Tools and Techniques for the Data Grid

Ohio State University Department of Computer Science and Engineering

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Tools and Techniques for the Tools and Techniques for the Data Grid Data Grid

Gagan Agrawal The Ohio State University


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Overall MotivationOverall Motivation• Computation has long become an integral part of any

scientific discipline – Parallels theory and experiments

• Last 2 (or more) decades have seen Computational-X emerge – Major emphasis on computational modeling – Involved CS support for high-end computing

• In last 5-10 years, X-Informatics is emerging – Data-driven science and engineering applications – Needs CS support for high-end and distributed computing


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Context: Grid Computing Context: Grid Computing • Wide area collaborations and pooling of

resources • Natural synergy with data-intensive

applications – Wide-area sharing of data – Using distributed resources for data analysis – Stage multiple tasks: data generation, processing,

visualization


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Scientific Data Analysis Scientific Data Analysis on (Grid-based) Data Repositorieson (Grid-based) Data Repositories

• Scientific data repositories– Large volume

» Gigabyte, Terabyte, Petabyte– Distributed datasets

» Generated/collected by scientific simulations or instruments

– Data could be streaming in nature

• Scientific data analysisData Specification Data Organization

Data Extraction Data Movement

Data AnalysisData Visualization


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Opportunities Opportunities • Scientific simulations and data collection

instruments generating large scale data • Rapidly increasing wide-area bandwidths • Grid standards enabling sharing of data • Service/grid model of computing

– Plug and play application modules / data sources


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Existing Efforts Existing Efforts • Data grids recognized as important component

of grid/distributed computing • Major topics

– Efficient/Secure Data Movement – Replica Selection – Metadata catalogs / Metadata services – Setting up workflows


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Open Issues Open Issues

• Accessing / Retrieving / Processing data from scientific repositories – Need to deal with low-level formats

• Integrating tools and services having/requiring data with different formats

• Support for processing streaming data in a distributed environment

• Developing scalable data analysis applications


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Ongoing Projects Ongoing Projects • Automatic Data Virtualization • On the fly data integration in a distributed

environment • Middleware for Processing Streaming Data • Compiling XQuery on Scientific and Streaming

Data • Middleware for Scalable Data Processing • Data Mining Algorithms and Systems


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Coastal Forecasting and Change Coastal Forecasting and Change Detection (Lake Erie)Detection (Lake Erie)


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An Example Application ScenarioAn Example Application Scenario


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Outline Outline • Automatic Data Virtualization

– Relational/SQL – XML/XQuery based

• Data Integration • Middleware for Streaming Data • Cluster and Grid-based data mining

middleware


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Automatic Data Virtualization: Automatic Data Virtualization: MotivationMotivation

• Emergence of grid-based data repositories– Can enable sharing of data in an unprecedented way

• Access mechanisms for remote repositories– Complex low-level formats make accessing and

processing of data difficult• Main desired functionality

– Ability to select, down-load, and process a subset of data


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Data VirtualizationData Virtualization An abstract view of data

datasetData Service

DataVirtualization

By Global Grid Forum’s DAIS working group:• A Data Virtualization describes an abstract view of data.• A Data Service implements the mechanism to access and process data through the Data Virtualization


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Our Approach: Automatic Data Our Approach: Automatic Data VirtualizationVirtualization

• Automatically create data services – A new application of compiler technology

• A metadata descriptor describes the layout of data on a repository

• An abstract view is exposed to the users • Two implementations:

– Relational /SQL-based – XML/XQuery based


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System OverviewSystem Overview

SELECT < Data Elements > FROM < Dataset Name > WHERE …. AND Filter( < Data Element> );


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Design a Meta-data Description Design a Meta-data Description LanguageLanguage

• Requirements– Specify the relationship of a dataset to the virtual

dataset schema– Describe the dataset physical layout within a file– Describe the dataset distribution on nodes of one or

more clusters– Specify the subsetting index attributes– Easy to use for data repository administrators and also

convenient for our code generation


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Design OverviewDesign Overview

• Dataset Schema Description Component• Dataset Storage Description Component• Dataset Layout Description Component


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An ExampleAn Example• Oil Reservoir Management

– The dataset comprises several simulation on the same grid

– For each realization, each grid point, a number of attributes are stored.

