Download Advancing Big Data Benchmarks: Proceedings of the 2013 by Tilmann Rabl, Nambiar Raghunath, Meikel Poess, Milind PDF

By Tilmann Rabl, Nambiar Raghunath, Meikel Poess, Milind Bhandarkar, Hans-Arno Jacobsen, Chaitanya Baru (eds.)

This booklet constitutes the completely refereed joint lawsuits of the 3rd and Fourth Workshop on immense info Benchmarking. The 3rd WBDB was once held in Xi'an, China, in July 2013 and the Fourth WBDB used to be held in San José, CA, united states, in October, 2013. The 15 papers awarded during this e-book have been rigorously reviewed and chosen from 33 shows. They concentrate on significant information benchmarks; purposes and situations; instruments, platforms and surveys.

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Extra resources for Advancing Big Data Benchmarks: Proceedings of the 2013 Workshop Series on Big Data Benchmarking, WBDB.cn, Xi'an, China, July16-17, 2013 and WBDB.us, San José, CA, USA, October 9-10, 2013, Revised Selected Papers

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It realizes this by maintaining a representation of the social graph in its memory. BG uses this representation to ensure its emulated simultaneous members and resources are unique at an instance in time. A novel feature of BG is its ability to quantify the amount of unpredictable (stale, inconsistent, erroneous) data produced by a data store. BG evaluates a data store for a workload that specifies an SLA. 01 % unpredictable data for Δ = 10 min. BG includes a heuristic search technique to quantify the maximum throughput (actions per second) observed with a data store while satisfying the pre-specified SLA.

Each of these nodes has 24 GB of RAM. These nodes are also equipped with a NetEffect NE020 10 Gb Accelerated Ethernet Adapter (iWARP RNIC) each. The 10 GigE cards are connected using a Fulcrum Focalpoint 10 GigE switch. This cluster is less than four years old and represents the leading-edge technologies used in commodity clusters for Big Data applications. In all our experiments, the Hadoop RPC server and clients run exclusively on different nodes. 7. 2 Micro-benchmark Results In this section, we show some performance results using our micro-benchmarks presented in Sect.

A Poisson distribution results in a pattern of requests that is bursty. This means λ is an average and the number of simultaneous socialites at an instance in time might be higher than λ. While the open emulator is more realistic, its design and implementation requires a careful study. This is because today’s data stores service requests at such a high rate that the emulator must support λ values in the order of a million without exhausting its CPU resources. In addition to the scalability discussions of Sect.

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