how mapreduce manages to achieve high computing performance

/Widths[525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 Our results show that it is possible to build a MapReduce-based system that is not only °exible and scal-able, but is also e–cient. /BaseFont/UHQZAQ+NimbusSanL-Regu /Widths[285 514 856 514 856 799 285 400 400 514 799 285 343 285 514 514 514 514 514 /plusminus /twosuperior /threesuperior /acute /mu /paragraph /periodcentered /cedilla /Widths[1028 514 514 1028 1028 1028 799 1028 1028 628 628 1028 1028 1028 799 279 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 /Name/F1 667 667 611 278 278 278 469 556 222 556 556 500 556 556 278 556 556 222 222 500 222 722 667 611 778 722 278 556 722 611 833 722 778 667 778 722 667 611 722 667 944 667 19 0 obj /FontDescriptor 15 0 R /Subtype/Type1 using the MapReduce framework to construct high-performance and highly scalable applications. /FirstChar 33 << We flnd that only immutable decoding introduces high performance overhead. It is a software framework that allows you to write applications for processing a large amount of data. endobj >> MapReduce was initially proposed by Google for large scale data processing in a distributed computing en … 402 405 400 571 542 742 542 542 457 514 1028 514 514 514 0 0 0 0 0 0 0 0 0 0 0 0 MapReduce implementation using CGL-MapReduce to solve this problem. 667 1000 722 667 667 667 667 278 278 278 278 722 722 778 778 778 778 778 584 778 /BaseFont/JFWLXE+CMR9 389 333 722 0 0 722 0 333 500 500 500 500 220 500 333 747 300 500 570 333 747 333 799 799 0 0 799 799 799 1028 514 514 799 799 799 799 799 799 799 799 799 799 799 722 722 722 722 667 667 611 556 556 556 556 556 556 889 556 556 556 556 556 278 278 to its ease-of-use and its ability to scale up on demand. /onesuperior /ordmasculine /guillemotright /onequarter /onehalf /threequarters /questiondown 0 0 742 600 571 571 856 856 285 314 514 514 514 514 514 771 457 514 742 799 514 928 computing environments. MapReduce frameworks such as Hadoop, however for large data sets, the implementation will not be efficient, because all map tasks need to access all the elements of one of the data sets. /LastChar 255 This requires dividing the workload across a large number of machines. memory, CPU, storage, etc.). << The use of MapReduce framework has been widely came into focus to handle such massive data effectively. 742 685 685 685 685 685 628 628 457 457 457 457 514 514 400 400 285 514 514 628 514 Encouraged by the success of the CPU-based MapReduce frameworks, we develop Mars, a MapReduce framework on graphics processors, or GPUs. endobj 0 0 0 0 0 0 0 333 191 278 278 355 556 556 889 667 222 333 333 389 584 278 333 278 xڝZI��ȱ��_��#�����2�Fֳ�Ȟn�3>T��&F @cQO��;��, $��'Ve%j����,n?6O���ͻ�o>��0�8��?n��ϒp�c?������c��EY��e^�=n��k��m����v������ȘCy�B���6L���?���͇h�~a6`R��f��~�G���+>o��++Y��]�U�y���4�ޕhb�����t�O��{t�;����P#�� ���x��r:|�k��m[�;�w�����vQA"3$��ϳ����U�X��oM�e%$��^.EvG��sە"�x��E�B�$�M���x��[y%~䛀��D��4�����aI��e�cU��>�=I�z�01��N���W�ݞd���op=c� ����~�,���$�6�8�Mdx�����x�5���=�����f!�0�� �_��i,�Mʶ{�}y�{��|/�P�z�Io2�k�P��W� �$��P�/24Ns׶{*w�����$�����꧑w��E+�,��K_���n.���ñ��28˾��E�h����U�v�9����++��`˼�!��&}G-2F\k�l�j���U] X�e�փ��3k��Sg���LU��c�,r�g�@T�i�i4�dd����i7�5������Π.O�ܓ'y�����Z��X'�toh��SS=�ɺ�[Y *�\u�z7(��h�n������u]�2��t���|�y�_�?���q�8$-6)�^�z�L���HTo���l������]{*�g��a�;K�c����-��"^����-. 400 584 333 333 333 611 556 278 333 333 365 556 834 834 834 611 722 722 722 722 722 The scalability of MapReduce is proven to be high, because a job in the MapReduce model is partitioned into numerous small tasks running on multiple machines in a large-scale cluster. /Name/F6 /bullet /endash /emdash /tilde /trademark /scaron /guilsinglright /oe /Delta /lozenge Sector is a high-performance distributed file system; Sphere is a parallel data processing engine used to process Sector data files. However, in the cloud deployments of MapReduce, the input data is located on remote storage which indicates the importance of the scheduling of Map tasks as well. 25 0 obj With the introduction of MapReduce and Hadoop version 2, previous JobTracker and TaskTracker daemons have been replaced with components of Yet Another Resource Negotiator (YARN), called ResourceManager and NodeManager. /Agrave /Aacute /Acircumflex /Atilde /Adieresis /Aring /AE /Ccedilla /Egrave /Eacute parallel processing models for robust and speedy data. MapReduce programs are designed to compute large volumes of data in a parallel fashion. /Name/F2 Failure of workers is handled transparently, by restarting the worker. /Name/F5 So the MapReduce framework is conducive to the Hadoop framework. /notequal /infinity /lessequal /greaterequal /partialdiff /summation /product /pi /Type/Font >> 16 0 obj analysis. 