– The dataset is stored on a 4 node cluster.

Component I: Dataset Schema Description[IPARS] // { * Dataset schema name *}REL = short int // {* Data type definition *}TIME = intX = floatY = floatZ = floatSOIL = floatSGAS = float

Component II: Dataset Storage Description[IparsData] //{* Dataset name *}//{* Dataset schema for IparsData *}DatasetDescription = IPARSDIR[0] = osu0/iparsDIR[1] = osu1/iparsDIR[2] = osu2/iparsDIR[3] = osu3/ipars


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Data Layout Description ComponentData Layout Description Component

Dataset Root

dataset 1 dataset 2 dataset 3

Data1 Data2 Data3 Data4 Data5 Data6

DATASET “ROOT” { DATATYPE { … } DATAINDEX { … } DATA { DATASET dataset1 DATASET dataset2 DATASET dataset3 } DATASET “dataset1” {

DATATYPE { … } DATASPACE { … } DATA { data1 data2 data3 } }

DATASET “dataset2” { DATATYPE { … } DATASPACE { … } DATA { data4 } }

DATASET “dataset3” {….

}}


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An ExampleAn Example• Oil Reservoir Management

– Use LOOP keyword for capturing the repetitive structure within a file.

– The grid has 4 partitions (0~3).

– “IparsData” comprises “ipars1” and “ipars2”. “ipars1” describes the data files with the spatial coordinates’ stored; “ipars2” specifies the data files with other attributes stored.

Component III: Dataset Layout DescriptionDATASET “IparsData” { //{* Name for Dataset *} DATATYPE { IPARS } //{* Schema for Dataset *} DATAINDEX { REL TIME } DATA { DATASET ipars1 DATASET ipars2 }

DATASET “ipars1” { DATASPACE { LOOP GRID ($DIRID*100+1):(($DIRID+1)*100):1 {

X Y Z } } DATA { $DIR[$DIRID]/COORDS $DIRID = 0:3:1 } } // {* end of DATASET “ipars1” *}

DATASET “ipars2” { DATASPACE { LOOP TIME 1:500:1 { LOOP GRID ( $DIRID*100+1):(( $DIRID+1)*100):1 {

SOIL SGAS } } } DATA { $DIR[ $DIRID]/DATA$REL $REL = 0:3:1 $DIRID = 0:3:1 } } //{* end of DATASET “ipars2” *}}


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Automatic Virtualization Using Meta-Automatic Virtualization Using Meta-datadata

• Aligned file chunks {num_rows,

{File1,Offset1,Num_Bytes1}, {File2,Offset2,Num_Bytes2}, ……, {Filem,Offsetm,Num_Bytesm} }

• Our tool parses the meta-data descriptor and generates function codes.

At run time, the query would provide parameters to invoke the generated functions to create Aligned File Chunks.

Dataset Root

dataset 1 dataset 2 dataset 3

Data1 Data2 Data3 Data4 Data5 Data6


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Compiler AnalysisCompiler Analysis• Meta-data descriptor

Create AFC

Process AFC

Index & Extraction function code

Data _Extract { Find _File _Groups() Process _File _Groups() } Find _File _Groups { Let S be the set of files that match against the query Classify files in S by the set of attributes they have Let S1, … ,Sm be the m sets T = Ø foreach {s1, … ,sm } si ∈ Si { {* cartesian product between S1, … ,Sm *} If the values of implicit attributes are not inconsistent { T = T ∪ {s1, … ,sm } } } Output T } Process _File _Groups { foreach {s1, … ,sm } ∈ T Find _Aligned _File _Chunks() Supply implicit attributes for each file chunk foreach Aligned File Chunk { Check against index Compute offset and length Output the aligned file chunk } }


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• Information Integration • Middleware for Streaming Data • Coarse-grained pipelined parallelism


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XML/XQuery ImplementationXML/XQuery Implementation

TEXT

…

NetCDF

RMDB

HDF5

XML

XQuery

???


