MEMS Based Storage Architecture for Relational Databases

1. MEMS Based Storage Architecture for Relational Databases Arjun Suresh S7, R College of Engineering Trivandrum

2. Outline • MEMS-based storage • Relational database layout • FRM (Flexible Retrieval Model) • Query Processing on FRM • Evaluation • Conclusion

3. Building Storage on MEMS MEMS areMicro Electrical Mechanical Systems. • Basic functions: sensors and actuators • Built by standard silicon processing • Combine mechanical and electrical components • Enable “systems-on-a-chip”

4. Why need MEMS-based storage? • Huge gaps between disks and RAM • 1000,000 latency gap (10ms vs 50 ns), widening 50% yearly. • 1000 price gap per byte • 1000 life gap • MEMS narrows gaps • 10X smaller latency than disk • 100X cheaper than RAM in the range 1 – 10 GB • 100 MB - 1 GB/s bandwidth • 10 GB capacity with a penny size • Desired for energy and volume critical systems

5. EEPROM/Flash DRAM Cost per Byte MEMS-based Storage Hard Disk Latency Technology Trends

6. IBM Millipede • Using pits in the polymers made by tip heating to store data

7. Bits stored underneath each tip MEMS Storage Architecture Read/write tips Media side view

8. Y X MEMS Storage Architecture Media Sled

9. MEMS Storage Architecture Springs Springs Springs Springs

10. Y X MEMS Storage Architecture Anchor Anchor Anchors attach the springs to the chip. Anchor Anchor

11. Y X MEMS Storage Architecture Sled is free to move

12. Y X MEMS Storage Architecture Sled is free to move

13. Y X MEMS Storage Architecture Springs pull sled toward center

14. Y X MEMS Storage Architecture Springs pull sled toward center

15. Y X MEMS Storage Architecture Actuator Actuators pull sled in both dimensions Actuator Actuator

16. Y X MEMS Storage Architecture Actuators pull sled in both dimensions

17. Y X MEMS Storage Architecture Actuators pull sled in both dimensions

18. Y X MEMS Storage Architecture Actuators pull sled in both dimensions

19. Y X MEMS Storage Architecture Actuators pull sled in both dimensions

20. Y X MEMS Storage Architecture Probe tip Probe tips are fixed Probe tip

21. Y X MEMS Storage Architecture Probe tips are fixed

22. One probe tip per rectangle Sled only moves over the area of a single rectangle Each tip accesses data at the same relative position Y X MEMS Storage Architecture

23. Sweep area of One probe tip Properties of MEMS Storage

24. N bits M bits One tip sector Properties of MEMS Storage One tip region

25. Physical Parameters

26. Existing Work on Integration of MEMS Storage • Solution proposed by CMU researchers • Mapping MEMS storage into conventional disk • Adapt I/O scheduling and data placement to MEMS • Preliminary study shows • Reduce the I/O stall times by 4 to 74 times over disks • Improve the overall application run times by 1.9 to 4.4 • Reduce the energy consumption by 10-54 times

27. Better solutions?? • The approach of mapping MEMS into disk • Simplify the procedure of integration of MEMS • Does not consider the physical properties of MEMS

28. Relational Data Placement StudentGrade

29. N-ary Storage Model (NSM) • Store records in a relation in slotted disk pages • Organize records sequentially on the disk pages

30. Decomposition Storage Model(DSM) name perm ID • Divide a relation into sub-relations based on the number of attributes • Each sub-relation corresponds to each attribute • Each sub-relation is organized into pages in the same way as NSM A disk page A disk page age grade A disk page A disk page

31. Partition Attributes Across (PAX) • Within each page, PAX groups all values of each attribute into a mini-page • A page is divided into mini-pages based on the number of attributes • It stores the same data as NSM in each page A disk page

32. Common Workload Requirements • Relational data should be compatible with • OLTP workloads • Due to the update characteristics, relations need to be accessed in a row-wise manner • OLAP workloads • Only a subset of attributes is of interest, data placement should facilitate data retrieval on a column-wise fashion

33. Flexible Retrieval Model (FRM) • Facilitates data retrieval in both row-wise and column-wise manner • Retrieves the relevant subsets of the relations • Uses two dimensional layout of MEMS storage • Improves the I/O utilization • Maximize the concurrent tips to only retrieve the necessary data • It is also cache-friendly • Use intra-record locality

34. Attr1 Attr2 Attr3 (0, 0) (0, 1) (1, 2) (0, 2) (0, 2) (0, 2) (0, 2) (0, 0) (0, 1) (0, 0) (0, 1) (0, 0) (0, 1) FRM Data Placement and Retrieval • Given a relation with three attributes, the size of each attribute is 8 bytes • Placing this relation in 4x4 MEMS-based storage • 4 concurrent tips

35. Query Processing on FRM • Selection and projection without index • Two-dimensional table scan • Selection and projection with index • Encode data position as 4-tuple (tip-x, tip-y, offset-x, offset-y) • Joins • Index-based or hash

36. Experiment Setup • MEMS storage: 1280 concurrent tips out of 6400 total tips • Pentium II Celeron 433X2 processors • L1 cache:16KB, 32-byte cache line, 20 ns delay • L2 cache:128KB, 32-byte cache line, 200 ns delay • A relation R with 1.28 million records • Sixteen 8-byte attributes in each record • Queries: SELECT A1, A2,…, An FROM R WHERE A1 > Bound;

37. Memory utilization Consumed memory(MB) The selected attributes in queries

38. I/O performance • NSM and PAX have the same I/O time • I/O time of FRM is proportional to the size of retrieved attributes

39. Cache Utilization Selection Queries (2 attributes)

40. Selection Queries (13 attributes)

41. Projection Queries Selectivity = 50%

42. Projection Queries Selectivity =10%

43. Conclusion and Future Work • Proposed a relational data placement scheme for MEMS-based storage • Take advantage of two-dimensional MEMS access feature • Arrange MEMS rows and columns to relational attributes and records • Save IO cost and improve cache performance • Other cache friendly techniques for MEMS-based storage devices are to be explored

44. Thank You……