500 500 1000 500 500 333 1000 556 333 1000 0 0 0 0 0 0 500 500 350 500 1000 333 1000 /BaseFont/KOZFNS+NimbusSanL-Bold /guilsinglleft /OE /Omega /radical /approxequal 147 /quotedblleft /quotedblright /LastChar 255 In real, it is a scalable The framework uses MapReduce to split the data into blocks and assign the chunks to nodes across a cluster. This approach to 736 611 871 563 697 782 708 1229 842 816 717 839 874 622 563 642 632 1017 732 685 It also allows implementation on large scale of commodity PCs to achieve high performance. 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 278 278 500 556 500 500 500 500 500 570 500 556 556 556 556 500 556 500] 0 0 0 0 0 0 0 333 278 250 333 555 500 500 1000 833 333 333 333 500 570 250 333 250 278 278 611 611 611 611 611 611 611 584 611 611 611 611 611 556 611 556] blogs/oreilly-open-source-skill-survey-blog, podcasts/the-application-modernization-series/, blogs/challenges-creating-an-open-scalable-and-secure-serverless-platform, In the era of open hybrid cloud, open source skills matter more than proprietary software skills, Build a microservices-based distributed cloud app with IBM Cloud Satellite, 13 challenges creating an open, scalable, and secure serverless platform, articles/when-to-use-iaas-faas-paas-and-caas, articles/cl-best-practices-deploying-apps-in-cloud, videos/hybrid-cloud-architecture-introduction, videos/hybrid-cloud-architecture-part-1-connectivity, videos/hybrid-cloud-architecture-part-3-security, Best practices for deploying your apps in the cloud, Hybrid cloud architecture: Part 1 Connectivity, Hybrid cloud architecture: Part 3 Security, tutorials/cl-ibm-blockchain-101-quick-start-guide-for-developers-bluemix-trs, IBM Blockchain 101: Quick-start guide for developers, patterns/create-cognitive-banking-chatbot, tutorials/create-interactive-dashboards-on-watson-studio, Create interactive dashboards on Watson Studio, articles/scale-up-and-scale-out-vms-vs-containers, Growing compute by scaling up and scaling out, videos/introduction-to-confidential-computing, IBM SaaS User and Subscription Management, IBM Cloud Application Performance Management API, IBM Sterling Fulfillment Optimizer with Watson. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 799 285 799 514 799 514 799 799 MapReduce gained its popularity when used successfully by Google. Clusters allow the data used by an application to be partitioned among the available computing resources and processed independently to achieve performance and scalability based on the amount of data. /Encoding 7 0 R /Type/Encoding 611 611 611 611 389 556 333 611 556 778 556 556 500 389 280 389 584 0 0 0 278 556 To achieve high performance in data-intensive computing, it is important to minimize the movement of data. /Type/Font >> Deliver on-demand computing resources with public, private, hybrid, or multicloud architectures. To address this issue, we have developed Marmot, a high-performance, geospatial big data processing system based on MapReduce. IBM Cloud Satellite: Build faster. MapReduce is a computing model designed for processing large data sets on server clusters [13]. /FontDescriptor 12 0 R /BaseFont/ASGGZP+NimbusRomNo9L-Medi Its flexibility in work distribution, loosely synchronized computation, and tolerance for het-erogeneity are ideal features for opportunistically available resources. >> The need for parallel computing has resulted. 7 0 obj It is log-ically separated into two … MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster. << 285 856 771 856 428 685 685 799 799 457 457 457 628 799 799 799 799 0 0 0 0 0 0 0 285 514 285 285 514 571 457 571 457 314 514 571 285 314 542 285 856 571 514 571 542 The free, community edition can parse log files, Omniture Web analytics data, XML and JSON. stream 0 0 0 0 0 0 0 333 238 278 333 474 556 556 889 722 278 333 333 389 584 278 333 278 27 0 obj /Widths[333 556 556 167 333 667 278 333 333 0 333 570 0 667 444 333 278 0 0 0 0 0 Hadoop framework [25, 26] is a Java based implementation of the MapReduce computing model.In one Hadoop cluster the nodes are categorized into one NameNode and several DataNodes. 