information-driven science in pervasive grid environments manish parashar the applied software...

Information-Driven Science in Pervasive Grid Environments

Manish ParasharThe Applied Software Systems Laboratory

ECE/CAIP, Rutgers Universityhttp://www.caip.rutgers.edu/TASSL

(Ack: NSF, DoE, NIH)

Outline

• Pervasive Grid Environments - Unprecedented Opportunities

• Pervasive Grid Environments - Unprecedented Challenges, Opportunities

• Project AutoMate @ TASSL, Rutgers University – Enabling Autonomic Applications in Pervasive Grid Environments

• An Illustrative Application

• Concluding Remarks

Pervasive Grid Environments - Unprecedented Opportunities• Pervasive Grids Environments

– Seamless, secure, on-demand access to and aggregation of, geographically distributed computing, communication and information resources

• Computers, networks, data archives, instruments, observatories, experiments, sensors/actuators, ambient information, etc.

– Context, content, capability, capacity awareness– Ubiquity and mobility

• Knowledge-based, information/data-driven, context/content-aware computationally intensive science– Symbiotically and opportunistically combine computations, experiments,

observations, and real-time information to model, manage, control, adapt, optimize, …

• Crisis management, monitor and predict natural phenomenon, monitor and manage engineering systems, optimize business processes

• A new paradigm for scientific investigation ?– seamless access

• resources, services, data, information, expertise, …– seamless aggregation– seamless (opportunistic) interactions/couplings

The original Grid concept has moved on!

• Coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations.

Source: I. Foster et al

Pervasive Grid Environments and Information Driven Science

Models dynamically composed.

“WebServices” discovered &

invoked.

Resources discovered, negotiated, co-allocated

on-the-fly. Model/Simulation

deployed

Experts query, configure resources

Experts interact and collaborate using ubiquitous and

pervasive portals

Applications& Services

Model A

Model BLaptop

PDA

ComputerScientist

Scientist

Resources

Computers, Storage,Instruments, ...

Data Archive &Sensors

DataArchives

Sensors, Non-Traditional Data

Sources

Experts mine archive, match

real-time data with history

Real-time data assimilation/injection

(sensors, instruments, experiments, data

archives),

Automated mining & matching

Models write into

the archive

Experts monitor/interact with/interrogate/steer models (“what if”

scenarios,…). Application notifies experts of interesting phenomenon.

Information-driven Management of Subsurface Geosystems: The Instrumented Oil Field (with UT-CSM, UT-IG, OSU, UMD, ANL)

Detect and track changes in data during production.Invert data for reservoir properties.Detect and track reservoir changes.

Assimilate data & reservoir properties into the evolving reservoir model.

Use simulation and optimization to guide future production.

Data Driven

ModelDriven

Optimize

• Economic revenue

• Environmental hazard

• …

Based on the present subsurface knowledge and numerical model

Improve numerical model

Plan optimal data acquisition

Acquire remote sensing data

Improve knowledge of subsurface to

reduce uncertainty

Update knowledge of modelMan

agem

ent

dec

isio

n

START

Dynamic Decision Dynamic Decision SystemSystem

Dynamic Data-Driven Dynamic Data-Driven Assimilation Assimilation

Data assimilation

Subsurface characterization

Experimental design

Dynamic, Data Driven Reservoir Management

LandfillsLandfills OilfieldsOilfields

ModelsModelsSimulationSimulation

DataDataControlControlUnderground

Pollution

Underground

Pollution

UnderseaReservoirsUnderseaReservoirs

Vision: Diverse Geosystems – Similar Solutions

Management of the Ruby Gulch Waste Repository (with UT-CSM, INL, OU)

– Flowmeter at bottom of dump

– Weather-station

– Manually sampled chemical/air ports in wells

– Approx 40K measurements/day

• Ruby Gulch Waste Repository/Gilt Edge Mine, South Dakota – ~ 20 million cubic yard of

waste rock– AMD (acid mine drainage)

impacting drinking water supplies

• Monitoring System– Multi electrode resistivity system

(523)• One data point every 2.4

seconds from any 4 electrodes

– Temperature & Moisture sensors in four wells

“Towards Dynamic Data-Driven Management of the Ruby Gulch Waste Repository,” M. Parashar, et al, DDDAS Workshop, ICCS 2006, Reading, UK, LNCS, Springer Verlag, Vol. 3993, pp. 384 – 392, May 2006.

