• Journal of Korea Multimedia Society
• /
• v.15 no.12
• /
• pp.1499-1505
• /
• 2012

Recently, the distributed processing system for big data has been actively investigated owing to the development of high speed network and storage technologies. In addition, virtual system that can provide efficient use of system resources through the consolidation of servers has been increasingly recognized. But, when we configure distributed processing system for big data in virtual machine environments, many problems occur. In this paper, we did an experiment on the optimization of I/O bandwidth according to the creation and placement of VMs and tasks with composing Hadoop cluster in virtual environments and evaluated the results of an experiment. These results conducted by this paper will be used in the study on the development of Hadoop Scheduler supporting I/O bandwidth balancing in virtual environments.

• Journal of Information Technology Services
• /
• v.13 no.4
• /
• pp.231-243
• /
• 2014

Of the various data that corporations can approach, web log data are important data that correspond to data analysis to implement customer relations management strategies. As the volume of approachable data has increased exponentially due to the Internet and popularization of smart phone, web log data have also increased a lot. As a result, it has become difficult to expand storage to process large amounts of web logs data flexibly and extremely hard to implement a system capable of categorizing, analyzing, and processing web log data accumulated over a long period of time. This study thus set out to apply Hadoop, a distributed processing system that had recently come into the spotlight for its capacity of processing large volumes of data, and propose an efficient analysis plan for large amounts of web log. The study checked the forms of web log by the effective web log collection methods and the web log levels by using Hadoop and proposed analysis techniques and Hadoop organization designs accordingly. The present study resolved the difficulty with processing large amounts of web log data and proposed the activity patterns of users through web log analysis, thus demonstrating its advantages as a new means of marketing.

• ETRI Journal
• /
• v.42 no.5
• /
• pp.748-760
• /
• 2020

MapReduce is a framework that can process huge datasets in parallel and distributed computing environments. However, a single machine failure during the runtime of MapReduce tasks can increase completion time by 50%. MapReduce handles task failures by restarting the failed task and re-computing all input data from scratch, regardless of how much data had already been processed. To solve this issue, we need the computed key-value pairs to persist in a storage system to avoid re-computing them during the restarting process. In this paper, the task failure resilience (TFR) technique is proposed, which allows the execution of a failed task to continue from the point it was interrupted without having to redo all the work. Amazon ElastiCache for Redis is used as a non-volatile cache for the key-value pairs. We measured the performance of TFR by running different Hadoop benchmarking suites. TFR was implemented using the Hadoop software framework, and the experimental results showed significant performance improvements when compared with the performance of the default Hadoop implementation.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.8
• /
• pp.73-81
• /
• 2014

This study proposed development method for practical use techniques compromise data storage layer, data processing layer, data analysis layer, visualization layer. Data of storage, process, analysis of each phase can see visualization. After data process through Hadoop, the result visualize from Mahout. According to this course, we can capture several features of customer, we can choose recommendation of financial item on time. This study introduce background and problem of big data and discuss development method and case study that how to create big data has new business opportunity through financial item recommendation case.

• Journal of the Korea Convergence Society
• /
• v.9 no.10
• /
• pp.13-19
• /
• 2018

MapReduce, the Hadoop's essential core technology, is most commonly used to process big data based on the Hadoop distributed file system. However, the existing MapReduce-based big data processing techniques have a feature of dividing and storing files in blocks predefined in the Hadoop distributed file system, thus wasting huge infrastructure resources. Therefore, in this paper, we propose an efficient MapReduce-based big data processing scheme. The proposed method enhances the storage efficiency of a big data infrastructure environment by converting and compressing the data to be processed into a data format in advance suitable for processing by MapReduce. In addition, the proposed method solves the problem of the data processing time delay arising from when implementing with focus on the storage efficiency.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.05a
• /
• pp.263-266
• /
• 2013

The universal cloud computing environment, the increase of mobile devices, and the emergence of various web-based services require large amounts of storage space. With the widespread use of Web-based storage services, such as Google Drive, Naver Ndrive, Daum Cloud, there is a need for more storage space. Therefore, storage areas can be provided according to the needs of users of virtualized storage resources through a network, and a large, easy to extend, and royalty in a specific geographical location, cloud storage may be the limelight. In this paper, find out about the features of open source software technology, Hadoop, Swift, GlusterFS for Cloud Storage infrastructure.