722 722 722 722 722 611 556 500 500 500 500 500 500 722 444 444 444 444 444 278 278 Although high-performance interconnects have been popular in the High-Performance Computing (HPC) community, the perfor-mance impact of these networks on MapReduce programs remains unclear. << 22 0 obj 722 722 722 722 667 667 611 556 556 556 556 556 556 889 500 556 556 556 556 278 278 The open source Apache Hadoop project, which adopts the MapReduce framework and a distributed file system, has recently given bioinformatics researchers an opportunity to achieve scalable, efficient and reliable computing performance on Linux clusters and on cloud computing services. 1. • Use of the parallelism of MapReduce to optimize parsing performance across massive structured and unstructured data sets. 10 0 obj 278 278 556 556 556 556 556 556 556 584 611 556 556 556 556 500 556 500] Hadoop – a popular open-source implementation of the Google’s MapReduce model is primarily developed by Ya-hoo [1]. A MapReduce program is composed of a map procedure, which performs filtering and sorting, and a reduce method, which performs a summary operation. /Type/Font /equal /greater /question /at /A /B /C /D /E /F /G /H /I /J /K /L /M /N /O /P /Q /copyright /ordfeminine /guillemotleft /logicalnot /hyphen /registered /macron /degree /Subtype/Type1 To address these programming challenges, we present a design and implementation of MapReduce for the Cell pro-cessor. Lamia Youseff, et al., presents an evaluation on the performance impact of Xen on MPI [27]. model to achieve a near real – time generation of user preferences regardless of total case memory size. endobj /Ecircumflex /Edieresis /Igrave /Iacute /Icircumflex /Idieresis /Eth /Ntilde /Ograve /BaseFont/HSFSHM+CMTT9 /Widths[333 611 611 167 333 611 278 333 333 0 333 584 0 611 500 333 278 0 0 0 0 0 << >> %PDF-1.4 INTRODUCTION MapReduce-based systems are increasingly being used for large-scale data analysis. Hadoop provides a systematic way to implement this programming paradigm. endobj 13 0 obj Every machine in a cluster both stores and processes data. Hadoop stores the data to disks using HDFS. Cloud computing is the delivery of on-demand computing resources, everything from applications to data centers, over the internet. The IBM Developer podcast is the place where developers hear all about open topics and technologies. Marmot extends Hadoop at a low level to support seamless integration between spatial and nonspatial operations of a solid framework, allowing improved performance of geoprocessing workflow. /Udieresis /Yacute /Thorn /germandbls /agrave /aacute /acircumflex /atilde /adieresis 500 1000 556 556 333 1000 667 333 1000 0 0 0 0 0 0 500 500 350 556 1000 333 1000 /FirstChar 33 The name node directs the placement of data onto compute nodes through HDFS, assigns 722 1000 722 667 667 667 667 278 278 278 278 722 722 778 778 778 778 778 584 778 Experimental results show that, with the fine-tuned parameters, we achieve a total of 4× speedup compared with the original performance (using the default settings). /FirstChar 1 833 556 500 556 556 444 389 333 556 500 722 500 500 444 394 220 394 520 0 0 0 333 525 525 525 525 525 525 525 525 525 525 525 525 525 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 tunistic computing. MapReduce is the core-processing engine of Hadoop, which accommodate rapidly increasing demands on computing resources required by massive data sets. 1028 685 685 914 914 0 0 571 571 685 514 742 742 799 799 628 821 674 543 794 542 556 333 944 0 0 667 0 333 556 556 556 556 280 556 333 737 370 556 584 333 737 333 722 722 667 333 278 333 581 500 333 500 556 444 556 444 333 500 556 278 333 556 278 /FontDescriptor 18 0 R 799 1028 1028 799 799 1028 799] 722 667 611 778 778 389 500 778 667 944 722 778 611 778 722 556 667 722 722 1000 278 500 500 500 500 500 500 500 500 500 500 333 333 570 570 570 500 930 722 667 722 Each MapReduce program defines two functions: map() and reduce(). Use these APIs to perform user management functions for your IBM SaaS subscriptions, IBM Cloud Application Performance Management API is a managed API service offering that provides the following cloud-centric REST APIs: Resource…. 