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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 Opportunities
• Pervasive Grid Environments - Unprecedented Challenges– Uncertainty
• System uncertainty• Application uncertainty• Information uncertainty
• Project AutoMate @ TASSL, Rutgers University – Enabling Autonomic Applications in Pervasive Grid Environments
• An Illustrative Application
• Concluding Remarks
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
• 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
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)
Virtualization(Create and manage VOs and VMs)
Abstraction(Support abstract behaviors/assumptions)
Virtualization(Create and manage VOs and VMs)
Abstraction(Support abstract behaviors/assumptions)
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)
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, *)
network
Documentke
ywo
rd2
computer keyword1
Storage space (MB)
Ba
se
ba
nd
wid
th(M
bp
s)
Computational resource
Cost
30
100
9
network
Documentke
ywo
rd2
computer keyword1
Storage space (MB)
Ba
se
ba
nd
wid
th(M
bp
s)
Computational resource
Cost
30
100
9
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, *)
comp*
Complex query(comp*, *)
keyword1
ke
ywo
rd2
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, *)
Storage space (MB)
Ba
se
ba
nd
wid
th
(Mb
ps
)Cost
2025
10
Complex query(10, 20-25, *)
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
0.01%0.01% 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
0.1%0.1% 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
• 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
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
• 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
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 !