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Datenbank Spektrum 14(3) - November 2014

https://dl.gi.de/handle/20.500.12116/11569
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Auflistung Datenbank Spektrum 14(3) - November 2014 nach Schlagwort "Energy efficiency"

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  • Zeitschriftenartikel
    HyPer Beyond Software: Exploiting Modern Hardware for Main-Memory Database Systems
    (Datenbank-Spektrum: Vol. 14, No. 3, 2014) Funke, Florian; Kemper, Alfons; Mühlbauer, Tobias; Neumann, Thomas; Leis, Viktor
    In this paper, we survey the use of advanced hardware features for optimizing main-memory database systems in the context of our HyPer project. We exploit the virtual memory management for snapshotting the transactional data in order to separate OLAP queries from parallel OLTP transactions. The access behavior of database objects from simultaneous OLTP transactions is monitored using the virtual memory management component in order to compact the database into hot and cold partitions. Utilizing many-core NUMA-organized database servers is facilitated by the morsel-driven adaptive parallelization and partitioning that guarantees data locality w.r.t. the processing core. The most recent Hardware Transactional Memory support of, e.g., Intel’s Haswell processor, can be used as the basis for a lock-free concurrency control scheme for OLTP transactions. Finally, we show how heterogeneous processors of “wimpy” devices such as tablets can be utilized for high-performance and energy-efficient query processing.
  • Zeitschriftenartikel
    WattDB - A Journey towards Energy Efficiency
    (Datenbank-Spektrum: Vol. 14, No. 3, 2014) Schall, Daniel; Härder, Theo
    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.