1,413 research outputs found

    Cloud Storage Performance and Security Analysis with Hadoop and GridFTP

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    Even though cloud server has been around for a few years, most of the web hosts today have not converted to cloud yet. If the purpose of the cloud server is distributing and storing files on the internet, FTP servers were much earlier than the cloud. FTP server is sufficient to distribute content on the internet. Therefore, is it worth to shift from FTP server to cloud server? The cloud storage provider declares high durability and availability for their users, and the ability to scale up for more storage space easily could save users tons of money. However, does it provide higher performance and better security features? Hadoop is a very popular platform for cloud computing. It is free software under Apache License. It is written in Java and supports large data processing in a distributed environment. Characteristics of Hadoop include partitioning of data, computing across thousands of hosts, and executing application computations in parallel. Hadoop Distributed File System allows rapid data transfer up to thousands of terabytes, and is capable of operating even in the case of node failure. GridFTP supports high-speed data transfer for wide-area networks. It is based on the FTP and features multiple data channels for parallel transfers. This report describes the technology behind HDFS and enhancement to the Hadoop security features with Kerberos. Based on data transfer performance and security features of HDFS and GridFTP server, we can decide if we should replace GridFTP server with HDFS. According to our experiment result, we conclude that GridFTP server provides better throughput than HDFS, and Kerberos has minimal impact to HDFS performance. We proposed a solution which users authenticate with HDFS first, and get the file from HDFS server to the client using GridFTP

    Modeling performance of Hadoop applications: A journey from queueing networks to stochastic well formed nets

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    Nowadays, many enterprises commit to the extraction of actionable knowledge from huge datasets as part of their core business activities. Applications belong to very different domains such as fraud detection or one-to-one marketing, and encompass business analytics and support to decision making in both private and public sectors. In these scenarios, a central place is held by the MapReduce framework and in particular its open source implementation, Apache Hadoop. In such environments, new challenges arise in the area of jobs performance prediction, with the needs to provide Service Level Agreement guarantees to the enduser and to avoid waste of computational resources. In this paper we provide performance analysis models to estimate MapReduce job execution times in Hadoop clusters governed by the YARN Capacity Scheduler. We propose models of increasing complexity and accuracy, ranging from queueing networks to stochastic well formed nets, able to estimate job performance under a number of scenarios of interest, including also unreliable resources. The accuracy of our models is evaluated by considering the TPC-DS industry benchmark running experiments on Amazon EC2 and the CINECA Italian supercomputing center. The results have shown that the average accuracy we can achieve is in the range 9–14%

    Only Aggressive Elephants are Fast Elephants

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    Yellow elephants are slow. A major reason is that they consume their inputs entirely before responding to an elephant rider's orders. Some clever riders have trained their yellow elephants to only consume parts of the inputs before responding. However, the teaching time to make an elephant do that is high. So high that the teaching lessons often do not pay off. We take a different approach. We make elephants aggressive; only this will make them very fast. We propose HAIL (Hadoop Aggressive Indexing Library), an enhancement of HDFS and Hadoop MapReduce that dramatically improves runtimes of several classes of MapReduce jobs. HAIL changes the upload pipeline of HDFS in order to create different clustered indexes on each data block replica. An interesting feature of HAIL is that we typically create a win-win situation: we improve both data upload to HDFS and the runtime of the actual Hadoop MapReduce job. In terms of data upload, HAIL improves over HDFS by up to 60% with the default replication factor of three. In terms of query execution, we demonstrate that HAIL runs up to 68x faster than Hadoop. In our experiments, we use six clusters including physical and EC2 clusters of up to 100 nodes. A series of scalability experiments also demonstrates the superiority of HAIL.Comment: VLDB201
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