Hochschule
Bonn-Rhein-Sieg
Energy Consumption of
small Database Systems
Faculty:
Course:
Instructor:
Computer Science and Engineering, York University (CA)
CSE 6421- Advanced Database Systems
Prof. Jarek Gryz
Speaker:
Date:
Benedikt Iltisberger
2010-12-01
Hochschule
Bonn-Rhein-Sieg

Presentation Time:


Energy Consumption of DB Systems
35-45 minutes
Questions:

I am looking forward to answer your
questions during the presentation.
Slide 2
Hochschule
Bonn-Rhein-Sieg
Energy Consumption of DB Systems
Agenda of the Presentation

What is the topic?

Why is it important?

How to solve the problem?

What do you have to keep in mind?

Does it work in the real world?

What is the conclusion?

What can be done in the future?
Slide 3
Hochschule
Bonn-Rhein-Sieg
Energy Consumption of DB Systems
What is the Topic?

Show a way how to measure the power
consumption of a single query!
Speed
Energy
Consumption
Slide 4
Hochschule
Bonn-Rhein-Sieg
Energy Consumption of DB Systems
What are the Scientific Challenges?

Never done before[12]

Could/should be implemented in any DBMS

Multiple factors need to be considered

Upcoming and important research area
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Bonn-Rhein-Sieg
Energy Consumption of DB Systems
Energy Consumption
Source: [7]
Slide 6
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Bonn-Rhein-Sieg
Energy Consumption of DB Systems
Why is it worth to focus on
energy saving?

Saving the environment

Growing market  Job opportunity

Saving costs for companies

Energy bills are rising quickly

Very important research area in Europe

Interesting from the technical point of view
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Energy Consumption of DB Systems
Benchmarking Organizations

SPEC


“New SPECweb2009 benchmark adds ability
to measure power consumption of web
servers” (June 5, 2009)[4]
TPC

“Transaction Processing Performance Council
Announces First Results for its TPC-Energy
Specification” (July 7, 2010)[5]
Slide 8
Hochschule
Bonn-Rhein-Sieg
Energy Consumption of DB Systems
Energy consumption in the IT
Source: [6]
Slide 9
Hochschule
Bonn-Rhein-Sieg
Energy Consumption of DB Systems
What have DBs to do with that?

Core Service

Delivering data for any business process

Store the foundation of a company

Need to be accessible 24/7

Today integrated even in small projects

Data warehouses and Data marts

Long running energy consuming queries
Slide 10
Hochschule
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Energy Consumption of DB Systems
Reasons for Decision-Makers
1.
Reducing costs
2.
Keep up a good image (Green IT)
1.
Innovation
2.
Keep up with the times
3.
Being faster than competitors
3.
Depending on the area of work
4.
Additional company policies
Slide 11
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Energy Consumption of DB Systems
Idea of this Proposal

Estimate the energy consumption of a
single query to





Compare queries in terms of efficiency
Optimize queries for energy saving
Get energy information in real time
Teach the optimizer to do this task
Green IT initiative
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Hochschule
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Energy Consumption of DB Systems
How to achieve this goal?

DBMS (e.g. DB2) can generate reports
about executed queries containing:


Original statement -> optimized statement
Access Plan with many details like:



CPU Cost
 CPU
I/O Cost
 HDD
Bufferpool Buffers  Main Memory
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Energy Consumption of DB Systems
What information is given?

