1. A Grid-Based Middleware for Scalable Processing of Remote Data
Leonid Glimcher
Advisor: Gagan Agrawal
DataGrid Lab

2. Abundance of Data Data generated everywhere:
Sensors (intrusion detection, satellites)
Scientific simulations (fluid dynamics, molecular dynamics)
Business transactions (purchases, market trends)
Analysis needed to translate data into knowledge
Growing data size creates problems for datamining
DataGrid Lab

3. Emergence of Grid Computing
Provides access to:
Resources
Data
otherwise inaccessible
Aimed at providing a stable foundation for distributed services
Comes at a price:
Standards are needed to integrate distributed services
Open Grid Service Architecture
Web Service Resource Framework
DataGrid Lab

4. Details can be very tedious: Data retrieval, movement and caching
Remote Data Analysis
Remote data analysis
Grid is a good fit
Details can be very tedious:
Data retrieval, movement and caching
Parallel data processing
Resource allocation
Application configuration
Middleware can be useful to abstract away details
DataGrid Lab

5. Our Approach
Supporting development of scalable applications that process remote data using middleware – FREERIDE-G (Framework for Rapid Development of Datamining Engines in Grid)
Middleware user
Repository cluster
Compute cluster
DataGrid Lab

6. FREERIDE-G Middleware
Realized:
Active Data Repository based implementation
Storage Resource Broker based implementation
compliance with remote repository standards
Performance modeling and prediction framework
Future:
Compliance with grid computing standards
Support for data integration and load balancing
DataGrid Lab

7. Publications Published:
“Parallelizing EM Clustering Algorithm on a Cluster of SMPs”, Euro-Par’04.
“Scaling and Parallelizing a Scientific Feature Mining Application Using a Cluster Middleware”, IPDPS’04
“Parallelizing a Defect Detection and Categorization Application”, IPDPS’05.
“FREERIDE-G: Supporting Applications that Mine Remote Data Repositories”, ICPP’06.
“A Performance Prediction Framework for Grid-Based Data Mining Applications”, IPDPS’07
In Submission:
“A Middleware for Remote Mining of Data From SRB-based Servers”, submitted to SC’07
“Middleware for Data Mining Applications on Clusters and Grids, invited to special issue of Journal of Parallel and Distributed Computing (JPDC) on "Parallel Techniques for Information Extraction"
DataGrid Lab

8. Performance prediction for FREERIDE-G Future directions
Outline
Introduction
FREERIDE-G over SRB
Performance prediction for FREERIDE-G
Future directions
Standardization as Grid Service
Data integration and load balancing
Related work
Conclusion
DataGrid Lab

9. FREERIDE-G Architecture
Compute Node
Data Host
I/O Registration
Connection establishment
SRB Agent
While (more chunks to process)
I/O request dispatched
Pending I/O polled
MCAT
SRB Master
Retrieved data chunks
analyzed
SRB Agent
Compute Node
DataGrid Lab

10. SRB Data Host SRB Master: Connection establishment Authentication
Forks agent to service I/O
SRB Agent:
Performs remote I/O
Services multiple client API requests
MCAT:
Catalogs data associated with datasets, users, resources
Services metadata queries (through SRB agent)
DataGrid Lab

11. SDSC Storage Resource Broker
Standard for remote data:
Storage
Access
Data can be distributed across organizations and heterogeneous storage system
Used to host data for FREERIDE-G
Access provided through client API
DataGrid Lab

12. Compute Node More compute nodes than data hosts Each node:
Registers IO (from index)
Connects to data host
While (chunks to process)
Dispatch IO request(s)
Poll pending IO
Process retrieved chunks
DataGrid Lab

13. FREERIDE-G Processing Structure
KEY observation: most data mining algorithms follow canonical loop
Middleware API:
Subset of data to be processed
Reduction object
Local and global reduction operations
Iterator
Derived from precursor system FREERIDE
While( ) {
forall( data instances d) {
I = process(d)
R(I) = R(I) op d }
…….
DataGrid Lab

