1. Towards Eco-friendly Database Management Systems W. Lang, J. M. Patel (U Wisconsin), CIDR 2009 Shimin Chen Big Data Reading Group

2. Introduction Energy consumption is important for data centers:  2005: 1.2% of total US energy consumption is attributed to powering and cooling servers, ~ $2.7B  If current methods for powering data centers continue, the consumption will nearly double by 2011 For DBMS:  Previously large ignored energy efficiency  Must start considering energy as a critical metric

3. This paper: ecoDB New project: energy efficient data processing techniques Two broad classes of techniques:  “global”: change how entire system is managed or used E.g. job scheduling  “local”: improve methods of processing data at individual nodes (focus of the paper)

4. Idea

5. Two Questions (1) “How does a system generate graphs as shown in Figure 1?”  DMBS must know HW capabilities and operating characteristics  Accurately estimate / continuously measure energy consumption (2) “How can such a graph be used?”  Systematic method to change settings  Service level agreements (SLAs) This paper focuses on mechanisms for creating graphs

6. Outline Introduction Processor Voltage/Frequency Control (PVC) Improved Query Energy-efficiency by Introducing Explicit Delays (QED) Opportunities for Energy Efficiency Summary

7. Techniques CPU freq = front side bus (FSB) freq * CPU multiplier DVFS (dynamic voltage and frequency scaling)  Each p-state defines a CPU multiplier  CPU voltage is based on CPU multiplier Under-clocking (Focus of this paper)  Reduce FSB freq  Finer granularity  Also changes RAM freq

8. System Under Test System components:  ASUS P5Q3 Deluxe Wifi-AP motherboard  Intel Core2-Duo E8500  2×1GKingston DDR3 main memory  ASUS GeForce 8400GS 256M  Western Digital Caviar SE16 320G SATA disk  Power supply unit (PSU): a Corsair VX450W PSU System power draw measured by a Yokogawa WT210 unit (suggested by SPEC Power benchmark) MS Windows Server 2008 JDBC (Java 1.6)

9. Power CPU power sensors on motherboard:  ASUS motherboard has an EPU processor that directly measures the CPU power.  ASUS P5Q3 Deluxe 6-Engine software displays information gathered from this hardware sensor. Current CPU wattage displayed in GUI:  The authors sample the GUI every second  Compute CPU joules using the average CPU wattage and the execution time of a workload

10. Component powers No hard disk, no operating system Focusing on CPU power:  CPU power consumption is often about 25% of the total system power consumption in the experiments

11. DB test Workload  Use a commercial DBMS and MySQL 5.1.28  TPC-H (ad-hoc decision support), scale factor 1.0 (1GB data)  Only run Query 5: six table join and a group by  A run consists of ten queries with different parameters FSB underclocking (allowed by ASUS 6-engine software)  Stock (normal)  Reduce FSB freq by 5%, 10%, and 15% CPU voltage downgrade  “small” and “medium” downgrade 7 settings:  Stock, 3 FSB freq reductions X 2 CPU voltage downgrades

12. Equal Energy delay product

13. With the same voltage level, larger frequency the better EDP Equal Energy delay product

14. Theoretical Modeling EDP= joules x times = power x time 2 = power / freq 2 Power=CV 2 F EDP = CV 2 /F

15. Disk Energy Measured separately for stock setting Warm database  CPU: 1228.7 Joules  Disk: 214.7 Joules Cold database  CPU: 2146.0 Joules  Disk: 1135.4 Joules

16. Outline Introduction Processor Voltage/Frequency Control (PVC) Improved Query Energy-efficiency by Introducing Explicit Delays (QED) Opportunities for Energy Efficiency Summary

17. Idea Explicitly delay queries look for commonalities among multiple queries Group multiple queries into a single query After execution, split query results

18. Setting DB clients repeatedly issue single table select queries with different selection predicate.  For example: SELECT * FROM lineitem WHERE l_quantity=X DBMS processes one query at a time QED: buffer queries in a queue, merge them, send the merged query, split results In the experiments, X is different for the queries, so no overlaps

19. As batch size increases, diminishing decrease in energy consumption.

20. Outline Introduction Processor Voltage/Frequency Control (PVC) Improved Query Energy-efficiency by Introducing Explicit Delays (QED) Opportunities for Energy Efficiency Summary

21. Opportunities in (DBMS) Software Traditional DB investigations into improving query response times Energy vs. performance tradeoffs  Operator-level: rethink join algorithms  Query-level: energy-efficient query plans  Workload management per server  Workload management for the entire collection of servers: scheduling and using techniques to turn entire servers off

22. Summary Energy-efficient data processing Studied two techniques  Processor Voltage/Frequency Control (PVC)  Improved Query Energy-efficiency by Introducing Explicit Delays (QED) Designing a DBMS to balance the response time vs. energy consumption opens a wide range of research issues