Towards Eco-friendly Database Management Systems W. Lang, J. M. Patel (U Wisconsin), CIDR 2009...
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Transcript of Towards Eco-friendly Database Management Systems W. Lang, J. M. Patel (U Wisconsin), CIDR 2009...
![Page 1: Towards Eco-friendly Database Management Systems W. Lang, J. M. Patel (U Wisconsin), CIDR 2009 Shimin Chen Big Data Reading Group.](https://reader036.fdocuments.in/reader036/viewer/2022081513/56649dc65503460f94aba60b/html5/thumbnails/1.jpg)
Towards Eco-friendly Database Management SystemsW. Lang, J. M. Patel (U Wisconsin), CIDR 2009
Shimin Chen
Big Data Reading Group
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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
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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)
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Idea
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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
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Outline Introduction Processor Voltage/Frequency Control (PVC) Improved Query Energy-efficiency by
Introducing Explicit Delays (QED) Opportunities for Energy Efficiency Summary
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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
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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)
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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
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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
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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
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Equal Energy delay product
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With the same voltage level, larger frequency the better EDP
Equal Energy delay product
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Theoretical Modeling EDP= joules x times = power x time2
= power / freq2
Power=CV2F EDP = CV2/F
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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
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Outline Introduction Processor Voltage/Frequency Control (PVC) Improved Query Energy-efficiency by
Introducing Explicit Delays (QED) Opportunities for Energy Efficiency Summary
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Idea Explicitly delay queries look for commonalities among multiple queries Group multiple queries into a single query After execution, split query results
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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
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As batch size increases, diminishing decrease in energy consumption.
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Outline Introduction Processor Voltage/Frequency Control (PVC) Improved Query Energy-efficiency by
Introducing Explicit Delays (QED) Opportunities for Energy Efficiency Summary
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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
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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