1. Daniel Schall, Volker Höfner, Prof. Dr. Theo Härder TU Kaiserslautern

2. Outline Energy efficiency in database sytems Multi-Core vs. Cluster WattDB Recent Current Work Future 2

3. In-memory technology Electricity Cost Motivation More and more data Bigger servers 3

4. Power Breakdown Load between 0 – 50 % Energy Consumption: 50 – 90%! ‘‘Analyzing the Energy Efficiency of a Database Server“, D. Tsirogiannis, S. Harizopoulos, and M. A. Shah SIGMOD 2010 ‘‘Distributed Computing at Multi-dimensional Scale“, Alfred Z. Spector Keynote on MIDDLEWARE 2008 4

5. Memory and Storage 0% 5 10 15 20 25 20%40%60%80%100% Device utilization Power (Watt) + + + + + + + + + + Four HDDs (1 TB) Four SSDs (256 GB) What about main-memory DBMSs from an energy-efficiency perspective? + 35 40 + + +++++ + Four 4GB DRAMs (16 GB) 30 Device utilization Power (Watt) 2400 2500 250 4GB DRAMs (1 TB) 0% 10 20 20%40%60%80%100% HDDs (1 TB) SSDs (1 TB) 30 ~ ~ Factor 100! 5

6. Growth of Main Memory makes it worse % 20406080100 System utilization P ower (Watt) 0 % 20 40 60 80 100 power@utilization energy- proportional behavior In-memory data management assumes continuous peak loads! Energy consumption of memory linearly grows with size and dominates all other components across all levels of system utilization

7. Mission: Energy-Efficiency! Energy cost > HW and SW cost Energy Efficiency = ‚‚Green IT‘‘ Work Energy Consumption 7

8. Average Server Utilization Google Servers: load at about 30 % SPH AG: load between 5 and 30 % 8

9. Energy Efficiency - Related Work Software Delaying queries Optimize external storage access patterns Force sleep states „Intelligent“ data placement  Narrow approaches  Only small improvements 9 Hardware Sleep states Optimize energy consumption when idle Select energy-efficient hardware Dynamic Voltage Scaling

10. Goal: Energy-Proportionality % 20406080100 System utilization P ower (Watt) 0 % 20 40 60 80 100 power@utilization energy- proportional behavior 1) reduce idle power consumption 2) eliminate disproportional energy consumption 1 2

11. From Multi-Core to Multi-Node CPU Cache Main memory 1Gb ethernet switch Core L1 Cache L2 Cache L3 Cache 11 % 20406080100 System utilization P ower (Watt) power@utilization 0 % 20 40 60 80 100

12. A dynamic cluster of wimpy nodes energy-proportional DBMS Load Time 12

13. Cluster Overview Light-weighted nodes, low-power hardware Each node Intel Atom D510 CPU 2 GB DRAM 80plus Gold power supply 1Gbit Ethernet interconnect 23 W (idle) - 26 W (100% CPU) 41 W (100% CPU + disks) Considered Amdahl-balanced Scale down the CPUs to the disks and network! 13

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15. Shared Disk AND Shared Nothing Physical hardware layout: Shared Disk every node can access every page local vs. remote latency Logical implementation: Shared Nothing: data is mapped to node n:1 exclusive access transfer of control Combine the benefits of both worlds!  15

16. Recent Work SIGMOD 2010 Programming Contest First prototype distributed DBMS BTW 2011 Demo Track Master node powering cluster up/down acc. to load SIGMOD 2011 Demo Track Energy-proportional query processing 16

17. Current Work Incorporate GPU-Operators improved energy-efficiency? more tuples/Watt? Monitoring & Load Forecasting For management decisions act instead of react Energy-Proportional Storage storage needs vs. processing needs 17

18. Future Work Policies for powering up / down nodes Load distribution and balancing among nodes Which use cases fit for the proposed architecture, which don‘t? Alternative hardware configurations Heterogeneous HW environment SSDs, other CPUs Energy-efficient self-tuning 18

19. Node3 Current Work 19 Table Partition Node1Node2 Partition

20. Node3 Future Work 20 Table Partition Node1Node2 Partition Node2

21. Conclusion Energy consumption matters! Current HW is not energy-proportional Systems most of the time at 20% - 50% utilization WattDB as a prototype for an energy-proportional DBMS Several challenges ahead 21

22. Thank You! Energy Proportionality on a Cluster Scale 22