Models, Surrogate/Reduced models

Ruby Gulch Waste Repository

Sensors

Control algorithms

OptimizationAlgorithms

Opt

imiz

atio

n

Controllable input

Observations Data Assimilation

Dynamic Data-Driven Waste Management

Actuators

Inverse Modeling

System responses

Parameters,Boundary & Initial Conditions

Forward Modeling

Prediction

ComparisonWith observations

Networkdesign

Application

goodbad

Adaptive Fusion of Stochastic Information for Imaging Fractured Vadose Zones (with U of AZ, OSU, U of IW)

• Near-Real Time Monitoring, Characterization and Prediction of Flow Through Fractured Rocks

Data-Driven Forest Fire Simulation (U of AZ)

• Predict the behavior and spread of wildfires (intensity, propagation speed and direction, modes of spread)

– based on both dynamic and static environmental and vegetation conditions

– factors include fuel characteristics and configurations, chemical reactions, balances between different modes of hear transfer, topography, and fire/atmosphere interactions.

“Self-Optimizing of Large Scale Wild Fire Simulations,” J. Yang*, H. Chen*, S. Hariri and M. Parashar, Proceedings of the 5th International Conference on Computational Science (ICCS 2005), Atlanta, GA, USA, Springer-Verlag, May 2005.

System for Laser Treatment of Cancer – UT, Austin

Source: L. Demkowicz, UT Austin

Synthetic Environment for Continuous Experimentation – Purdue University

Source: A. Chaturvedi, Purdue Univ.

Integrated Wireless Phone Based Emergency Response System – Notre Dame

• Detect abnormal patterns in mobile call activity and locations• Initiate dynamic data driven simulations to predict the evolution of

the abnormality• Initiate higher resolution data collection in localities of interest• Interface with emergency response Decision Support Systems

Source: G. Madey, ND

Many Application Areas ….

• Hazard prevention, mitigation and response– Earthquakes, hurricanes, tornados, wild fires, floods, landslides, tsunamis, terrorist

attacks• Critical infrastructure systems

– Condition monitoring and prediction of future capability• Transportation of humans and goods

– Safe, speedy, and cost effective transportation networks and vehicles (air, ground, space)

• Energy and environment– Safe and efficient power grids, safe and efficient operation of regional collections

of buildings• Health

– Reliable and cost effective health care systems with improved outcomes• Enterprise-wide decision making

– Coordination of dynamic distributed decisions for supply chains under uncertainty• Next generation communication systems

– Reliable wireless networks for homes and businesses

• … … … …

• Report of the Workshop on Dynamic Data Driven Applications Systems, F. Darema et al., March 2006, www.dddas.org

Source: M. Rotea, NSF

Outline


• Pervasive Grid Environments - Unprecedented Challenges– Uncertainty

• System uncertainty• Application uncertainty• Information uncertainty




Pervasive Grid Applications – Unprecedented Challenges: Uncertainty

• System Uncertainty– Very large scales– Ad hoc (amorphous) structures/behaviors

• p2p, hierarchical, etc, architectures

– Dynamic• entities join, leave, move, change behavior

– Heterogeneous• capability, connectivity, reliability, guarantees, QoS

– Lack of guarantees• components, communication

– Lack of common/complete knowledge• number, type, location, availability, connectivity, protocols, semantics, etc.

Pervasive Grid Applications – Unprecedented Challenges: Uncertainty

• Application Uncertainty– Dynamic behaviors

• space-time adaptivity

– Dynamic and complex couplings• multi-physics, multi-model, multi-resolution, ….

