Evaluation of Data Placement Method in Database Run-Time Processing Considering Energy Saving and...

Evaluation of Data Placement Method in Database Run-Time Processing Considering Energy Saving and

Application Performance

Naho IIMURA† Norifumi NISHIKAWA‡ Miyuki NAKANO†† 　 Masato OGUCHI†

†Ochanomizu University 　‡ Hitachi, Ltd., Yokohama Research Laboratory

†† Shibaura Institute of Technology

NOVEMBER 3rd, 2014 IGCC14 GPCDP Workshop

OUTLINE

• Introduction• Previous Work• Our Proposed Method and Evaluation Plan

– Data Placement Control• Experimental• Evaluation Results and Discussion• Conclusion and Future Works

November 3rd, 2014 IGCC14 GPCDP Workshop 2

Background(1/2)

• The amount of digital data is increased rapidly.• The scale of datacenters(DC) has become

larger.→ 　 The management use cost of DC has

become larger.


•Operating costs of machines•Amortization of buildings and cooling equipment•Electricity charge

Energy saving of DC is drawing attention NOW

Background(2/2)


63%14%

13%

5%5%

Typical Data Center Energy Consumption Rate

Cooling

Servers

Storages

Network Hardware

Power Conversion

Reducing the power consumption of storage is an efficient way to save energy in datacenters

Research Objective

• Conventional Methods– Performance enhancement of cooling facilities– Performance enhancement of power efficiency and more...

• Our Proposed Method– The power saving of storage

by efficient management of data


have been already addressed

Goal of Research

To achieve …• Analyze of Power Consumption and System

Performance of the TPC-H Runtime• Propose Storage Power Saving Method


Reducing Energy Consumption of Storage while Minimizing the Deterioration

of Database Application Performance

TPC-H is one of the standard benchmark tool of database application

Previous Works

• Combine application level I/O and device level I/O• Extract Logical I/O pattern• Select appropriate power saving methods


Runtime Power Saving Framework

Application level I/O Monitor

Buffer based Power Saving Methods

• Pre-load• Write Delay

Storage Device level I/O Monitor

Control MAID• Power ON/OFF

DB Buffer

DB EngineApplications

File System BufferDevice Driver

Storage Buffer

Storage Devices

Energy Efficient Storage Management Cooperated with Large Data Intensive Applications Nishikawa ,et al. (IEEE ICDE 2012)

Present Work Direction

• Experiment Environment– Large scale Unit → Single Unit

• For the storage power saving of TPC-H runtime– Calculate the Break-Even Time– Evaluate our proposed method focusing on the

Service Level Agreement (SLA)• Data Placement Control


To analyze power saving more detail in

more small unit

Our Proposed Method

• Data Placement Control– Based on I/O frequency on Run-Time applications, Modify

the data placement

Change to the Standby-mode when disk is not being used 　Finally, power consumption can be reduced more


Long I/O interval

*The using frequency of data

＜＜

Evaluation Plan

The number of used HDDs are 3 - 10

Evaluation Environment


CPU 4 cores * 2

Memory 8GBytes

HDD 3TB *11

OS Cent OS 5.10 64bit

DBMS HITACHI HiRDBSingle ServerVersion 9

Benchmark

TPC-H (SF=10)

Power Meter

YOKOGAWADigital Power Meter

Local PC

Remote Server

Power Meter

PC to Control Power Meter

Ochanomizu Univ.

Univ. of Tokyo

Break-Even Time• Break-Even Time is the amount of time to continue the Standby state

that satisfies the following condition.• The amount of energy needed for the spinup or spindown of the disk

is equal to that of the energy saved by remaining in the Standby state during Break-Even Time.


24 seconds

To reduce power consumption by using the Standby state, an I/O interval of approximately 24 seconds or more is needed.

The Investigation of I/O Frequency

• Investigate the I/O frequency of data, tables and indexes of during runtime processing of TPC-H queries.– Divide LINE ITEM Table and Indexes into 10 Buffers.– I/O interval is obtained every second.– The survey period is from the beginning to the end

of the query execution.– Focus on the actual number of times the READs

from the obtained data items.


Classified Data From I/O Frequency

• Classified the data into two types:– the data that HAVE actual number of instances of

READ or NOT

• In next page, I/O frequency of partitioned data is focused


I/O Frequency of Partitioned Data


•Partitioned tables are used in a numerical order•A longer I/O intervals is obtainable by placing partitioned data with near numbers on the same disk

First ExperimentData placement control with table partitioning

• Divide LINE ITEM Table and Indexes into 10 buffers– To use more flexible arrangement of data– Use the Hash Partitioning

• Place all data on 3 HDDs– Design 2 patterns of placement about partitioned data.