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Programming/Query LanguageProgramming/Query Language• High-level declarative languages ease application

development – Popularity of Matlab for scientific computations

• New challenges in compiling them for efficient execution

• XQuery is a high-level language for processing XML datasets – Derived from database, declarative, and functional languages ! – XPath (a subset of XQuery) embedded in an imperative language

is another option


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Approach / Contributions Approach / Contributions • Use of XML Schemas to provide high-level abstractions

on complex datasets • Using XQuery with these Schemas to specify

processing • Issues in Translation

– High-level to low-level code – Data-centric transformations for locality in low-level codes – Issues specific to XQuery

» Recognizing recursive reductions » Type inferencing and translation


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External Schema

XQuery Sources

Compiler

XML Mapping Service

System ArchitectureSystem Architecture

logical XML schema physical XML schema

C++/C


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• Information Integration • Middleware for Streaming Data • Cluster and Grid-based data mining

middleware


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Data Integration: Overall GoalData Integration: Overall Goal• Tools for data integration driven by:

– Data explosion» Data size & number of data sources

– New analysis tools– Autonomous resources

» Heterogeneous data representation & various interfaces – Frequent Updates– Common Situations:

» Flat-file datasets » Ad-hoc sharing of data


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Current ApproachesCurrent Approaches• Manually written wrappers

– Problems» O(N2) wrappers needed, O(N) for a single updates

• Mediator-based integration systems– Problems

» Need a common intermediate format » Unnecessary data transformation

• Integration using web/grid services» Needs all tools to be web-services (all data in XML?)


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Our ApproachOur Approach• Automatically generate wrappers

– Stand-alone programs– For integrated DBs, (grid) workflow systems

• Transform data in files of arbitrary formats– No domain- or format-specific heuristics– Layout information provided by users

• Help biologists write layout descriptors using data mining techniques

• Particularly attractive for – flat-file datasets – ad hoc data sharing – data grid environments


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Our Approach: AdvantagesOur Approach: Advantages• Advantages:

– No DB or query support required– One descriptor per resource needed – No unnecessary transformation– New resources can be integrated on-the-fly


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Our Approach: ChallengesOur Approach: Challenges• Description language

– Format and logical view of data in flat files– Easy to interpret and write

• Wrapper generation and Execution– Correspondence between data items– Separating wrapper analysis and execution

• Interactive tools for writing layout descriptors – What data mining techniques to use ?


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Wrapper Generation System Wrapper Generation System OverviewOverview

Layout Descriptor Schema Descriptors

Parser Mapping Generator

Data Entry Representation Schema Mapping

DataReader DataWriterSynchronizer

SourceDataset

TargetDataset

Application Analyzer

WRAPINFO


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• Information Integration • Middleware for Streaming Data • Coarse-grained pipelined parallelism


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Streaming Data ModelStreaming Data Model• Continuous data arrival and processing • Emerging model for data processing

– Sources that produce data continuously: sensors, long running simulations

– WAN bandwidths growing faster than disk bandwidths • Active topic in many computer science communities

– Databases– Data Mining – Networking ….


