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Fault-tolerant computer systems / Cloud infrastructure / Data Intensive Computing / MapReduce / Apache Hadoop / External sorting / Dataflow / Computer cluster / Replication / Computing / Concurrent computing / Parallel computing


Applying Simple Performance Models to Understand Inefficiencies in Data-Intensive Computing Elie Krevat∗ , Tomer Shiran∗ , Eric Anderson† , Joseph Tucek† , Jay J. Wylie† , Gregory R. Ganger∗ ∗ Carnegie Mell
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Document Date: 2012-02-08 14:14:06

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Riverbed / HewlettPackard Labs / IBM / APC / Microsoft Research / NEC Laboratories / NetApp / LSI / HP / Oracle / Yahoo! Labs / Samsung / STEC / Symantec / Seagate / EMC / Google / VMWare / Intel / Facebook / Hitachi / /

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Carnegie Mellon University / Parallel Data Laboratory Carnegie Mellon University / /

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dataflow systems / map-reduce systems / map-reduce computing / parallel applications / software stack / simplified dataflow processing tool / hypothetical systems / scale-out systems / file systems / online transaction records / data-intensive computing / similar data processing tasks / computing / cloud computing / map-reduce style systems / parallel systems / data-intensive computing systems / well-tuned systems / /

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Army Research Office / Carnegie Mellon University / Parallel Data Laboratory Carnegie Mellon University Pittsburgh / /

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Gregory R. Ganger / Dw N Dw / Joseph Tucek / Eric Anderson / Tomer Shiran / Jay J. Wylie / Dw N Table / /

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model / model for both production and hypothetical systems / representative / Dean / /

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Pennsylvania / Manitoba / /

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computing Today / /

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Ethernet / load balancing / simulation / operating system / DBMS / /

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