– Dynamic and complex (ad hoc, opportunistic) interactions• application application, application resource, application data,

application user, …

– Software/systems engineering issues• Emergent rather than by design

• Information Uncertainty– Availability, resolution, quality of information– Devices capability, operation, calibration– Trust in data, data models

Pervasive Grid Applications – Research Issues, Opportunities

• Applications and algorithms– tobust model/algorithm development and calibration

• impact of information on models and models on information acquisition

– continuous model, algorithm, emergent system validation– uncertainty estimation – parameter selection and optimization– observability, identifiability, tractability

• Measurement and actuation systems– “real-time” data collection and transport

• in-network aggregation, assimilation

– data selection and application integration, data quality management– data/data-model heterogeneity– security trust, data provenance, audit trails– actuation and control

Pervasive Grid Applications – Research Issues, Opportunities

• Systems software– programming systems/models for data integration and runtime self-

management• components and compositions capable of adapting behavior and

interactions

• correctness, consistency, performance, quality-of-service constraints

– data management mechanisms for acquisition with real time, space and data quality constraints

• high data volumes/rates, heterogeneous data qualities, sources

– runtime execution services that guarantee correct, reliable execution with predictable and controllable response time

• data assimilation, injection, adaptation

Outline






Organization Organization Organization

Grid Middleware Infrastructure

Virtual Organization

Applications

Programming System(Programming Model + Abstract Machine)

Virtualization(Create and manage VOs and VMs)

Abstraction(Support abstract behaviors/assumptions)

Conce

ptualIm

plementatio

n

Virtual Machine Virtual Machine Virtual Machine

Organization Organization OrganizationOrganizationOrganization OrganizationOrganization OrganizationOrganization

Grid Middleware Infrastructure

Virtual Organization

Applications

Programming System(Programming Model + Abstract Machine)





Conce

ptualIm

plementatio

n

Virtual Machine Virtual Machine Virtual Machine

Programming Pervasive Grid Systems

– Programming System

• programming model, languages/abstraction – syntax + semantics

– entities, operations, rules of composition, models of coordination/communication

• abstract machine, execution context and assumptions

• infrastructure, middleware and runtime

– Hide or expose uncertainty?

• robustness, ease of programming

• the inverted stack …

“Conceptual and Implementation Models for the Grid,” M. Parashar and J.C. Browne, Proceedings of the IEEE, Special Issue on Grid Computing, IEEE Press, Vol. 19, No. 3, March 2005.

Programming Pervasive Grid Systems

• Computing has evolved and matured to provide specialized solutions to satisfy relatively narrow and well defined requirements in isolation– performance, security, dependability, reliability, availability, throughput,

pervasive/amorphous, automation, reasoning, etc.

• In case of pervasive Grid applications/environments, requirements, objectives, execution contexts are dynamic and not known a priori– requirements, objectives and choice of specific solutions (algorithms,

behaviors, interactions, etc.) depend on runtime state, context, and content– applications should be aware of changing requirements and executions

contexts and to respond to these changes are runtime

• Autonomic computing - systems/applications that self-manage – use appropriate solutions based on current state/context/content and

based on specified policies– address uncertainty at multiple levels– asynchronous algorithms, decoupled interactions/coordination,

content-based substrates

Project AutoMate: Enabling Autonomic Applications

• Conceptual models and implementation architectures – programming systems based on popular programming models

• object, component and service based prototypes

– content-based coordination and messaging middleware

– amorphous and emergent overlays

• http://automate.rutgers.edu

Ru

dd

erC

oo

rdin

ation

Mid

dlew

are

Sesam

e/DA

IS P

rotection Service

Autonomic Grid Applications

Programming SystemAutonomic Components, Dynamic Composition,

Opportunistic Interactions, Collaborative Monitoring/Control

Decentralized Coordination EngineAgent Framework,

Decentralized Reactive Tuple Space

Semantic Middleware ServicesContent-based Discovery, Associative Messaging

Content OverlayContent-based Routing Engine,

Self-Organizing Overlay

Ont

olog

y, T

axon

omy

Met

eor/

Sq

uid

Co

nte

nt-

bas

edM

idd

lew

are

Acc

ord

Pro

gra

mm

ing

Fra

mew

ork

Project AutoMate: Components

• Accord – A Programming System for Autonomic Grid Applications

• Rudder/Comet – Decentralized Coordination Middleware• Meteor – Content-based Interactions Middleware• ACE – Autonomic Composition Engine• Squid – Decentralized Information Discovery and Content-

based Routing• SESAME – Context-Aware Access Management• DAIS – Cooperative Protection against Network Attacks