IBM DATABASE 2 Explain Table Format Tool

DB2 version: 09.07.3

Detailed information about queries including

Access Plan (also graphical)

Original query  Optimized Query

Detailed information about the different costs

Detailed information about tables, indexes, …
Slide 14
Hochschule
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Energy Consumption of DB Systems
Information from the Statistic File
Database Context:
---------------Parallelism:
CPU Speed:
Comm Speed:
Buffer Pool size:
Sort Heap size:
Database Heap size:
Lock List size:
Maximum Lock List:
Average Applications:
Locks Available:
None
1.889377*107
100
6000
1000
900
4096
22
1
28835
4) SORT : (Sort)
---------------Cumulative Total Cost:
Cumulative CPU Cost:
Cumulative I/O Cost:
Cumulative Re-Total Cost:
Cumulative Re-CPU Cost:
Cumulative Re-I/O Cost:
Cumulative First Row Cost:
Estimated Bufferpool Buffers:
27541.2
5.39421*109
23667.2
27541.1
5.39388*109
0
27541.2
585
Detailed information: [9, 10, 11]
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Energy Consumption of DB Systems
Key Metrics in Detail

CPU Cost


I/O Cost


Number of seeks and page transfers
Bufferpool Buffer


Instructions needed to execute a query
Number of used pages
Total Cost

Measured in Timerons (proprietary IBM algo.)
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Energy Consumption of DB Systems
The Optimizer’s Access Plan
Slide 17
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Energy Consumption of DB Systems
Slide 18
Hochschule
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|
18000
TABLE: TPCDS
ITEM
Q2
Energy Consumption of DB Systems
/------------------+------------------\
211.307
299.933
^HSJOIN
FETCH
(
8)
( 18)
26913.4
30.4522
23078.2
4
/----------+-----------\
/---+----\
13889.9
29221.1
300
300
NLJOIN
TBSCAN
IXSCAN
TABLE:
TPCDS
(
9)
(
17)
(
19)
PROMOTION
17710.9
9200.94
14832.2
8246
/-------+--------\
|
352.257
39.4311
1.9208e+006
TBSCAN
FETCH
TABLE: TPCDS
( 10)
( 13)
CUSTOMER_DEMOGRAPHICS
723.246
39.5703
Q4
656
32.9996
|
/----+-----\
352.257
39.4311
2.8804e+006
SORT
RIDSCN
DP-TABLE: TPCDS
( 11)
( 14)
STORE_SALES
723.246
7.59231
Q5
656
1
|
|
352.257
39.4311
0.105098
0
|
300
INDEX: SYSIBM
SQL100701103316930
Q1
Q1
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Energy Consumption of DB Systems
Parse and Compute
1.
Perl script to gather important information


2.
Provide script with following parameters:



3.
Grab all CPU, HDD and Memory operations
Sum them up
Energy consumption of 1 CPU cylce
Energy consumption of 1 HDD operation
Energy consumption of 1 Memory operation
Calculate the query's power consumption

Using the information from no. 1 and no. 2
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Energy Consumption of DB Systems
Perl Script
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Energy Consumption of DB Systems
Assumed DB System

CPU[1]



HDD[2]



Overall:
213.66W
Seagate Cheetah 15K, 600GB, 16MB Cache
I/O Data transfer rate 600MB/s, 16.35W (avg operating power
consumption)
RAM[6]


Intel Xeon X7560 @2.266 GHz, 130W
8 Cores, 16 Threads, 24MB L3 Cache
4GB DDR3 RAM (2 * 2GB DIMM#) with 9W per DIMM  18W
Additional Hardware[6]:

30% of average power consumption of other components which
leads to (130W + 16.35W + (2 * 9W)) * 0.3 = 49,305W
Source: [6], Page 1233, Section 3.2
#) DIMM  Dual In-Line Memory Module
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Energy Consumption of DB Systems
How to measure the parameters? I

CPU



Peak power consumption (p) of the manufacturer (Watts
per hour)
Total MIPS per hour (c)
Calculate avg power consumption (a)


p/c=a
Problems


Slide calculation flaws may end in high differences
Many instructions take more the one cycle


This also depends on the CPU type
Current multicore architecture need special customization
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Energy Consumption of DB Systems
How to measure the parameters? II