• KIPS Transactions on Computer and Communication Systems
• /
• v.9 no.9
• /
• pp.207-212
• /
• 2020

The amount of data has increased significantly due to advances in technology, and various big data processing platforms are emerging, to handle it. Among them, the most widely used platform is Hadoop developed by the Apache Software Foundation, and Hadoop is also used in the IoT field. However, the existing Hadoop-based IoT sensor data collection and analysis environment has a problem of overloading the name node due to HDFS' Small File, which is Hadoop's core project, and it is impossible to update or delete the imported data. This paper uses Apache Kudu and Impala to design Lambda Architecture. The proposed Architecture classifies IoT sensor data into Cold-Data and Hot-Data, stores it in storage according to each personality, and uses Batch-View created through Batch and Real-time View generated through Apache Kudu and Impala to solve problems in the existing Hadoop-based IoT sensor data collection analysis environment and shorten the time users access to the analyzed data.

• Journal of Digital Convergence
• /
• v.12 no.8
• /
• pp.351-359
• /
• 2014

The log data generated by security equipment have been synthetically analyzed on the ESM(Enterprise Security Management) base so far, but due to its limitations of the capacity and processing performance, it is not suited for big data processing. Therefore the another way of technology on the big data platform is necessary. Big Data platform can achieve a large amount of data collection, storage, processing, retrieval, analysis, and visualization by using Hadoop Ecosystem. Currently ESM technology has developed in the way of SIEM (Security Information & Event Management) technology, and to implement security technology in SIEM way, Big Data platform technology is essential that can handle large log data which occurs in the current security devices. In this paper, we have a big data platform Hadoop Ecosystem technology for analyzing the security log for sure how to implement the system model is studied.

• KIISE Transactions on Computing Practices
• /
• v.23 no.2
• /
• pp.128-133
• /
• 2017

In recent years, the number of systems for the analysis of large volumes of data is increasing. Hadoop, a representative big data system, stores and processes the large data in the distributed environment of multiple servers, where system-resource management is very important. The authors attempted to detect anomalies from the rapid changing of the log data that are collected from the multiple servers using simple but efficient anomaly-detection techniques. Accordingly, an Apache Hive storage architecture was designed to store the log data that were collected from the multiple servers in the Hadoop ecosystem. Also, three anomaly-detection techniques were designed based on the moving-average and 3-sigma concepts. It was finally confirmed that all three of the techniques detected the abnormal intervals correctly, while the weighted anomaly-detection technique is more precise than the basic techniques. These results show an excellent approach for the detection of log-data anomalies with the use of simple techniques in the Hadoop ecosystem.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.3
• /
• pp.607-612
• /
• 2015

As the amount of vehicle's diagnostics data increases, the actors in automotive ecosystem will encounter difficulties to perform a real time analysis in order to simulate or to design new services according to the data gathered from the connected cars. In this paper, we have conducted a study of a Big Data solution that expresses the essential deep analytics to process and analyze vast quantities of vehicles on board diagnostics data generated by cars. Hadoop and its ecosystems have been deployed to process a large data and delivered useful outcomes that may be used by actors in automotive ecosystem to deliver new services to car owners. As the Intelligent transport system is involved to guarantee safety, reduce rate of crash and injured in the accident due to speed, addressing big data solution based on vehicle diagnostics data is upcoming to monitor real time outcome from it and making collection of data from several connected cars, facilitating reliable processing and easier storage of data collected.