• More information/Papers – http://automate.rutgers.edu “AutoMate: Enabling Autonomic Grid Applications,” M. Parashar et al, Cluster Computing: The Journal of Networks, Software Tools, and Applications, Special Issue on Autonomic Computing, Kluwer Academic Publishers. Vol. 9, No. 2, pp. 161 – 174, 2006.

Accord: Rule-Based Programming System

• Accord is a programming system which supports the

development of autonomic applications.– Enables definition of autonomic components with programmable

behaviors and interactions.

– Enables runtime composition and autonomic management of these

components using dynamically defined rules.

• Dynamic specification of adaptation behaviors using rules.

• Enforcement of adaptation behaviors by invoking sensors and actuators.

• Runtime conflict detection and resolution.

• 3 Prototypes: Object-based, Components-based (CCA),

Service-based (Web Service)“Accord: A Programming Framework for Autonomic Applications,” H. Liu* and M. Parashar, IEEE Transactions on Systems, Man and Cybernetics, Special Issue on Engineering Autonomic Systems, IEEE Press, Vol. 36, No 3, pp. 341 – 352, 2006.

Autonomic Element in Accord

Element Manager

Event generation

Actuatorinvocation

OtherInterface

invocation

Internalstate

Contextualstate

Rules

Element Manager

Functional Port

Autonomic Element

Control Port

Operational Port

ComputationalElement

The Accord Runtime Infrastructure

Application workflow

Composition manager

Application strategiesApplication requirements

Interaction rules

Interaction rules

Interaction rules

Interaction rules

Behavior rules

Behavior rules

Behavior rules

Behavior rules

LLC Based Self Management within Accord

• Element/Service Managers are augmented with LLC Controllers– monitors state/execution context of elements– enforces adaptation actions determined by the controller– augment human defined rules

Self-Managing Element

ComputationalElement

LLC Controller

Element Manager

InternalState

ContextualState

OptimizationFunction

LLC Controller

Element Manager

Advice Computational Element Model

Decentralized (Decoupled/Asynchronous) Content-based Middleware Services

SquidTON: Reliability and Fault Tolerance

• Pervasive Grid systems are dynamic, with nodes joining, leaving and failing relatively often

• => data loss and temporarily inconsistent overlay structure• => the system cannot offer guarantees

– Build redundancy into the overlay network– Replicate the data

• SquidTON = Squid Two-tier Overlay Network– Consecutive nodes form unstructured groups, and at the same time are connected by a

global structured overlay (e.g. Chord)– Data is replicated in the group

33

2522

82

86

91

94

128132

150157

141

231

249

240

185

192

1710

Groups of nodes Group identifier

250231

249240

185

192

128

132

150

157141

161

8286

91

94

3325

22

1710

102

41

Content Descriptors and Information Space

• Data element = a piece of information that is indexed and discovered– Data, documents, resources, services, metadata, messages, events, etc.

• Each data element has a set of keywords associated with it, which describe its content => data elements form a keyword space

2D keyword space for a P2P file sharing system

3D keyword space for resource sharing, using the attributes: storage space, base bandwidth and cost

network

Documentke

ywo

rd2

computer keyword1

Storage space (MB)

Ba

se

ba

nd

wid

th(M

bp

s)

Computational resource

Cost

30

100

9

comp*

Complex query(comp*, *)

keyword1

ke

ywo

rd2

Storage space (MB)

Ba

se

ba

nd

wid

th

(Mb

ps

)Cost

2025

10

Complex query(10, 20-25, *)

2D keyword space for a P2P file sharing system

3D keyword space for resource sharing, using the attributes: storage space, base bandwidth and cost

network

Documentke

ywo

rd2

computer keyword1

Storage space (MB)

