By Tilmann Rabl, Kai Sachs, Meikel Poess, Chaitanya Baru, Hans-Arno Jacobson
This publication constitutes the completely refereed post-workshop lawsuits of the fifth foreign Workshop on sizeable info Benchmarking, WBDB 2014, held in Potsdam, Germany, in August 2014.
The thirteen papers offered during this ebook have been rigorously reviewed and chosen from a number of submissions and canopy subject matters resembling benchmarks standards and recommendations, Hadoop and MapReduce - within the various context reminiscent of virtualization and cloud - in addition to in-memory, facts new release, and graphs.
Read 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
It corresponds to the area C. For a single phase and a single worker, the elastic overhead is the diﬀerence between the measurement part time and the reference part time. Finally, the sum of the elasticity overheads from every (growth and decline) phase and worker is reported. Using a speciﬁed service price the previously determined time expenditures are converted to the monetary metrics: scaling costs and elasticity costs. The time and monetary costs can be summarized to elastic scalability overhead and elastic scalability costs respectively.
Connected energy monitoring systems enable more efﬁcient utilization of energy resources and reduce global warming. Trafﬁc management systems aim to reduce congestion and decrease automobile accidents. It is clear that such systems can deliver tremendous value and beneﬁts over traditional solutions. IoT has fueled massive investment and growth across the entire spectrum of industry and public sectors. Companies such as Oracle , Cisco , Intel , Microsoft  and others, are investing heavily in IoT initiatives in order to address the computing and infrastructure requirements of this market.
The proposed benchmark targets a multitude of diﬀerent systems, from classical RDBMSs to MapReduce-based systems such as Apache Hive. Contestants c Springer International Publishing Switzerland 2015 T. Rabl et al. ): WBDB 2014, LNCS 8991, pp. 37–44, 2015. 1007/978-3-319-20233-4 5 38 D. Vorona et al. Fig. 1. Business Scenario: Grow cluster as workload grows to ensure performance. Shrink cluster when possible to save costs. g. HiveQL). g. schema-free vs predeﬁned schema) or programming/query languages of diﬀerent types of data management and data processing systems.
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