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WattDB - A Journey towards Energy Efficiency

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Abstract

Due to their narrow power spectrum between idle and full utilization [2], satisfactory energy efficiency of servers can only be reached in the peak-performance range, whereas energy efficiency obtained for lower activity levels is far from being optimal. Hence, this hardware property obviates a desired energy proportionality or minimal energy use for the entire range of system utilization. To approximate energy proportionality for all activity levels, we developed various versions of WattDB, a distributed DBMS, which runs on a dynamic cluster of wimpy computing nodes. In this survey, we sketch important design decisions and implementation steps towards the final state of WattDB. For these reasons, we discuss our findings on a cluster with dedicated storage nodes and static data allocation, on dynamic data repartitioning and allocation, and on a dynamic cluster where each node can serve as storage and processing node in a symmetric way. Our experiments show that WattDB dynamically adjusts to the workload present and reconfigures itself to satisfy performance demands while keeping its energy consumption at a minimum. Finally, we compare the performance and energy results of the WattDB software running on the cluster of wimpy nodes with that of a brawny server.

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Notes

  1. It is also possible to turn nodes completely off, resulting in about 0.5 W of power use. But then, start-up times are much higher than just waking up from suspend-to-RAM.

  2. The initial version of the core engine was developed for the 2010 SIGMOD Programming Contest, where it won the second prize [5].

  3. Pipelining operators can process one record at a time and emit the result, e.g., projection operators.

  4. Blocking operators need to fetch all records from the underlying operators, before emitting the first result record.

  5. rDMA = remote direct memory access

  6. Note that 100 % utilization is not equivalent in both systems, the big server is possibly much faster than the cluster.

  7. We also noticed, that OLTP runtimes are significantly lower compared to our experiments with OLAP in parallel.

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Acknowledgments

The research project Energy-Efficient Processing in Database Systems was partly funded by the German Research Foundation (DFG) under the contracts HA 1286/8-1 and HA 1286/8-2. Finally, we would like to thank the anonymous reviewers for their insightful reviews.

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  1. University of Kaiserslautern, P.O. Box 3049, 67653, Kaiserslautern, Germany

    Daniel Schall & Theo Härder

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  1. Daniel Schall
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  2. Theo Härder
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Correspondence to Daniel Schall.

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Schall, D., Härder, T. WattDB - A Journey towards Energy Efficiency. Datenbank Spektrum 14, 183–198 (2014). https://doi.org/10.1007/s13222-014-0168-8

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  • DOI: https://doi.org/10.1007/s13222-014-0168-8

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