• Compare 2 patterns of placement during runtime processing of TPC-H– Times of I/O Interval– Power Consumption and Response Time


Two Patterns of Data Placement

A) Partitioned data is placed by round-robin placement

B) Partitioned data with near numbers are placed on the same HDDs


1,4,7,10 2,5,8 3,6,9

1,2,34,5,6 7,8 9,10

Number and Times of I/O Intervals

• Pattern A get more times of short (less than the Break-Even Time) I/O interval.

• Placement of partitioned data by Near-Number Placement is efficient in this case.


A) Round-Robin Placement B) Near-Number Placement

～ 24sec(Break-Even Time) 111 12

25 ～ 100sec 59 35

101 ～ 200sec 9 14

201sec ～ 8 14

Much More TimesGet longer I/O

intervals

First ExperimentResult

Power Consumption Response Time


(1) (2)0

20,000

40,000

60,000

80,000

100,000

120,000 98,880 98,789

84,244 65,972

Without Standby stateWith Standby state[J]

(1) (2)0:00

24:00

48:00

72:00

96:00

120:00

82:58 85:13

105:5292:43

Without Standby stateWith Standby state

[mm:ss]

A B A B

•Reduce the Power Consumption more in B because I/O interval is longer than A.

•Delay rate of Response Time of B is smaller than A because the seek overhead is smaller.

The placement partitioned data by Near-Number Placement is efficient in this case.

A) Round-Robin Placement B) Near-Number Placement

Reduction Rate of Power Consumption 15% 33%

Delay Rate of Response Time 22% 8%

Second ExperimentData placement control with using 10 HDDs

• The number of HDD is 10• Compare during runtime processing of TPC-H

queries between With and Without Data Placement Control– Power Consumption and Response Time


Second ExperimentData Placemet(1/2)

• Without Control– Placed all data such that the amount of data in each HDD to

be evenly.– The frequency of the data I/O is not considered in this case.


HDD1

HDD7HDD6

HDD2 HDD3 HDD4 HDD5

HDD9HDD8 HDD10LINEITEM_1

LINEITEM_Index_1

21%

16%

12%8%7%

7%

7%

7%7%7%

HDD1 HDD2HDD3 HDD4HDD5 HDD6HDD7 HDD8HDD9 HDD10

Energy State of All Disks is

Idle or Active

the same partitioned number of data are placed on the same HDD

The amount of data in each HDD

Second ExperimentData Placemet(2/2)

• With Control– HDD1: Placed the data that have I/O– HDD2: Placed the data that have no I/O– HDD3-10: No data is placed


HDD1

HDD7

HDD6HDD2 HDD3 HDD4 HDD5

HDD9HDD8 HDD10Have I/O No I/O

No data

Idle or Active

Standby

Biased

Second ExperimentResult

Power Consumption Response Time


0:00

24:00

48:00

72:00

96:00

120:00

92:04 96:24

Without ControlWith Control

[mm:ss]

0

100,000

200,000

300,000

400,000 332,649

93,070

Without controlWith control

[J]

•Reduce the Power Consumption 72%•Delay of Response Time is 4%•This Result is reasonable because only one of HDD has the data that have I/O, and the power consumption state is Idle or Active.

Conclusion

• Proposed data placement control method in Database Run-Time Processing – Based on I/O frequency, modify the data placement– Consider energy saving and application performance

• Evaluate our proposed method with TPC-H– Found the data placement control method is

effective for energy saving during runtime application processing


Future Works

• Examination of more detailed data placement

• Investigation the relation of Trade-off between power consumption and response time


Acknowledgement

• Thank for the conscientious advice with this work– Institute of Industrial Science, the University of Tokyo

• Associate Prof. Daisaku Yokoyama

– Kogakuin University • Associate Prof. Saneyasu Yamaguchi

– Institute of Information Security• Prof. Atsuhiro Goto

– Shibaura Institute of Technology• Associate Prof. Midori Sugaya

• This work is partly supported by the Ministry of Education, Culture, Sports, Science and Technology


END

Thank you for your kind attention.


Evaluation of Data Placement Method in Database Run-Time Processing Considering Energy Saving and...

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