• Journal of Software Engineering Society
• /
• v.25 no.4
• /
• pp.63-67
• /
• 2012

According to the growth of data being generated in man fields, a distributed programming model MapReduce has been introduced to handle it. In this paper, we build two cluster system with Solaris and Linux environment on SUN Blade150 respectively and then to evaluate the performance of a MapReduce application running on MapReduce middleware Hadoop in terms of its average elapse time and standard deviation. As a result of this experiment, we show that the overall performance of the MapReduce application based on Hadoop is affected by the configuration of the cluster system.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.15 no.5
• /
• pp.511-518
• /
• 2015

As the cost-per-byte of SSDs dramatically decreases, the introduction of SSDs to Hadoop becomes an attractive choice for high performance data processing. In this paper the cost-per-performance of SSD-based Hadoop cluster (SSD-Hadoop) and HDD-based Hadoop cluster (HDD-Hadoop) are evaluated. For this, we propose a MapReduce performance model using queuing network to simulate the execution time of MapReduce job with varying cluster size. To achieve an accurate model, the execution time distribution of MapReduce job is carefully profiled. The developed model can precisely predict the execution time of MapReduce jobs with less than 7% difference for most cases. It is also found that SSD-Hadoop is 20% more cost efficient than HDD-Hadoop because SSD-Hadoop needs a smaller number of nodes than HDD-Hadoop to achieve a comparable performance, according to the results of simulation with varying the number of cluster nodes.

• KIISE Transactions on Computing Practices
• /
• v.21 no.8
• /
• pp.561-566
• /
• 2015

Hadoop is becoming widely adopted in scientific and commercial areas as an open-source distributed data processing framework. Recently, for real-time processing and analysis of data, an attempt to apply high-performance computing technologies to Hadoop is being made. In this paper, we have expanded the Hadoop Filesystem library to support Lustre, which is a popular high-performance parallel distributed filesystem, and implemented the Hadoop MapReduce execution environment over the Lustre filesystem. We analysed Hadoop MapReduce over Lustre by using Hadoop standard benchmark tools. We found that Hadoop MapReduce over Lustre execution has a performance 2-13 times better than a typical Hadoop MapReduce execution.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.11
• /
• pp.569-584
• /
• 2016

In this paper, we present a spatial knowledge extractor implemented in Hadoop MapReduce parallel, distributed computing environment. From a large spatial dataset, this knowledge extractor automatically derives a qualitative spatial knowledge base, which consists of both topological and directional relations on pairs of two spatial objects. By using R-tree index and range queries over a distributed spatial data file on HDFS, the MapReduce-enabled spatial knowledge extractor, MRSPAKE, can produce a web-scale spatial knowledge base in highly efficient way. In experiments with the well-known open spatial dataset, Open Street Map (OSM), the proposed web-scale spatial knowledge extractor, MRSPAKE, showed high performance and scalability.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.5
• /
• pp.975-987
• /
• 2013

The Hadoop system was developed by the Apache foundation based on GFS and MapReduce technologies of Google. Many modern systems for managing and processing the big data have been developing based on the Hadoop because the Hadoop was designed for scalability and distributed computing. The R software has been considered as a well-suited analytic tool in the Hadoop based systems because the R is flexible to other languages and has many libraries for complex analyses. We introduced Rhipe which is a R package supporting MapReduce programming easily under the Hadoop system, and implemented a MapReduce program using Rhipe for multiple regression especially. In addition, we compared the computing speeds of our program with the other packages (ff and bigmemory) for processing the large data. The simulation results showed that our program was more fast than ff and bigmemory as the size of data increases.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.5
• /
• pp.1013-1027
• /
• 2013

As the need for large-scale data analysis is rapidly increasing, Hadoop, or the platform that realizes large-scale data processing, and MapReduce, or the internal computational model of Hadoop, are receiving great attention. This paper reviews the basic concepts of Hadoop and MapReduce necessary for data analysts who are familiar with statistical programming, through examples that combine the R programming language and Hadoop.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.05a
• /
• pp.103-105
• /
• 2017

Hadoop MapReduce is a processing framework in which users' job can be efficiently processed in parallel and distributed ways on the Hadoop cluster. MapReduce task schedulers are used to select target nodes and assigns user's tasks to them. Previous schedulers cannot fully utilize resources of Hadoop cluster because they does not consider dynamic characteristics of cluster based on nodes' availability. To increase utilization of Hadoop cluster, this paper proposes a novel task assignment policy for MapReduce that assigns a job tasks to dynamic cluster efficiently by considering availability of each node.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.05a
• /
• pp.479-482
• /
• 2014

Nowadays open-source hadoop systems have been using widely to efficiently manage a fast-growing big data. Hadoop systems consist of distributed file processing system called HDFS (Hadoop Distributed File System) and distributed parallel processing system called MapReduce. The MapReduce reads and processes big data from HDFS and then processed results are written in HDFS again by the MapReduce. Such a processing method has different system structure respectively according to hadoop version. Therefore, this paper shows analysis results for performance of hadoop systems. For this, we devise a way which monitors hadoop systems and measure occurrence frequency of processes, threads, and variables generated in hadoop system itself using the devised way. So, by using the measured results as analysis indicator, we help the indicator predict inner performance of hadoop systems.

• 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.20 no.8
• /
• pp.1-6
• /
• 2015

The MapReduce Program of Hadoop Distributed File System operates on any unspecified nodes due to distributed-parallel process and block replicate for data stability. Since it is difficult to guarantee the cache locality when a Solid State Drive is used as a cache in hadoop, cache hit-rate is decreased. In this paper, we suggest a method to improve cache hit rate by pre-loading the input data of the MapReduce onto the SSD cache. To perform this method, we estimated the blocks that are used on each node by using capacity scheduler and block metadata. Eventually we could increase the performance of SSD cache by loading the blocks onto SSD cache before the Map Task run.