14. Classic Data Mining Applications
Tested here:
Expectation Maximization clustering
Kmeans clustering
K Nearest Neighbor search
Previously on FREERIDE:
Apriori and FPGrowth frequent itemset miners
RainForest decision tree construction
Middleware is useful for wide variety of algorithms
DataGrid Lab

15. Scientific applications
VortexPro:
Finds vortices in volumetric fluid/gas flow datasets
Defect detection:
Finds defects in molecular lattice datasets
Categorizes defects into classes
DataGrid Lab

16. Experimental setup (LAN)
Single cluster of dual processor 2.4 GHz Opteron 250 connected through Mellanox Infiniband (1 Gb)
Goals:
Evaluate data and compute node scalability
Quantify overhead of using SRB (vs. ADR and TCP/IP)
Active Data Repository implementation uses ADR for data retrieval and TCP/IP streams for data delivery to compute node (for processing)
DataGrid Lab

17. LAN results Defect detection (16.4 GB) Kmeans (6.4 GB) Observations:
Configurations with equal numbers of compute nodes and data hosts ADR outperforms SRB
Configurations with more compute nodes, SRB wins
Great scalability with respect to data host and compute nodes
DataGrid Lab

18. Setup 2: Organizational Grid
Data hosted on Opteron 250’s (as before)
Processed on Opteron 254’s
2 clusters connected through 2 10 GB optical fibers
Goals:
Investigate effects of remote processing
Data chunk effect on remote retrieval overhead
Retrieval concurrency effect on overhead
DataGrid Lab

19. Effects of chunk size Applications: Vortex detection (13.6 GB)
kNN search (6.4 GB)
Observations:
Chunk size has no effect on execution time in LAN
Enlarging chunk size in organization grid decreases remote retrieval overheads
Difference accounted for in ability to overlap retrieval with processing
DataGrid Lab

20. Effects of concurrent retrieval
Applications:
Defect detection (12.2 GB)
Kmeans clustering (6.4GB)
Observations:
Retrieval and processing more overlapped
All applications tested benefited from increase in concurrency level
Remote retrieval overhead can be reduced effectively
DataGrid Lab

21. Performance prediction for FREERIDE-G Future directions
Outline
Introduction
FREERIDE-G over SRB
Performance prediction for FREERIDE-G
Future directions
Standardization as Grid Service
Data integration and load balancing
Related work
Conclusion
DataGrid Lab

22. Motivation for Performance Prediction
Dataset replica selection complicated:
Cluster size (number of partitions)
Network speed between repository and compute clusters
Compute cluster selection:
Cluster size (processing parallelism)
Network speed (data delivery)
Processing structure
Modeling performance is essential for selection of data replicas and compute resources
DataGrid Lab

23. Approach and Processing Structure
3 phases of execution:
Retrieval at data server
Data delivery to compute node
Parallel processing at compute node
Special processing structure:
Generalized reduction
Texec = Tdisk + Tnetwork + Tcompute
DataGrid Lab

24. Information Needed Dataset replica (repository cluster) size
Compute cluster size
Connecting network transfer rate
Application profile
3 stage breakdown
Reduction operation characteristics
Reduction object size
DataGrid Lab

25. Initial Phases Data retrieval and delivery phases
Both phases scale with:
Dataset size
Number of storage nodes
Data delivery also scales with:
Network transfer rate
Appropriate component of execution time breakdown is scaled using factor ratios
DataGrid Lab

26. Data Processing Phase Processing data in 2 components:
Parallel processing on each node scales with:
number of cluster nodes
dataset size
Sequential:
reduction object communication
global reduction
Common processing structure leveraged for accurate processing time prediction
DataGrid Lab

27. Predicting for Different Resources
Requires scaling factors:
3 phases of computation
representative set of applications
application being modeled doesn’t have to be part of representative set
Average used for scaling:
susceptible to outliers
accuracy effected
DataGrid Lab

28. Summarized Prediction Results
Modeling reduction in detail pays off
Correctly model changes in (very accurate):
Dataset size
Parallel configuration
storage cluster
compute cluster
Network speed
Underlying resources (less accurate)
DataGrid Lab

