Evaluation of Data Placement Method
in Database Run-Time Processing
Considering Energy Saving and
Application Performance
Naho IIMURA†
Miyuki NAKANO††
Norifumi NISHIKAWA‡
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
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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
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Background(2/2)
Typical Data Center Energy Consumption Rate
5% 5%
Cooling
Servers
13%
Storages
14%
63%
Network Hardware
Power Conversion
Reducing the power consumption of storage is
an efficient way to save energy in datacenters
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Research Objective
• Conventional Methods
have been already
addressed
– 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
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Goal of Research
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
To achieve …
• Analyze of Power Consumption and System
Performance of the TPC-H Runtime
• Propose Storage Power Saving Method
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Previous Works
Energy Efficient Storage Management Cooperated with Large Data Intensive
Applications
Nishikawa ,et al. (IEEE ICDE 2012)
• Combine application level I/O and device level I/O
• Extract Logical I/O pattern
• Select appropriate power saving methods
Applications
DB Engine
DB Buffer
File System Buffer
Device Driver
Storage Buffer
Storage Devices
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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
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Present Work Direction
• Experiment Environment
– Large scale Unit → Single Unit
To analyze power
saving more detail in
more small 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
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Our Proposed Method
• Data Placement Control
– Based on I/O frequency on Run-Time applications, Modify
the data placement
Long I/O
interval
*The using frequency of data
＜
＜
Change to the Standby-mode when disk is not being used
Finally, power consumption can be reduced more
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Evaluation Plan
Investigate the I/O frequency of data during runtime
processing of TPC-H queries
Based on I/O frequency, modify Data Placement
The number of used
HDDs are 3 - 10
Compare the performance and energy consumption during
runtime processing of TPC-H queries between WITH and
WITHOUT data placement control
Evaluation Environment
CPU
Memory
HDD
4 cores * 2
OS
Cent OS 5.10
64bit
DBMS
HITACHI HiRDB
Single Server
Version 9
Bench
mark
Power
Meter
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Univ. of Tokyo
8GBytes
3TB *11
PC to
Control
Power
Meter
Power
Meter
Remote
Server
Local PC
Ochanomizu Univ.
TPC-H
(SF=10)
YOKOGAWA
Digital Power
Meter
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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.
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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.
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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
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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
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First Experiment
Data 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
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Two Patterns of Data Placement
A) Partitioned data is placed by round-robin placement
1,4,
7,10
2,5,8
3,6,9
B) Partitioned data with near numbers are placed on the same
HDDs
1,2,3
4,5,6
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7,8
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9,10
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Number and Times of I/O Intervals
A) Round-Robin Placement
B) Near-Number Placement
～24sec(Break-Even Time)
111
12
25～100sec
59
35
9
14
101～200sec
Much More Times
8
201sec～
Get longer I/O
intervals
14
• Pattern A get more times of short (less than
the Break-Even Time) I/O interval.
• Placement of partitioned data by NearNumber Placement is efficient in this case.
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First Experiment
Result
Power Consumption
Response Time
Without Standby state
With Standby state
[J]
[mm:ss]
Without Standby state
With Standby state
•Reduce the Power Consumption120:00
more in B105:52
98,880
98,789
because I/O96:00
interval
is longer than92:43
A.
100,000
82:58
84,244
80,000
•Delay rate of Response
Time of B72:00
is smaller than A 85:13
65,972
60,000
because the48:00
seek overhead is smaller.
40,000
The placement partitioned data by
Near-Number Placement is
24:00
20,000
efficient
in this case.
0
0:00
120,000
(1)
A
(2)
B
(1)A
A) Round-Robin Placement
(2)
B
B) Near-Number Placement
Reduction Rate of Power
Consumption
15%
33%
Delay Rate of Response
Time
22%
8%
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Second Experiment
Data 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
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Second Experiment
Data 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
HDD2
HDD3
HDD6
LINEITEM_1
LINEITEM_Index_1
the same
partitioned number
of data are placed
on the same HDD
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HDD7
HDD4
HDD8
HDD5
Energy State of All
Disks is
Idle or Active
HDD9
HDD10
The amount of data in each HDD
7%
7%
HDD1
7%
21%
HDD3
7%
HDD5
16%
7%
HDD7
HDD9
8%
8%
12%
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HDD2
HDD4
HDD6
HDD8
HDD10
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Second Experiment
Data 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
Idle or
Active
Standby
HDD1
HDD2
Have I/O
No I/O
HDD3
HDD7
Biased
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HDD4
HDD5
HDD6
HDD8
HDD9
HDD10
No data
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Second Experiment
Result
Power Consumption
[J]
400,000
332,649
Response Time
Without control
[mm:ss]
Without Control
With control
120:00
With Control
96:00
300,000
0
96:24
72:00
200,000
100,000
92:04
93,070
48:00
24:00
0:00
•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.
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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
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Future Works
• Examination of more detailed data placement
• Investigation the relation of Trade-off between
power consumption and response time
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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
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END
Thank you for your kind attention.
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