Information-Driven Science in Pervasive
Grid Environments
Manish Parashar
The Applied Software Systems Laboratory
ECE/CAIP, Rutgers University
http://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, multiinstitutional virtual organizations.
Source: I. Foster et al
Pervasive Grid Environments and Information Driven
Science
Resources discovered,
negotiated, co-allocated
on-the-fly.
Model/Simulation
deployed
Experts query,
configure
resources
Experts interact and
collaborate using
ubiquitous and
pervasive portals
Experts monitor/interact with/interrogate/steer
models (“what if” scenarios,…). Application
notifies experts of interesting phenomenon.
Models
write into
the archive
Real-time data
assimilation/injection
(sensors, instruments,
experiments, data
archives),
Experts mine
archive, match
real-time data with
history
Automated
mining &
matching
Models
dynamically
composed.
“WebServices”
discovered &
invoked.
Information-driven Management of Subsurface Geosystems: The
Instrumented Oil Field (with UT-CSM, UT-IG, OSU, UMD, ANL)
Model
Driven
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
Dynamic, Data Driven Reservoir Management
Dynamic Decision
System
Dynamic Data-Driven
Assimilation
Optimize
• Economic revenue
Management decision
• Environmental hazard
•…
Subsurface characterization
Based on the present subsurface knowledge
and numerical model
Improve knowledge
of subsurface to
reduce uncertainty
Data assimilation
Update knowledge
of model
Acquire remote
sensing data
Improve numerical
model
Plan optimal data
acquisition
Experimental design
START
Vision: Diverse Geosystems – Similar Solutions
Landfills
Oilfields
Models Simulation
Underground
Pollution
Control
Data
Undersea
Reservoirs
Management of the Ruby Gulch Waste Repository (with
UT-CSM, INL, OU)
• 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.
– Flowmeter at bottom of dump
– Weather-station
– Manually sampled chemical/air
ports in wells
– Approx 40K measurements/day
Dynamic Data-Driven Waste Management
Sensors
Optimization
Algorithms
Ruby Gulch
Waste Repository
Actuators
Controllable input
Data Assimilation
Optimization
Observations
Control
algorithms
Models, Surrogate/
Reduced models
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
System
responses
Inverse
Modeling
Parameters,
Boundary & Initial
Conditions
Forward
Modeling
Prediction
bad
Network
design
Comparison
With observations
good
Application
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 selfmanagement
• 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
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
Applications
Programming System
(Programming Model + Abstract Machine)
Abstraction
(Support abstract behaviors/assumptions)
Grid Middleware Infrastructure
Virtualization
(Create and manage VOs and VMs)
Virtual Machine Virtual Machine Virtual Machine
– Hide or expose uncertainty?
• robustness, ease of programming
• the inverted stack …
Virtual Organization
Organization
Organization
Organization
“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
Ontology, Taxonomy
Programming System
Autonomic Components, Dynamic Composition,
Opportunistic Interactions, Collaborative Monitoring/
Control
Decentralized Coordination Engine
Agent Framework,
Decentralized Reactive Tuple Space
Semantic Middleware Services
Content-based Discovery, Associative Messaging
Content Overlay
Content-based Routing Engine,
Self-Organizing Overlay
Rudder
Coordination
Middleware
Meteor/Squid
Content-based
Middleware
Autonomic Grid Applications
Sesame/DAIS Protection Service
Accord
Programming
Framework
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
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 Contentbased 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
Event
generation
Element Manager
Other
Interface
invocation
Actuator
invocation
Functional Port
Computational
Element
Control
Port
Element Manager
Operational Port
Autonomic Element
Internal
state
Rules
Contextual
state
The Accord Runtime Infrastructure
Application strategies
Application requirements
Application workflow
Composition manager
Interactio
n rules
Interactio
n rules
Interactio
n rules
Interactio
n rules
Behavior
rules
Behavior
rules
Behavior
rules
Behavior
rules
LLC Based Self Management within Accord
LLC Controller
Self-Managing Element
LLC Controller
Optimization
Function
Computational
Element Model
Advice
Element Manager
Element Manager
Computational
Element
Contextual
State
Internal
State
• 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
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
–
10 17 22
10
17
25
249
240
231
33
249
22
240
33
41
82
192
86
185
91
94
192
25
231 250
82
185
86
102
128 161
94
91
132
157
141
157
150
150
141
132
128
Groups of nodes
Group identifier
Content Descriptors and Information Space
• Data element = a piece of information that is indexed and discovered
– Data, documents, resources, services, metadata, messages, events, etc.