HDD



Peak power consumption (p) of the manufacturer
(Watts per hour)
Calculate the max. avg I/O operations per hour (c)
Calculate avg power consumption per I/O and hour (a)


p/c=a
Problems


Different energy consumption for read & write
RAID-n or other storage systems
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Energy Consumption of DB Systems
How to measure the parameters? III

Main Memory



Peak power consumption (p) of the manufacturer
(Watts per hour)
Calculate the max. page change rate per hour (c)
Calculate avg power consumption per page change (a)


p/c=a
Problems:


Very complicated and inprecise
Strongly dependent on memory architecture and
manufacturing process
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Energy Consumption of DB Systems
Exemplary Technical Issue:
“Samsung DDR3”
Source: [8]
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Energy Consumption of DB Systems
Automation of Data Collection
about CPU, HDD, RAM

Proposal: “energyDB”

Name, Modell, Speed, Energy consumption, …

Online service to provide needed information

Comparable with CDDB[13] or freedb[14]

Useful for other projects as well
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Hochschule
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Energy Consumption of DB Systems
Optimizing Queries

Areas of optimization:

Execution speed (typical parameter)


Resource usage


When time is relevant
For many concurrent users/queries
New: Energy Consumption
Slide 28
Hochschule
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Energy Consumption of DB Systems
Optimizing Queries No. 1

Original Query:
SELECT i_item_id,
CAST(avg(CAST(ss_quantity AS DECFLOAT)) AS DECIMAL(10,6))
agg1, CAST(avg(ss_list_price) AS DECIMAL(10,6)) agg2,
CAST(avg(ss_coupon_amt) AS DECIMAL(10,6)) agg3,
CAST(avg(ss_sales_price) AS DECIMAL(10,6)) agg4
FROM tpcds.store_sales, tpcds.customer_demographics, tpcds.date_dim,
tpcds.item, tpcds.promotion
WHERE ss_sold_date_sk = d_date_sk and ss_item_sk = i_item_sk and ss_cdemo_sk
= cd_demo_sk and ss_promo_sk = p_promo_sk and cd_gender = 'M' and
cd_marital_status = 'S' and cd_education_status = 'College' and
(p_channel_email = 'N' or p_channel_event = 'N') and d_year = 2000
GROUP BY i_item_id
ORDER BY i_item_id
FETCH FIRST 100 ROWS ONLY
Slide 29
Hochschule
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Energy Consumption of DB Systems
Optimizing Queries (Results)
Original Query 1
(with index)
CPU Cost
I/O Cost
Buffer Pool usage*
Energy Consumption
Total Cost
Declined Query 1
(index deleted)
4.96336*109
5.39446*109
8636.78
23667.20
68303.99
70816.80
TBD
TBD
12119.3
27541.2
*) Sum of all buffer pool operations for all involved queries.
Slide 30
Hochschule
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Energy Consumption of DB Systems
Optimizing Queries No. 2

Original Query:
SELECT i_item_id,
CAST(avg(CAST(ss_quantity AS DECFLOAT)) AS DECIMAL(10,6)) agg1,
CAST(avg(ss_list_price) AS DECIMAL(10,6)) agg2,
CAST(avg(ss_coupon_amt) AS DECIMAL(10,6)) agg3,
CAST(avg(ss_sales_price) AS DECIMAL(10,6)) agg4
FROM tpcdsnp.store_sales, tpcdsnp.customer_demographics, tpcdsnp.date_dim,
tpcdsnp.item, tpcdsnp.promotion
WHERE ss_sold_date_sk = d_date_sk and ss_item_sk = i_item_sk and
ss_cdemo_sk = cd_demo_sk and ss_promo_sk = p_promo_sk and cd_gender = 'M’
and cd_marital_status = 'S' and cd_education_status = 'College' and
(p_channel_email = 'N' or p_channel_event = 'N') and d_year = 2000
GROUP BY i_item_id
ORDER BY i_item_id
FETCH FIRST100 ROWS ONLY
Slide 31
Hochschule
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Energy Consumption of DB Systems
Optimizing Queries (Results)
Original Query 2
(with index)
CPU Cost
I/O Cost
Buffer Pool usage*
Energy Consumption
Total Cost
Declined Query 2
(index deleted)
4.51373*107
1.45693*109
654.55
8495.00
3971.55
17575.20
TBD
TBD
1124.43
8772.47
*) Sum of all buffer pool operations for all involved queries.
Slide 32
Hochschule
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Energy Consumption of DB Systems
Optimizing Queries (Problems)

