1. 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 3 rd, 2014 IGCC14 GPCDP Workshop

2. OUTLINE Introduction Previous Work Our Proposed Method and Evaluation Plan – Data Placement Control Experimental Evaluation Results and Discussion Conclusion and Future Works November 3rd, 2014IGCC14 GPCDP Workshop2

3. 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. November 3rd, 2014IGCC14 GPCDP Workshop3 Operating costs of machines Amortization of buildings and cooling equipment Electricity charge Energy saving of DC is drawing attention NOW

4. Background(2/2) November 3rd, 2014IGCC14 GPCDP Workshop4 Reducing the power consumption of storage is an efficient way to save energy in datacenters

5. 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 November 3rd, 2014IGCC14 GPCDP Workshop5 have been already addressed

6. Goal of Research To achieve … Analyze of Power Consumption and System Performance of the TPC-H Runtime Propose Storage Power Saving Method November 3rd, 2014IGCC14 GPCDP Workshop6 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

7. Previous Works Combine application level I/O and device level I/O Extract Logical I/O pattern Select appropriate power saving methods November 3rd, 2014IGCC14 GPCDP Workshop77 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 Engine Applications File System Buffer Device Driver Storage Buffer Storage Devices Energy Efficient Storage Management Cooperated with Large Data Intensive ApplicationsNishikawa,et al. (IEEE ICDE 2012)

8. 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 November 3rd, 2014IGCC14 GPCDP Workshop8 To analyze power saving more detail in more small unit

9. 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 November 3rd, 2014IGCC14 GPCDP Workshop9 Long I/O interval *The using frequency of data ＜ ＜

10. Evaluation Plan Compare the performance and energy consumption during runtime processing of TPC-H queries between WITH and WITHOUT data placement control Based on I/O frequency, modify Data Placement Investigate the I/O frequency of data during runtime processing of TPC-H queries The number of used HDDs are 3 - 10

11. Evaluation Environment November 3rd, 2014IGCC14 GPCDP Workshop11 CPU 4 cores * 2 Memory 8GBytes HDD 3TB *11 OS Cent OS 5.10 64bit DBMS HITACHI HiRDB Single Server Version 9 Bench mark TPC-H (SF=10) Power Meter YOKOGAWA Digital Power Meter Local PC Remote Server Power Meter PC to Control Power Meter Ochanomizu Univ. Univ. of Tokyo

12. 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. November 3rd, 2014IGCC14 GPCDP Workshop12 24 seconds To reduce power consumption by using the Standby state, an I/O interval of approximately 24 seconds or more is needed.

13. 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. November 3rd, 2014IGCC14 GPCDP Workshop13

14. 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 November 3rd, 2014IGCC14 GPCDP Workshop14

15. I/O Frequency of Partitioned Data November 3rd, 2014IGCC14 GPCDP Workshop15 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

16. 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 November 3rd, 2014IGCC14 GPCDP Workshop16

17. 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 November 3rd, 2014IGCC14 GPCDP Workshop17 1,4, 7,10 1,4, 7,10 2,5,8 3,6,9 1,2,3 4,5,6 1,2,3 4,5,6 7,8 9,10

18. 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. November 3rd, 2014IGCC14 GPCDP Workshop18 A) Round-Robin PlacementB) Near-Number Placement ～ 24sec(Break-Even Time) 11112 25 ～ 100sec 5935 101 ～ 200sec 914 201sec ～ 814 Much More Times Get longer I/O intervals

19. First Experiment Result Power ConsumptionResponse Time November 3rd, 2014IGCC14 GPCDP Workshop19 AB AB 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 PlacementB) Near-Number Placement Reduction Rate of Power Consumption 15%33% Delay Rate of Response Time 22%8%

20. 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 November 3rd, 2014IGCC14 GPCDP Workshop20

21. 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. November 3rd, 2014IGCC14 GPCDP Workshop21 HDD1 HDD7HDD6 HDD2HDD3HDD4HDD5 HDD9HDD8HDD10 LINEITEM_1 LINEITEM_Index_1 Energy State of All Disks is Idle or Active 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

22. 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 November 3rd, 2014IGCC14 GPCDP Workshop22 HDD1 HDD7 HDD6HDD2HDD3HDD4HDD5 HDD9HDD8HDD10 Have I/O No I/O No data Idle or Active Standby Biased

23. Second Experiment Result Power ConsumptionResponse Time November 3rd, 2014IGCC14 GPCDP Workshop23 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. 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.

24. 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 November 3rd, 2014IGCC14 GPCDP Workshop24

25. Future Works Examination of more detailed data placement Investigation the relation of Trade-off between power consumption and response time November 3rd, 2014IGCC14 GPCDP Workshop25

26. 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 November 3rd, 2014IGCC14 GPCDP Workshop26

27. END Thank you for your kind attention. November 3rd, 2014IGCC14 GPCDP Workshop27