• v.34 no.9
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• pp.435-444
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• 2007

Caching web pages is an important part of web infrastructures. The effects of caching service are even more pronounced for wireless infrastructures due to their limited bandwidth. Medium to large-scale infrastructures deploy a cluster of servers to solve the scalability problem and hot spot problem inherent in caching. In this paper we present scheduling scheme based on cache utilization in a wireless internet proxy server cluster environment. The proposed method uses cache utilization for distributing evenly client requests to a cluster of cache servers and solving hot spot problem. We have implemented our approach and performed various experiments using publicly available traces. Experimental results on a cluster of 16 cache servers demonstrate that the proposed hashing method gives 45% to 114% Performance improvement over other widely used methods while addressing the hot spot problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1963-1978
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• 2015

With the advancement of mobile web environments, identification and analysis of the user behavior play a significant role and remains a challenging task to implement with variations observed in the model. This paper presents an efficient method for mining optimized user behavior prediction model using genetic algorithm on mobile web structure. The framework of optimized user behavior prediction model integrates the temporary and permanent register information and is stored immediately in the form of integrated logs which have higher precision and minimize the time for determining user behavior. Then by applying the temporal characteristics, suitable time interval table is obtained by segmenting the logs. The suitable time interval table that split the huge data logs is obtained using genetic algorithm. Existing cluster based temporal mobile sequential arrangement provide efficiency without bringing down the accuracy but compromise precision during the prediction of user behavior. To efficiently discover the mobile users' behavior, prediction model is associated with region and requested services, a method called optimized user behavior Prediction Model using Genetic Algorithm (PM-GA) on mobile web structure is introduced. This paper also provides a technique called MAA during the increase in the number of models related to the region and requested services are observed. Based on our analysis, we content that PM-GA provides improved performance in terms of precision, number of mobile models generated, execution time and increasing the prediction accuracy. Experiments are conducted with different parameter on real dataset in mobile web environment. Analytical and empirical result offers an efficient and effective mining and prediction of user behavior prediction model on mobile web structure.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.892-898
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• 2007

This study aims to implement an efficient process management system for small and medium sized(local) construction companies and a performance management system for the Korean construction industry. The process management system by Lean Construction is Web 2.0 platform-based and creates clusters with numerous general contractors and sub-contractors, which will enable mutually organic process management. Plus, this system will enable them to compare project performance management by analyzing it during or after a project by collecting and accumulating lots of data occurring in pursuit of a project. These performance management cases will be of help in process planning during similar upcoming projects. This study is expected to somewhat reduce the burden of implementing a complicated process management protocol and system that Korean small and medium sized (local) construction companies experience with their web-based process management, and is supposed to realize accurate performance management with highly reliable data which are significantly accumulated within the database.

• The KIPS Transactions:PartA
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• v.11A no.5
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• pp.355-364
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• 2004

These days, various clustering technologies have been adopted to construct web sites. High performance hardware switches have good performance, but have disadvantage of high cost for constructing small and middle-sized web sites. Now a days, many sites have been constructed with the LVS (Linux Virtual Server), which is free of charge and has good performance. Having a centralized load balancing with one front-end, the LVS causes a bottleneck when it receives all at once. In the paper, we suggest a way to remove the LVS bottleneck by providing multiple front-ends. In this architecture, all of cluster nodes act as both a front-end and a back-end. When the load of a node receiving requests is not large enough, the node responds to the client directly. When the load of a node is large enough, the node send the request to a node which is selected by a scheduling algorithm. The scheduling algorithm is discussed to balance loads between servers. While single front-end cluster raises the throughput curvedly, the multiple front-end cluster raises the throughput linearly.

• Journal of KIISE
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• v.42 no.3
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• pp.307-319
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• 2015

Current ontology studies use the Hadoop distributed storage framework to perform map-reduce algorithm-based reasoning for scalable ontologies. In this paper, however, we propose a novel approach for scalable Web Ontology Language (OWL) Horst Lite ontology reasoning, based on distributed cluster memories. Rule-based reasoning, which is frequently used for scalable ontologies, iteratively executes triple-format ontology rules, until the inferred data no longer exists. Therefore, when the scalable ontology reasoning is performed on computer hard drives, the ontology reasoner suffers from performance limitations. In order to overcome this drawback, we propose an approach that loads the ontologies into distributed cluster memories, using Spark (a memory-based distributed computing framework), which executes the ontology reasoning. In order to implement an appropriate OWL Horst Lite ontology reasoning system on Spark, our method divides the scalable ontologies into blocks, loads each block into the cluster nodes, and subsequently handles the data in the distributed memories. We used the Lehigh University Benchmark, which is used to evaluate ontology inference and search speed, to experimentally evaluate the methods suggested in this paper, which we applied to LUBM8000 (1.1 billion triples, 155 gigabytes). When compared with WebPIE, a representative mapreduce algorithm-based scalable ontology reasoner, the proposed approach showed a throughput improvement of 320% (62k/s) over WebPIE (19k/s).