Ba

se

ba

nd

wid

th(M

bp

s)


Cost

30

100

9

comp*


keyword1

ke

ywo

rd2

Storage space (MB)

Ba

se

ba

nd

wid

th

(Mb

ps

)Cost

2025

10


network

Documentke

ywo

rd2

computer keyword1

Storage space (MB)

Ba

se

ba

nd

wid

th(M

bp

s)


Cost

30

100

9

network

Documentke

ywo

rd2

computer keyword1

Storage space (MB)

Ba

se

ba

nd

wid

th(M

bp

s)


Cost

30

100

9

computer keyword1

Storage space (MB)

Ba

se

ba

nd

wid

th(M

bp

s)


Cost

30

100

9

comp*


keyword1

ke

ywo

rd2

Storage space (MB)

Ba

se

ba

nd

wid

th

(Mb

ps

)Cost

2025

10


comp*


keyword1

ke

ywo

rd2

comp*


keyword1

ke

ywo

rd2

Storage space (MB)

Ba

se

ba

nd

wid

th

(Mb

ps

)Cost

2025

10


Storage space (MB)

Ba

se

ba

nd

wid

th

(Mb

ps

)Cost

2025

10


Content Indexing: Hilbert SFC

• f: Nd N, recursive generation

• Properties:– Digital causality

– Locality preserving

– Clustering

1

0

10 00 01 10 11

11

10

01

00

00 11

01 10

0000

0010

0001

0011

0100

01010110

0111

10001001

101010111100

11111110

1101

Cluster: group of cells connected by a segment of the curve

Content Routing/Discovery - Squid

000000

000100

001001

001111

100001

1110000

00

01

000 001 010 011 100 101 110 111

111

110

101

100

011

010

001

000

(011, *)

Query: (Storage space = 30, Bandwidth = *) => Binary query (011110, *)

Storage space

Ban

dw

idth

01

0

1

1100

01 10

00

00 01 10 11

01

10

11

0000 0001

0011

0100

0101 0110

0111 1000

1001 1010

1011

11001101

1110 1111

0010

0001

001

011

0111

…

0

00,01

000101,000110

011111 011010,011011,011100

001001,001010

0110,0111

0001,0010

Query Engine – Experimental Evaluation

• System size: 103 to 106 nodes• Data:

– Uniformly distributed, synthetic generated data

– 4*105 CiteSeer data

• Load balanced system

• Experiments:– Number of clusters

generated for a query

– Number of nodes queried

• All results are plotted on a logarithmic scale

Squid Content Routing/Discovery Engine:Optimization

• Number of clusters generated for queries with coverage 1%, 0.1%, 0.01%, with and without optimization

• The results are normalized against the clusters that the query defines on the curve (i.e. without optimization).

3D Uniformly distributed data 3D CiteSeer data

0.0001

0.001

0.01

0.1

1

10

System size

Nor

mal

ized

nu

mb

erof

clu

ster

s

Clusters 1% query 0.1% query 0.01% query

10 3 10 4 100.00001

0.0001

0.001

0.01

0.1

1

10

System size

Nor

mal

ized

nu

mb

erof

clu

ster

s

Clusters 1% query 0.1% query 0.01% query

10 3 10 4 10

Squid Content Routing/Discovery Engine – Nodes Queried

• Percentage of nodes queried for queries with coverage 1%, 0.1%, 0.01%, with and without optimization

0.01

0.1

1

10

System size

%of

nod

esq

uer

ied

1%1% optimization

0.1%0.1% optimization


10 3 10 4 10 5 100.01

0.1

1

10

System size

%of

nod

esq

uer

ied

10 3 10 4 10 5

1%1% optimization


0.01%0.01% optimizatio

3D Uniformly distributed data 3D CiteSeer data

Semantics of Associative Rendezvous Interactions

• Messages– (header, action, data)– Symmetric post primitive: does not

differentiating between interest/data

• Associative selection– match between interest and data

profiles

• Reactive behavior– Execute action field upon matching

profile credentials

message context

TTL (time to live)

