energy consumption of small database systems

HochschuleBonn-Rhein-Sieg

Energy Consumption of small Database

Systems

Faculty: Computer Science and Engineering, York University (CA)Course: CSE 6421- Advanced Database SystemsInstructor: Prof. Jarek Gryz

Speaker: Benedikt IltisbergerDate: 2010-12-01

HochschuleBonn-Rhein-Sieg Energy Consumption of DB Systems

Presentation Time:35-45 minutes

Questions: I am looking forward to answer your questions during the presentation.

Slide 2


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


What is the Topic? Show a way how to measure the

power consumption of a single query!

Slide 4

Speed

EnergyConsumption


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

Slide 5


Energy Consumption

Slide 6Source: [7]


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

viewSlide 7


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


Energy consumption in the IT

Slide 9Source: [6]


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


Reasons for Decision-Makers1. 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


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

Slide 12


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

Slide 13


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


Information from the Statistic File

Slide 15

Database Context:----------------Parallelism: NoneCPU Speed: 1.889377*107

Comm Speed: 100Buffer Pool size: 6000Sort Heap size: 1000Database Heap size: 900Lock List size: 4096Maximum Lock List: 22Average Applications: 1Locks Available: 28835

4) SORT : (Sort)----------------Cumulative Total Cost: 27541.2Cumulative CPU Cost: 5.39421*109

Cumulative I/O Cost: 23667.2Cumulative Re-Total Cost: 27541.1Cumulative Re-CPU Cost: 5.39388*109

Cumulative Re-I/O Cost: 0Cumulative First Row Cost: 27541.2Estimated Bufferpool Buffers: 585

Detailed information: [9, 10, 11]


Key Metrics in Detail CPU Cost

Instructions needed to execute a query I/O Cost

Number of seeks and page transfers

Bufferpool Buffer Number of used pages

Total Cost Measured in Timerons (proprietary IBM

algo.) Slide 16


The Optimizer’s Access Plan

Slide 17


Slide 18


Slide 19

| /------------------+------------------\ 18000 211.307 299.933 TABLE: TPCDS ^HSJOIN FETCH ITEM ( 8) ( 18) Q2 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 0.105098 Q1 14832.2 8246 0 /-------+--------\ | | 352.257 39.4311 1.9208e+006 300 TBSCAN FETCH TABLE: TPCDS INDEX: SYSIBM ( 10) ( 13) CUSTOMER_DEMOGRAPHICS SQL100701103316930 723.246 39.5703 Q4 Q1 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 TBSCAN SORT 3 ( 12) ( 15)


Parse and Compute

1. Perl script to gather important information Grab all CPU, HDD and Memory operations Sum them up

2. Provide script with following parameters:

Energy consumption of 1 CPU cylce Energy consumption of 1 HDD operation Energy consumption of 1 Memory operation

3. Calculate the query's power consumption

Using the information from no. 1 and no. 2

Slide 20


Perl Script

Slide 21


Assumed DB System CPU[1]

Intel Xeon X7560 @2.266 GHz, 130W 8 Cores, 16 Threads, 24MB L3 Cache

HDD[2]

Seagate Cheetah 15K, 600GB, 16MB Cache I/O Data transfer rate 600MB/s, 16.35W (avg operating

power consumption) RAM[6]

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 Slide 22Source: [6], Page 1233, Section 3.2

#) DIMM Dual In-Line Memory Module

Overall:213.66W


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

customizationSlide 23


How to measure the parameters? II

Slide 24

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


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 processSlide 25


Exemplary Technical Issue:“Samsung DDR3”

Slide 26

Source: [8]


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 wellSlide 27


Optimizing Queries Areas of optimization:

Execution speed (typical parameter) When time is relevant

Resource usage For many concurrent users/queries

New: Energy Consumption

Slide 28


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


Optimizing Queries (Results)

Original Query 1(with index)

Declined Query 1(index deleted)

CPU Cost 4.96336*109 5.39446*109

I/O Cost 8636.78 23667.20

Buffer Pool usage* 68303.99 70816.80

Energy Consumption TBD TBD

Total Cost 12119.3 27541.2

Slide 30

*) Sum of all buffer pool operations for all involved queries.


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


Optimizing Queries (Results)

Original Query 2(with index)

Declined Query 2(index deleted)

CPU Cost 4.51373*107 1.45693*109

I/O Cost 654.55 8495.00

Buffer Pool usage* 3971.55 17575.20

Energy Consumption TBD TBD

Total Cost 1124.43 8772.47

Slide 32

*) Sum of all buffer pool operations for all involved queries.


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


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


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


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 processSlide 36


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, ...

Slide 37


Concluding …This topic is worth for further research!

Slide 38


Reference:1. http://www.intel.com/p/en_US/products/server/processor/xeon7000/

specifications2. http://www.seagate.com/www/en-us/products/enterprise-hard-drive

s/cheetah-15k#tTabContentSpecifications

3. http://www.gruene-it.de/index.php/2010/09/17/ram-stromverbrauch-server/

4. http://www.spec.org/web2009/press/release.html5. http://www.tpc.org/information/press/tpcpress20100707.asp6. 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..

Thank you for your attention! Any questions?

Slide 39

http://www.intel.com/p/en_US/products/server/processor/xeon7000/specifications



http://www.seagate.com/www/en-us/products/enterprise-hard-drives/cheetah-15k%23tTabContentSpecifications



http://www.gruene-it.de/index.php/2010/09/17/ram-stromverbrauch-server/



http://www.spec.org/web2009/press/release.html

http://www.spec.org/web2009/press/release.html

http://www.tpc.org/information/press/tpcpress20100707.asp

http://www.tpc.org/information/press/tpcpress20100707.asp


Reference (cont.):7. http://www.bp.com/liveassets/bp_internet/globalbp/globalbp_uk_en

glish/reports_and_publications/statistical_energy_review_2008/STAGING/local_assets/2010_downloads/statistical_review_of_world_energy_full_report_2010.pdf

8. http://www.samsung.com/global/business/semiconductor/Greenmemory/Applications/ServerStorage/ServerStorage_DDR3.html

9. http://www-01.ibm.com/support/docview.wss?rs=0&uid=swg21207055

10. http://publib.boulder.ibm.com/infocenter/db2luw/v8/index.jsp?topic=/com.ibm.db2.udb.doc/admin/r0000295.htm

11. http://www.ibm.com/developerworks/data/library/techarticle/0212wieser/index.html

12. http://www.freepatentsonline.com/y2009/0281986.html13. http://www.gracenote.com/14. http://www.freedb.org15. R. Ramahrishnan, J. Gehrke, „Database Management Systems”,

Third Edition, McGraw-Hill, Boston, 2003.

Slide 40

http://www.bp.com/liveassets/bp_internet/globalbp/globalbp_uk_english/reports_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/Applications/ServerStorage/ServerStorage_DDR3.html



http://www-01.ibm.com/support/docview.wss?rs=0&uid=swg21207055

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.freepatentsonline.com/y2009/0281986.html

http://www.gracenote.com/

http://www.gracenote.com/

http://www.freedb.org/

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