1. CSCE 990: Advanced Distributed Systems
Prof. Ying Lu
Spring 2017
Usage Pattern-Driven Dynamic Data Layout Reorganization by
Sai Suman
Department of Computer Science
University of Nebraska-Lincoln

2. What problem is the paper trying to address?
Scientific applications work on massive amounts of data
Write patterns of data access doesn’t match read patterns
This leads to poor performance
Example:
Scientific simulations write data of all variables in time steps (write layout)
Analysis and visualization applications read subset of variables over several time steps (read pattern)
Poor performance
Mismatch between write layout and read patterns

3. What problem is the paper trying to address?
Solution is to increase contiguous I/O accesses
Layout reorganization methods are one way to do that
Create multiple full replicas of data (or)
Merge multiple non-contiguous data blocks into a single continuous chunk
But, need to support multiple read access patterns
Dynamically

4. What problem is the paper trying to address?
Dynamic data reorganization framework
Support multiple read access patterns
Redirection of read accesses to favorable layout
Also,
Dynamic data access pattern tracing
Efficient storage of replicas
Partial replicas

5. Approach Dynamic Pattern Identification
Trace read accesses at runtime
Identify data usage patterns of the application
Flexible multi-layout management with storage budgets
Evaluate user specified storage constraints and current usage patterns
Support multiple layout reorganization techniques
Select the most suitable technique
Runtime decision making with partial match and redirection

6. Supported reorganization layouts
Data reorganization techniques that our framework supports. The
numbers in each cell are the starting offsets of original data, and the arrow
lines are the order of the reorganized offsets: (a) (original) row-major layout,
(b) column-major (transposition) layout, (c) blocked (chunking) layout, used
as a pre-processing step before applying (d) and (e), (d) z-curve, (e) Hilbertcurve,
(f) custom merging of a subset (data at offsets 5, 9, 13, 7, 11, 15).

7. Approach (contd…)
Runtime decision making with partial match and redirection
Page-level cost model to estimate data access cost during decision making
Automatic read-redirection to the selected layout
Allow partial match for read patterns over reorganized replicas
By allowing partial matches between read patterns
and the reorganized replicas, we extend the usability of existing
layouts compared to the exact match strategy from previous
work

8. Design Trace Analyzer Layout Decision Maker
Trace I/O read calls and identify data access patterns
Layout Decision Maker
Analyze the cost of access over available layout for requested data
Select layout with best access performance
Pattern and Layout Knowledge Base
Metadata for available layout and data access pattern history
Data Reorganization Manager
Reorganizes and replicates the data with optimized layouts
When data present in different layouts  directs to layout with best performance

9. Design

10. Trace analysis and pattern recognition
To understand data usage of applications  trace I/O read calls and identifying patterns
Data usage patterns:
Variables within dataset being accessed
Variables within accessed region
Size of the request
Identify metadata from read calls (HDF5) issued by the application
Store it in an auxiliary data structure
Analyze data selection information:
Element point
Bounding boxes

11. Trace analysis and pattern recognition
Bounding Box Selection:
Applications reads data from a variable bounded by spatial locations
Example: A sub-plane in 2D array, a sub-cube in 3D array
Hyperslab in HDF5 I/O library
Users can select multiple bounding boxes using set operations
Useful when data of a dataset scattered in a file

12. Trace analysis and pattern recognition
Element selection:
Uses a query to find the data
Requires the coordinates of scattered data elements
But, large number of non-contiguous reads with small request sizes  low I/O throughput
Need for optimization

13. Trace analysis and pattern recognition
Generic optimization  optimize without requiring high-level criteria of selection
Doesn’t change existing indexing techniques
Example:
Data of interest = particles of high energy i.e. Energy > some x
Small subset of elements would be repeatedly accessed
Place those elements in a contiguous data chunk  save access time for future accesses
Range query
Clustering

14. Layout decision making
Find best matching replica using pattern detection information
3 steps:
Step 1: Candidate selection:
A) Course grained pruning: Prune layouts that don’t satisfy the request
B)Fine grained pruning: Load metadata of remaining layouts and compare with requested regions

15. Layout decision making
consider replicas that partially overlap with the
requested data as not eligible because the overlapping regions
cannot be estimated accurately without loading the metadata
of a replica.

16. Layout decision making
Step 2: Layout ranking via cost model
Use a page-level cost model to estimate file read cost
Estimated read time for replica r across multiple Object Storage Targets (OSTs)
O is the Object Storage Targets (OSTs)
The requested region is converted to linear space and Nipg and Nichk are calculated within an OST
Select replica with smallest Tr

17. Layout decision making

18. Layout decision making
Step 3: I/O redirection in HDF5
Automatically redirect the read to selected replica (from the cost model) using the replica’s metadata (file, variable’s name and path etc.)
When a replica is not found by the model, default HDF5 read is used

19. Pattern and Layout Knowledge Base
Offline incremental analysis to extract and analyze data usage patterns
On a read call, analyze:
When and how many processes are issuing read requests together
Total size and I/O throughput
For each read, a usage pattern is generated and stored in pattern history:
{variable name/path, selection type and spatial region, process IDs, start/end time, total size, I/O rate}
Merge local patterns to create global patterns and store them in knowledge base
Also store replica’s original file and reorganization information
Merge is done as a preprocessing step
Global patterns help in “hot” data

20. Data Layout Reorganization
3 separate tasks
1) Replica creation:
When and how to create a new layout using knowledge base information
3 scenarios:
a) The original dataset can be reorganized and limited additional storage space is allowed
b) The original dataset cannot be modified, but unlimited storage space is allowed
c) The original dataset can be reorganized but no additional
storage is allowed

21. Data Layout Reorganization
2) Replica Eviction
Replicas ranked according according to a combination of:
Recent usage
Size
effectiveness (performance improvement timeold/timenew)
Evict older and less effective replicas
More details needed from the paper but not provided

22. Data Layout Reorganization
3) OST-Aware Replica Placement
BAD: (a) Each process access data from all OSTs. (b) Each process access data
from a subset of OSTs.
IDEAL: (a) The number of processes equals to that of OSTs. (b) The
number of processes is larger than that of OSTs.

23. Data Layout Reorganization
3) OST-Aware Replica Placement
Stripe count = number of OSTs to access
Stripe size = size of data to write to an OST
When multiple processes reading contiguous data with size ~ (Stripe count) * (Stipe size)
Each OST accessed by several processes  contention
To avoid OST contention, each OST should be contacted by as few processes as possible
No details are provided on how this is done (?)
OST  storage devices

24. Experimental comparisons
Plasma physics particle data : subset of a trillion particle dataset with Energy > 1.1.
Queries selected using Energy > some X
1.3 to 90 times speedup overall

25. Experimental comparisons
Electrocortocigraphy (ECoG) Data: records the electrical activity from the cerebral cortex when the patient is reading different words at different times.
2 to 6 times speedup overall
Here, concatenation techniques is used for layout reorganization
Read time comparison over different query selections

26. Experimental comparisons
Adaptive Mesh Refinement (AMR) Data: allows user to specify a multi-dimensional region, and read the corresponding data of all levels
1.8 to 4 times speedup overall

27. Conclusion and future work
Data usage patterns to select the most suitable layout
Storage efficient optimizations
Improves read performance of several scientific applications involving data analysis
Future work: Data compression techniques for reducing size of data accesses

28. Questions?