Towards Eco-friendly Database Management SystemsW. Lang, J. M. Patel (U Wisconsin), CIDR 2009

Shimin Chen

Big Data Reading Group

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

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)

Idea

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

Outline Introduction Processor Voltage/Frequency Control (PVC) Improved Query Energy-efficiency by

Introducing Explicit Delays (QED) Opportunities for Energy Efficiency Summary

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

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)

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

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

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

Equal Energy delay product

With the same voltage level, larger frequency the better EDP

Equal Energy delay product

Theoretical Modeling EDP= joules x times = power x time2

= power / freq2

Power=CV2F EDP = CV2/F

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

Outline Introduction Processor Voltage/Frequency Control (PVC) Improved Query Energy-efficiency by

Introducing Explicit Delays (QED) Opportunities for Energy Efficiency Summary

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

Setting DB clients repeatedly issue single table select

queries with different selection predicate. For example:

SELECT *FROM lineitemWHERE 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

As batch size increases, diminishing decrease in energy consumption.

Outline Introduction Processor Voltage/Frequency Control (PVC) Improved Query Energy-efficiency by

Introducing Explicit Delays (QED) Opportunities for Energy Efficiency Summary

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

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