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Summary/Limitations of Current Summary/Limitations of Current WorkWork

• Focus on– centralized processing of stream from a single source

(databases, data mining) – communication only (networking)

• Many applications involve– distributed processing of streams– streams from multiple sources


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Motivating ApplicationMotivating Application

Switch Network

X

Network Fault Management System

Network Fault Management System


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Motivating Application (2)Motivating Application (2)Computer Vision Based Surveillance


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Features of Distributed Streaming Features of Distributed Streaming Processing ApplicationsProcessing Applications

• Data sources could be distributed– Over a WAN

• Continuous data arrival • Enormous volume

– Probably can’t communicate it all to one site• Results from analysis may be desired at multiple sites • Real-time constraints

– A real-time, high-throughput, distributed processing problem


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Need for a Grid-Based Stream Need for a Grid-Based Stream Processing Middleware Processing Middleware

• Application developers interested in data stream processing – Will like to have abstracted

» Grid standards and interfaces » Adaptation function

– Will like to focus on algorithms only • GATES is a middleware for

– Grid-based – Self-adapting

Data Stream Processing


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Adaptation for Real-time ProcessingAdaptation for Real-time Processing• Analysis on streaming data is approximate • Accuracy and execution rate trade-off can be

captured by certain parameters (Adaptation parameters) – Sampling Rate – Size of summary structure

• Application developers can expose these parameters and a range of values


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Public class Sampling-Stage implements StreamProcessing{… void init(){…}… void work(buffer in, buffer out){

…

while(true) { Image img = get-from-buffer-in-GATES(in); Image img-sample = Sampling(img, sampling-ratio); put-to-buffer-in-GATES(img-sample, out);

}…

}

API for AdaptationAPI for Adaptation

sampling-ratio = GATES.getSuggestedParameter();

GATES.Information-About-Adjustment-Parameter(min, max, 1)


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• Information Integration • Middleware for Streaming Data • Cluster and Grid-based data mining

middleware


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Scalable Mining ProblemScalable Mining Problem

• Our understanding of what algorithms and parameters will give desired insights is often limited

• The time required for creating scalable implementations of different algorithms and running them with different parameters on large datasets slows down the data mining process


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Mining in a Grid Environment Mining in a Grid Environment

A data mining application in a grid environment - - Needs to exploit different forms of available parallelism

- Needs to deal with different data layouts and formats - Needs to adapt to resource availability


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FREERIDE Overview FREERIDE Overview • Framework for Rapid

Implementation of datamining engines

• Demonstrated for a variety of standard mining algorithm

• Targeted distributed memory

parallelism, shared memory parallelism, and combination

• Can be used as basis for scalable grid-based data mining implementations

• Published in SDM 01, SDM 02, SDM 03, Sigmetrics 02, Europar 02, IPDPS 03, IEEE TKDE (to appear)


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FREERIDE-GFREERIDE-G• Data processing may not be feasible where the

data resides • Need to identify resources for data processing • Need to abstract data retrieval, movement and

parallel processing


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Students InvolvedStudents InvolvedRecent Ph.D Grads (2005-06)

– Ruoming Jin (Kent State University) – Wei Du (Yahoo) – Xiaogang Li (Ask.com)– Liang Chen (Amazon) – Li Weng (Oracle)

• Current Students: – Xuan Zhang (graduating Winter 07) – Kaushik Sinha (joint with Misha Belkin) – Leonid Glimcher (4th year) – Qian Zhu (3rd year) – Wenjing Ma (2nd year) – David Chiu (2nd year) – Fan Wang (2nd year)


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Some Newer Topics Some Newer Topics • Resource allocation, fault tolerance, and

process migration in GATES (Qian Zhu) • FREERIDE-G using SRB (Leonid Glimcher) • FREERIDE on newer architectures (Wenjing

Ma) • Deep web mining (for bioinformatics) (Fan

Wang) • Service-oriented composition of data and

services (David Chiu)


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Summary Summary • Distributed data-driven science:

– We have a long way to go • The holy grail will be

– The system finds all relevant data for you – The system finds all relevant analysis tools for you – The system best uses all possible resources to give you

the fastest response – Does all of this transparent to you !

• We will never get there, but the journey is interesting ….

Tools and Techniques for the Data Grid

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