• Journal of Internet Computing and Services
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• v.7 no.3
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• pp.155-167
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• 2006

It is desirable if users surfing on the Internet could find Web pages related to their interests as closely as possible. Toward this ends, this paper presents a topic specific Web crawler computing the degree of relevance. collecting a cluster of pages given a specific topic, and refining the preliminary set of related web pages using term frequency/document frequency, entropy, and compiled rules. In the experiments, we tested our topic specific crawler in terms of the accuracy of its classification, crawling efficiency, and crawling consistency. First, the classification accuracy using the set of rules compiled by CN2 was the best, among those of C4.5 and back propagation learning algorithms. Second, we measured the classification efficiency to determine the best threshold value affecting the degree of relevance. In the third experiment, the consistency of our topic specific crawler was measured in terms of the number of the resulting URLs overlapped with different starting URLs. The experimental results imply that our topic specific crawler was fairly consistent, regardless of the starting URLs randomly chosen.

• Journal of the Korean Society for information Management
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• v.16 no.2
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• pp.7-26
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• 1999

This experimental study examines informetric analysis of World Wide Web based upon cocitation analysis of Web pages and features of Web resources in the field of communication studies. Cocitation analysis is basically performed to examine the intellectual structure of the communication studies in reflecting link count on the Web. The selected Web resources in the field are mapped in two dimensions based upon the similarities of cocitation frequency, correlation matrix, mutidimensional scale and cluster analysis. Cocitation analysis methods using organizational homepage, personal homepage, or Web index, to Web produced clustering of Web resources that had topical similarities. So far, although informetric analysis of Web resources is in the preliminary stage, it shows that Web can be a new tool for indicating the intellectual structure of a specific research field. In addition, this study analyzes characteristics of printing resources and Web resources, and differences of research methods in applying cocitation analysis.

• The KIPS Transactions:PartC
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• v.8C no.4
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• pp.379-388
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• 2001

인터넷을 이용한 전자상거래(e-commerce)가 확산되면서 중소규모의 다중 서버를 클러스터화하여 클라이언트의 요청에 대한 응답을 수행하는 네트웍 서비스에 대한 요구가 증대하고 있다. 이러한 웹서버 클러스터 환경의 요청을 위해서는 TCP정보나 요청되는 대상의 컨텐트(content) 정보를 이용하여 라우팅을 수행하는 라우터의 구현이 필수적이다. 이러한 TCP 라우팅이나 컨텐트 라우팅의 구현을 전적으로 소프트웨어에 의존하는 경우 전송속도의 저하가 발생할 우려가 있다. 이는 기존의 TCP 커넥션 설정과 이 TCP 커넥션을 이용한 데이터 전송이 라우팅을 고려하지 않은 가상의 점대점(point-to-point) 연결구조를 갖기 때문이다. 따라서, 본 논문에서는 보안세션 라우팅을 고속화시키기 위하여 TCP 커넥션 고속화를 기반으로 보안세션의 재사용성을 향상시키는 웹서버 클러스터 환경에 최적화된 디스패칭(dispatching) 기법을 제시하고자 한다.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.88-88
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• 2000

As more and more critical commercial applications move on the Internet, providing highly available servers becomes increasingly important. One of the advantages of a clustered system is that it has hardware and software redundancy. High availability can be provided by detecting node or daemon failure and reconfiguring the system appropriately so that the workload can be taken over bi the remaining nodes in the cluster. This paper presents how to provide the guaranteeing high availability of clustering web server. The load balancer becomes a single failure point of the whole system. In order to prevent the failure of the load balancer, we setup a backup server using heartbeat, fake, mon, and checkpointing fault-tolerance method. For high availability of file servers in the cluster, we setup coda file system. Coda is a advanced network fault-tolerance distributed file system.

• Genomics & Informatics
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• v.4 no.3
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• pp.125-128
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• 2006

We present an openfree hypertext (also known as wiki) web cluster called BioCC. BioCC is a novel wiki farm that lets researchers create hundreds of biological web sites. The web sites form an organic information network. The contents of all the sites on the BioCC wiki farm are modifiable by anonymous as well as registered users. This enables biologists with diverse backgrounds to form their own Internet bio-communities. Each community can have custom-made layouts for information, discussion, and knowledge exchange. BioCC aims to form an ever-expanding network of openfree biological knowledge databases used and maintained by biological experts, students, and general users. The philosophy behind BioCC is that the formation of biological knowledge is best achieved by open-minded individuals freely exchanging information. In the near future, the amount of genomic information will have flooded society. BioGG can be an effective and quickly updated knowledge database system. BioCC uses an opensource wiki system called Mediawiki. However, for easier editing, a modified version of Mediawiki, called Biowiki, has been applied. Unlike Mediawiki, Biowiki uses a WYSIWYG (What You See Is What You Get) text editor. BioCC is under a share-alike license called BioLicense (http://biolicense.org). The BioCC top level site is found at http://bio.cc/