500 333 944 0 0 667 0 333 556 556 556 556 260 556 333 737 370 556 584 333 737 333 << 667 611 333 278 333 584 556 278 556 611 556 611 556 333 611 611 278 278 556 278 889 /Filter[/FlateDecode] /Subtype/Type1 To handle database-like work-load, MapReduce users should strictly use mutable de-coding. Another system sharing most of the design principles of MapReduce is Sector/Sphere [13], which has been designed to support distributed data storage and processing over large Cloud systems. ... Hydra is a genome sequence database search engine that is designed to run on top of the Hadoop and MapReduce distributed computing framework . /Name/F3 Each MapReduce computation processes a set of input key/value pairs and produces a set of output key/value pairs. /R /S /T /U /V /W /X /Y /Z /bracketleft /backslash /bracketright /asciicircum /underscore /FirstChar 1 /ugrave /uacute /ucircumflex /udieresis /yacute /thorn /ydieresis] >> /FontDescriptor 9 0 R /Type/Font processing technique and a program model for distributed computing based on java /FirstChar 1 /grave /quotesingle /space /exclam /quotedbl /numbersign /dollar /percent /ampersand Anywhere. [20] … /Name/F4 278 556 556 556 556 556 556 556 556 556 556 333 333 584 584 584 611 975 722 722 722 These schemas all adopt the divide-and-conquer strategy to split the merging job into sequential phases/stages consisting of subtasks that are conquered in an ordered, parallel, and bottleneck-free way. /idieresis /eth /ntilde /ograve /oacute /ocircumflex /otilde /odieresis /divide /oslash >> We can develop an efficient iterative MapReduce implementation using CGL-MapReduce to … As a result, MapReduce scientific data processing is a popular application on the Cloud [6]. /aring /ae /ccedilla /egrave /eacute /ecircumflex /edieresis /igrave /iacute /icircumflex /Encoding 7 0 R /quoteright /parenleft /parenright /asterisk /plus /comma /hyphen /period /slash The Hadoop framework itself manages all the tasks of issuing, verifying completion of work, fetching data from HDFS, copying data to the nodes’ group, and so all. For the last few years, MapReduce has appeared as the most popular computing paradigm for parallel, batch-style and analysis of large amount of data [3]. /FirstChar 33 /Encoding 7 0 R 400 584 333 333 333 556 537 278 333 333 365 556 834 834 834 611 667 667 667 667 667 /LastChar 196 The NameNode manages the metadata of the cluster, whilst the DataNode executes a number of Map (mapper) and Reduce (reducer) operations in parallel. 833 556 556 556 556 333 500 278 556 500 722 500 500 500 334 260 334 584 0 0 0 222 It does so in a reliable and fault-tolerant manner. /Widths[333 500 500 167 333 556 222 333 333 0 333 584 0 611 500 333 278 0 0 0 0 0 A single master manages all the workers. MapReduce is a programming model for data processing. /FontDescriptor 21 0 R The MapReduce Over the past five years MapReduce has attained considerable interest from both the database and systems research community. 278 556 556 556 556 556 556 556 556 556 556 278 278 584 584 584 556 1015 667 667 The map() function takes one input key/value optimization are also needed to insure high-performance results as well as high programmer productivity. In Hadoop, mostly the computing takes place on nodes and data in nodes itself which reduces the network traffic. endobj C. Hadoop Hadoop is an open-source cluster-based MapRe-duce implementation written in Java [8]. /Oacute /Ocircumflex /Otilde /Odieresis /multiply /Oslash /Ugrave /Uacute /Ucircumflex /florin /quotedblbase /ellipsis /dagger /daggerdbl /circumflex /perthousand /Scaron plication to achieve high performance. The software offers seamless scalability options. 