By Tilmann Rabl, Kai Sachs, Meikel Poess, Chaitanya Baru, Hans-Arno Jacobson
This publication constitutes the completely refereed post-workshop court cases of the fifth overseas Workshop on massive info Benchmarking, WBDB 2014, held in Potsdam, Germany, in August 2014.
The thirteen papers awarded during this ebook have been conscientiously reviewed and chosen from various submissions and canopy subject matters corresponding to benchmarks requirements and recommendations, Hadoop and MapReduce - within the diverse context corresponding to virtualization and cloud - in addition to in-memory, facts new release, and graphs.
Read Online or Download Big Data Benchmarking: 5th International Workshop, WBDB 2014, Potsdam, Germany, August 5-6- 2014, Revised Selected Papers PDF
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Additional resources for Big Data Benchmarking: 5th International Workshop, WBDB 2014, Potsdam, Germany, August 5-6- 2014, Revised Selected Papers
The author envision that TPCx-HS will be a useful benchmark standard to buyers, as they evaluate 28 R. Nambiar new systems for Hadoop deployments in terms of performance, price-performance and energy efﬁciency. Also, enabling healthy competition between vendors that will result in product developments and improvements. Acknowledgement. Developing an industry standard benchmark for a new environment like Big Data has taken the dedicated efforts of experts across many companies. The author thank the contributions of Andrew Bond (Red Hat), Andrew Masland (NEC), Avik Dey (Intel), Brian Cauﬁeld (IBM), Chaitanya Baru (SDSC), Da Qi Ren (Huawei), Dileep Kumar (Cloudera), Jamie Reding (Microsoft), John Poelman (IBM), Karthik Kulkarni (Cisco), Meikel Poess (Oracle), Mike Brey (Oracle), Mike Crocker (SAP), Paul Cao (HP), Reza Taheri (VMware), Simon Harris (IBM), Tariq Magdon-Ismail (VMware), Wayne Smith (Intel), Yanpei Chen (Cloudera), Michael Majdalany (L&M), Forrest Carman (Owen Media) and Andreas Hotea (Hotea Solutions).
We made sure that the system was running stable at the end of each measurement phase and no cached results were reused in the following phases, so the tested system did not get any “unfair” advantage. The benchmark run consists of 3 growth phases and 2 decline phases. The scaling factor is 2, the growth factor is 2 as well. As mentioned in Sect. 1, we choose the number of parallel clients as the changing dimension, starting with a single client in the ﬁrst phase. We made use of the TPC-H port from the Hive developers  and extended it with optimizations provided by Shark.
References 1. 2. 3. 4. aspx 36 5. 6. 7. 8. A. Joshi et al. html 9. org 10. html 11. de Abstract. Existing analytical query benchmarks, such as TPC-H, often assess database system performance on on-premises hardware installations. On the other hand, some benchmarks for cloud-based analytics deal with flexible infrastructure, but often focus on simpler queries and semi-structured data. With our benchmark draft we attempt to bridge the gap by challenging analytical platforms to answer complex queries on structured business data while leveraging the elastic infrastructure of the cloud to satisfy performance requirements.
Big Data Benchmarking: 5th International Workshop, WBDB 2014, Potsdam, Germany, August 5-6- 2014, Revised Selected Papers by Tilmann Rabl, Kai Sachs, Meikel Poess, Chaitanya Baru, Hans-Arno Jacobson