Action

[Data]

Header

• store• retrieve• notify• delete

Profile = list of (attribute, value) pairs:Example: <(sensor_type, temperature), (latitude, 10), (longitude, 20)>

C1

C2

post (<p1, p2>, store, data)

notify_data(C2)

notify(C2)

(1)

(3)

(2) post(<p1, *>, notify_data(C2) )

<p1, p2> <p1, *>

match

Comet Coordination Space

• A virtual global shared-space is constructed from a semantic multi-dimensional information space, which is deterministically mapped onto the system peer nodes

• The space is associatively accessible by all system peer nodes. Access is independent of the physical locations of tuples or hosts– Tuple distribution

• A tuple/template is associated with k keywords

• Squid content-based routing engine used or exact and approximate tuple distribution and retrieval

– Transient spaces• Enable application to explicitly exploit context locality

“COMET: A Scalable Coordination Space in Decentralized Distributed Environments,” Z. Li* and M. Parashar, Proceedings of the 2nd International Workshop on Hot Topics in Peer-to-Peer Systems (HOT-P2P 2005), San Diego, CA, USA, IEEE Computer Society Press, pp. 104 – 111, July 2005.

Coordination primitives

• Basic primitives– Out, In, Rd

• Tuple retrieval– Exact retrieval

• Keys only consist of complete keywords

• Routs to a single destination

– Approximate retrieval • Keys consist of partial

keywords, wildcards

• Routs to multiple destinations

Supporting the Rudder Agent Framework

• Agents communication – associatively reading, writing, and extracting tuples

• Agent coordination protocols– Decentralized election protocol

• Based on wait-free consensus protocols– Resilient to node/link failures

– Discovery protocol• Registry implemented using XML tuples• Element registered using Out • Element unregistered using In • Elements discovered using Rd/RdAll operation

– Interaction protocol• Contract-Net protocol• Two agent bargaining protocol

• Workflow engine

“Enabling Dynamic Composition and Coordination of Autonomic Applications using the Rudder Agent Framework,” Z. Li* and M. Parashar, The Knowledge Engineering Review, Cambridge University Press (also SAACS 2005).

Implementation/Deployment Overview

• Current implementation builds on JXTA– SquidTON, Squid, Comet and

Meteor layers are implemented as event-driven JXTA services

• Deployments include– Campus Grid @ Rutgers

– Orbit wireless testbed (400 nodes)

– PlanetLab wide-area testbed• At least one node selected from

each continent

Outline






The Instrumented Oil Field of the Future (UT-CSM, UT-IG, RU, OSU, UMD, ANL)

Detect and track changes in data during productionInvert data for reservoir propertiesDetect and track reservoir changes

Assimilate data & reservoir properties into the evolving reservoir model

Use simulation and optimization to guide future production, future data acquisition strategy

• Production of oil and gas can take advantage of installed sensors that will monitor the reservoir’s state as fluids are extracted

• Knowledge of the reservoir’s state during production can result in better engineering decisions

– economical evaluation; physical characteristics (bypassed oil, high pressure zones); productions techniques for safe operating conditions in complex and difficult areas

“Application of Grid-Enabled Technologies for Solving Optimization Problems in Data-Driven Reservoir Studies,” M. Parashar, H. Klie, U. Catalyurek, T. Kurc, V. Matossian, J. Saltz and M Wheeler, FGCS. The International Journal of Grid Computing: Theory, Methods and Applications (FGCS), Elsevier Science Publishers, Vol. 21, Issue 1, pp 19-26, 2005.