722 1000 722 667 667 667 667 389 389 389 389 722 722 778 778 778 778 778 570 778 /quoteleft /a /b /c /d /e /f /g /h /i /j /k /l /m /n /o /p /q /r /s /t /u /v /w /x A growing number of cloud companies, such as Amazon [9] are planning to build their next gener-ation clusters on top of high-performance interconnects. This characteristic allows processing algorithms to execute on the nodes where the data resides reducing system overhead and increasing performance. << /LastChar 196 /zero /one /two /three /four /five /six /seven /eight /nine /colon /semicolon /less The various types of cloud computing deployment models include public cloud, private cloud, hybrid cloud, and multicloud. Apache Hadoop is a MapReduce is a popular programming model for cloud computing, which simplifies large-scale parallel data processing. >> /y /z /braceleft /bar /braceright /asciitilde 128 /Euro /integral /quotesinglbase MapReduce. 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 /Subtype/Type1 Read more, O'Reilly survey highlights that open source cloud skills set devs apart for career prospects, While application modernization is often seen as a technology initiative, it is also a cultural shift and affords the opportunity…. MapReduce job comprises a number of map tasks and reduces tasks. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 642 856 799 714 685 771 742 799 742 799 endobj by entities unquestionably, require high performance. The computation takes a set of input key/value pairs and produces a set of output key/value pairs. /ring 11 /breve /minus 14 /Zcaron /zcaron /caron /dotlessi /dotlessj /ff /ffi /ffl computing resources. plementation, the performance of MapReduce can be im-proved by a factor of 2.5 to 3.5 and approaches to Parallel Databases. /Type/Font /Length 4083 Authors Michael T. G. et al., in their paper [6], study the MapReduce framework from an algorithmic standpoint and demonstrate the usefulness of approach by designing and /Differences[1 /dotaccent /fi /fl /fraction /hungarumlaut /Lslash /lslash /ogonek /Type/Font MapReduce then processes the data in parallel on each node to produce a unique output. Securely. In the context of MapReduce task scheduling, many algorithms mainly focus on the scheduling of Reduce tasks with the assumption that scheduling of Map tasks is already done. Before learning how Hadoop works, let’s brush the basic Hadoop concept. /BaseFont/PMUXXM+CMSY9 /Subtype/Type1 Findings: In this study, we custom-design optimized schemas for three Apache big data platforms, Hadoop (MapReduce), HBase, and Spark, to perform sorted merging of a large number of VCF files. In Google’s MapReduce implementation, the high-performance distributed file system, GFS [14], is used to store the input, intermediate, and output data. We show that a mutable decoding scheme is faster than an immutable decoding scheme by a factor of 10, and improves the performance of MapReduce in the selection task by a factor of 2. Informatica’s HParser is available in a both a free and commercial edition. 371 528 799 642 942 771 799 699 799 756 571 742 771 771 1056 771 771 628 285 514 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 525 /Ydieresis 161 /exclamdown /cent /sterling /currency /yen /brokenbar /section /dieresis /FontDescriptor 24 0 R /LastChar 255 514 514 514 514 514 514 285 285 285 799 485 485 799 771 728 742 785 699 671 806 771 400 570 300 300 333 556 540 250 333 300 330 500 750 750 750 500 722 722 722 722 722 The algorithm is similar to the matrix multiplication algorithm we will explain in section 3. 556 333 1000 556 556 333 1000 667 333 1000 0 0 0 0 0 0 333 333 350 556 1000 333 1000 Energy exploration and high-performance computing (HPC) are made for each other. /Subtype/Type1 /LastChar 196 The "MapReduce System" orchestrates the processing by marshalling the distributed … 1042 799 285 514] 722 722 667 611 778 722 278 500 667 556 833 722 778 667 778 722 667 611 722 667 944 MapReduce runs these applications in parallel on a cluster of low-end machines. While this union is conceptually appealing, a vi- High Performance Computing (HPC) systems ... A distributed computing system manages hundreds or thousands of computer systems, which are limited in processing resources (e.g. GPUs have recently been utilized in various domains, including high-performance computing [22]. << MapReduce is the data processing layer of Hadoop. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 525 525 525 525 525 525 525 525 525 525 0 0 525 525 525] A key aspect of the MapReduce software framework, as expressed in the Hadoop implementation, is the use of a special, centralized compute node, called the NameNode. endobj

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