29. Performance prediction for FREERIDE-G Future directions
Outline
Introduction
FREERIDE-G over SRB
Performance prediction for FREERIDE-G
Future directions
Standardization as Grid Service
Data integration and load balancing
Related work
Conclusion
DataGrid Lab

30. FREERIDE-G Grid Service
Emergence of Grid leads to computing standards and infrastructures
Data hosts component already integrated through SRB for remote data access
OGSA – previous standard for Grid Services
WSRF – standard that merges Web and Grid Service definitions
Globus Toolkit is most fitting infrastructure for Grid Service conversion of FREERIDE-G
DataGrid Lab

31. Data Mining Service Provider FREERIDE-G User Parallelization Support
Dataset &
Repository Info
FREERIDE-G User
Web Server
Configuration
WSDL API functions
FREERIDE-G
configuration
Parallelization
Support
Remote Retrieval
Support
Web
Server
Launcher
Deployer for
Data
Servers
Compute Nodes
Resource Mgr
CN 1
Data Server 1
CN 1 … …
Data Server N
CN M-1
CN M
DataGrid Lab

32. Conversion Challenges
Interfacing C++ middleware with GT 4.0 (Java)
Swig to generate Java Native Interface (JNI) wrapper
Integrating data mining service through WSDL interface
Encapsulate service as a resource
Middleware deployment using GT 4.0
Use Web Service – Grid Resource Allocation Module for deployment
DataGrid Lab

33. Need for Load Balancing & Integration
Data integration:
Physical layout different from application (logical) view
Automatically generated wrapper to make connection between 2 views
Load balancing:
Horizontal vs. vertical views
Uneven distribution of vertical views
Different network transfer rates (for delivery of different views)
DataGrid Lab

34. Run-time Load Balancing
Based on unbalanced (initial) distribution figure out an even partitioning scheme (across repository nodes)
Based on difference in initial and even distributions, figure out data senders/receivers
Match up sender/receiver pairs
Organize multiple concurrent transfers required to alleviate data delivery bottleneck
DataGrid Lab

35. Approach to Integration
Bandwidth available also used to determine level of concurrency
After all vertical views of chunks are re-distributed to destination repository nodes, use wrapper to convert data
Automatically generated wrapper:
Input – physical data layout (storage layout)
Output – logical data layout (application view)
DataGrid Lab

36. Performance prediction for FREERIDE-G Future directions
Outline
Introduction
FREERIDE-G over SRB
Performance prediction for FREERIDE-G
Future directions
Standardization as Grid Service
Data integration and load balancing
Related work
Conclusion
DataGrid Lab

37. Grid Computing THE GRID Standards: Open Grid Service Architecture
Web Services Resource Framework
Implementations:
Globus toolkit, WSRF.NET, WSRF::Lite
Other Grid Systems:
Condor and Legion,
Workflow composition systems:
GridLab, Nimrod/G (GridBus), Cactus, GRIST
DataGrid Lab

38. Data Grids Metadata cataloging: Artemis project Remote data retrieval:
SRB, SEMPLAR
DataCutter, STORM
Stream processing midleware:
GATES
dQUOB
Data integration:
Automatic wrapper generation
DataGrid Lab

39. Remote Data in Grid KnowledgeGrid tool-set:
Developing datamining workflows (composition)
GridMiner toolkit:
Composition of distributed, parallel workflows
DiscoveryNet layer:
Creation, deployment, management of services
DataMiningGrid framework:
Distributed knowledge discovery
Other projects partially overlap in goals with ours
DataGrid Lab

40. Resource Allocation Approaches
3 broad categories for resource allocation:
Heuristic approach to mapping
Prediction through modeling:
Statistical estimation/prediction
Analytical modeling of parallel application
Simulation based performance prediction
DataGrid Lab

41. Conclusions
FREERIDE-G – middleware for scalable processing of remote data
Support for high-end processing
Ease use of parallel configurations
Hide details of data movement and caching
Predictable and scalable performance
Compliance with remote repository standards
Future:
Compliance with grid computing standards
Support for data integration and load balancing
DataGrid Lab