Better performance but higher energy
usage:



Precomputation for faster response times
Materialized Views
CPU


Cheap in terms of time consumption but
Expensive in terms of energy consumption
Slide 33
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Energy Consumption of DB Systems
Conclusions I

Reducing costs is always welcome in companies

High need for this research in the long term

Due to corporate identity and rising energy costs

Environmental saving

Query optimization does not necessarily
lead to power saving

Proposed technique can be added to the optimizer
Slide 34
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Energy Consumption of DB Systems
Conclusions II

Cons

You need to measure the single components in the
first place

Not easily applicable to complex systems

Accuracy must be determined

Checking results with hardware based measurement data
How to measure VMs?
Slide 35
Hochschule
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Energy Consumption of DB Systems
Future Work I

Add power consumption optimization techniques
directly to the optimizer

IBM seems to be working on that[12]

Optional output of estimate power consumption

Improve calculation techniques


Even slide inaccuracy can have a big impact

RAM usage is pretty inaccurate at the moment
Automization of the process
Slide 36
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Energy Consumption of DB Systems
Future Work II

Implementation of complex Dataware House
systems or for distributed database systems

Inaccuracy with measuring multi CPU systems

„energyDB“ web service implementation

Impact of hardware power saving

GPU for databases, SSD, Dynamic voltage scaling, ...
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Energy Consumption of DB Systems
Concluding …
This topic is worth for further research!
Slide 38
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Energy Consumption of DB Systems
Thank you for your attention!
Any questions?
Reference:
1.
2.
3.
4.
5.
6.
http://www.intel.com/p/en_US/products/server/processor/xeon7000/specificat
ions
http://www.seagate.com/www/en-us/products/enterprise-harddrives/cheetah-15k#tTabContentSpecifications
http://www.gruene-it.de/index.php/2010/09/17/ram-stromverbrauch-server/
http://www.spec.org/web2009/press/release.html
http://www.tpc.org/information/press/tpcpress20100707.asp
M. Poess, R. O. Nambiar, Energy Cost, “The Key Challenge of Today’s Data
Centers: A Power Consumption Analysis of TPC-C results”,Oracle
Corporation and Hewlett-Packard Company, N.A..
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Hochschule
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Energy Consumption of DB Systems
Reference (cont.):
7.
8.
9.
10.
11.
12.
13.
14.
15.
http://www.bp.com/liveassets/bp_internet/globalbp/globalbp_uk_english/repo
rts_and_publications/statistical_energy_review_2008/STAGING/local_assets
/2010_downloads/statistical_review_of_world_energy_full_report_2010.pdf
http://www.samsung.com/global/business/semiconductor/Greenmemory/Appl
ications/ServerStorage/ServerStorage_DDR3.html
http://www-01.ibm.com/support/docview.wss?rs=0&uid=swg21207055
http://publib.boulder.ibm.com/infocenter/db2luw/v8/index.jsp?topic=/com.ibm.
db2.udb.doc/admin/r0000295.htm
http://www.ibm.com/developerworks/data/library/techarticle/0212wieser/index
.html
http://www.freepatentsonline.com/y2009/0281986.html
http://www.gracenote.com/
http://www.freedb.org
R. Ramahrishnan, J. Gehrke, „Database Management Systems”, Third
Edition, McGraw-Hill, Boston, 2003.
Slide 40