By Tilmann Rabl, Nambiar Raghunath, Meikel Poess, Milind Bhandarkar, Hans-Arno Jacobsen, Chaitanya Baru (eds.)
This e-book constitutes the completely refereed joint lawsuits of the 3rd and Fourth Workshop on substantial information Benchmarking. The 3rd WBDB used to be held in Xi'an, China, in July 2013 and the Fourth WBDB was once held in San José, CA, united states, in October, 2013. The 15 papers offered during this ebook have been rigorously reviewed and chosen from 33 displays. They specialise in sizeable facts benchmarks; functions and situations; instruments, structures and surveys.
Read or Download 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 PDF
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Extra info 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
2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. BigDataBench : A Big Data Benchmark Suite. cn/BigDataBench Protocol buﬀers. com/p/protobuf/ RDMA for Apache Hadoop. edu Remote Rrocedure Call. org/wiki/Remote procedure call TPC Benchmark H - Standard Speci cation. : Malstone: towards a benchmark for analytics on large data clouds. : Implementing remote procedure calls. ACM Trans. Comput, Syst. : Benchmarking cloud serving systems with YCSB. : MapReduce: simpliﬁed data processing on large clusters.
First, it may block BGCi until the referenced data item becomes available. Second, it may return an error to BGCi to generate a diﬀerent member/resource id and try again. Third, it may simply abort this action and generate a new action all together. We intend to quantify the tradeoﬀ associated with these three possibilities and their impact on both the distribution of requests and the benchmarking framework. – What is the scalability characteristic of the proposed technique? The proposed request generation technique requires diﬀerent BGClients to exchange messages to lock and unlock data items and to determine the feasibility of actions.
This step involves multiple experiments issuing the given workload against the data store to select the smallest duration that results in a steady system behavior deﬁned as one whose resource utilization and observed throughput do not change in time. For such a system the recently observed behavior will continue to hold into the future. 5 Validation A novel feature of BG is its ability to quantify the amount of unpredictable data (stale, inconsistent, erroneous) produced by a data store. A data store may produce unpredictable data for a variety of reasons.
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 by Tilmann Rabl, Nambiar Raghunath, Meikel Poess, Milind Bhandarkar, Hans-Arno Jacobsen, Chaitanya Baru (eds.)