Effective Oil Reservoir Management: Well Placement/Configuration

• Why is it important – Better utilization/cost-effectiveness of existing reservoirs– Minimizing adverse effects to the environment

Better Management

Less Bypassed Oil

Bad Management

Much Bypassed Oil

Effective Oil Reservoir Management: Well Placement/Configuration

• What needs to be done– Exploration of possible well placements and configurations for

optimized production strategies– Understanding field properties and interactions between and

across subdomains– Tracking and understanding long term changes in field

characteristics

• Challenges – Geologic uncertainty: Key engineering properties unattainable– Large search space: Infinitely many production strategies possible– Complex physical properties and interactions. – Complex numerical models

An Autonomic Well Placement/Configuration Workflow

If guess not in DBinstantiate IPARS

with guess asparameter

Send guesses

MySQLDatabase

If guess in DB:send response to Clientsand get new guess fromOptimizer

OptimizationService

IPARSFactory

SPSA

VFSA

ExhaustiveSearch

DISCOVERclient

client

Generate Guesses Send GuessesStart Parallel

IPARS InstancesInstance connects to

DISCOVER

DISCOVERNotifies ClientsClients interact

with IPARS

AutoMate Programming System/Grid Middleware

History/ Archived

Data

Sensor/Context

DataOil prices, weather,etc.

AutonomicAutonomic OilOil Well Placement/Configuration

permeability Pressure contours3 wells, 2D profile

Contours of NEval(y,z,500)(10)

Requires NYxNZ (450)evaluations. Minimum

appears here.

VFSA solution: “walk”: found after 20 (81) evaluations

AutonomicAutonomic OilOil Well Placement/Configuration (VFSA)

“An Reservoir Framework for the Stochastic Optimization of Well Placement,” V. Matossian, M. Parashar, W. Bangerth, H. Klie, M.F. Wheeler, Cluster Computing: The Journal of Networks, Software Tools, and Applications, Kluwer Academic Publishers, Vol. 8, No. 4, pp 255 – 269, 2005 “Autonomic Oil Reservoir Optimization on the Grid,” V. Matossian, V. Bhat, M. Parashar, M. Peszynska, M. Sen, P. Stoffa and M. F. Wheeler, Concurrency and Computation: Practice and Experience, John Wiley and Sons, Volume 17, Issue 1, pp 1 – 26, 2005.

Outline






Conclusion

• Pervasive Grid Environments & Next Generation Scientific Investigation– Knowledge-based, data and information driven, context-aware,

computationally intensive– Unprecedented opportunity for global scientific investigation

• can enable accurate solutions to complex applications; provide dramatic insights into complex phenomena

– Unprecedented research challenges• scale, complexity, heterogeneity, dynamism, reliability, uncertainty, …• applications, algorithms, measurements, data/information, software

– Project AutoMate: Autonomic Computational Science on the Grid• semantic + autonomics • Accord, Rudder/Comet, Meteor, Squid, Topos, …

• More Information, publications, software– www.caip.rutgers.edu/~parashar/– [email protected]

The Team

• TASSL, CAIP/ECE Rutgers University – Viraj Bhat – Sumir Chandra– Andres Q. Hernandez– Nanyan Jiang– Zhen Li (Jenny) – Vincent Matossian – Cristina Schmidt – Mingliang Wang– Li Zhang

• Key CE/CS Collaborators– Rutgers Univ.

• D. Silver, D. Raychaudhuri, P. Meer, M. Bushnell, etc.

– Univ. of Arizona• S. Hariri

– Ohio State Univ.• T. Kurc, J. Saltz

– GA Tech• K. Schwan, M. Wolf

– University of Maryland• A. Sussman, C. Hansen

• Key Applications Collaborators– Rutgers Univ.

• R. Levy, S. Garofilini

– UMDNJ• D. Foran, M. Reisse

– CSM/IG, Univ. of Texas at Austin• H. Klie, M. Wheeler, M. Sen, P. Stoffa

– ORNL, NYU• S. Klasky, C.S. Chang

– CRL, Sandia National Lab., Livermore• J. Ray, J. Steensland

– Univ. of Arizona/Univ. of Iowa, OSU• T. –C. J. Yeh, J. Daniels, A. Kruger

– Idaho National Laboratory • R. Versteeg

– PPPL• R. Samtaney

– ASCI/CACR, Caltech• J. Cummings

Thank you !

information-driven science in pervasive grid environments manish parashar the applied software...

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data archives

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data reservoir properties

informationdriven science

remote sensing data

realtime information

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