network
computer
2D keyword space
for a P2P file
sharing system
Base bandwidth
(Mbps)
t
os
C
9
30
Storage space (MB)
Complex query
(comp*, *)
comp*
keyword1
Computational
resource
100
keyword2
Document
keyword1
Complex query
(10, 20-25, *)
20
Base bandwidth
(Mbps)
keyword2
• Each data element has a set of keywords associated with it, which
describe its content => data elements form a keyword space
25
3D keyword space
for resource
sharing, using the
attributes: storage
space, base
bandwidth and cost
Co
10
st
Storage space (MB)
Content Indexing: Hilbert SFC
• f: Nd  N, recursive generation
01
10
1
0101
0100
0110
1000
0111
0011 0010
00
11
0
0000 0001
0
1
00
01
1011
1100
1110
10
1001
11
1010 10
1101 01
1111 00
11
• Properties:
– Digital causality
– Locality preserving
– Clustering
Cluster: group of cells connected
by a segment of the curve
Content Routing/Discovery - Squid
Bandwidth
Query: (Storage space = 30, Bandwidth = *) => Binary query (011110, *)
1
11 0101 0110 1001
0111
01
1000
101
1011
00
0
1
Storage space
…
100
011
0011
0010 1101
0000
0001 1110 1111
1100
010
11
00
(011, *)
110
10
01
10 0100
0
111
1010
001
00
01
10
000
000 001 010 011 100 101 110 111
11
00
0
000000
0
111000
0001
01
00,
01
000100
001
0001,
0010
100001
0110,
0111
001001
000101,
000110
011
0111
001111
001001,
001010
011111
011010,
011011,
011100
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).
10
Normalized number of clusters
Normalized number of clusters
10
1
0.1
0.01
0.001
0.0001
10 3
10 4
10 5
System size
Clusters
1% query
0.1% query
0.01% query
3D Uniformly distributed data
1
0.1
0.01
0.001
0.0001
0.00001
10 3
Clusters
10 4
System size
1% query
0.1% query
3D CiteSeer data
10 5
0.01% query
Squid Content Routing/Discovery Engine – Nodes
Queried
• Percentage of nodes queried for queries with coverage 1%,
0.1%, 0.01%, with and without optimization
10
% of nodes queried
% of nodes queried
10
1
0.1
0.01
10 3
10 4
10 5
10 6
System size
1%
1% optimization
0.1%
0.1% optimization
1
0.1
0.01
10 3
10 4
1%
1% optimization
0.1%
0.1% optimization
10 5
System size
0.01%
0.01% optimization
3D Uniformly distributed data
3D CiteSeer data
0.01%
0.01% optimization
Semantics of Associative Rendezvous
Interactions
profile credentials
• Messages
Header
– (header, action, data)
– Symmetric post primitive: does not
differentiating between interest/data
message context
TTL (time to live)
Action
• Associative selection
[Data]
– match between interest and data
profiles
• store
• retrieve
• notify
• delete
Profile = list of (attribute, value) pairs:
• Reactive behavior
Example:
<(sensor_type, temperature), (latitude, 10), (longitude, 20)>
– Execute action field upon matching
post (<p1, p2>, store, data)
<p1, p2>
(1)
C1
<p1, *>
match
(2) post(<p1, *>, notify_data(C2) )
notify_data(C2)
(3)
notify(C2)
C2
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)
•
•
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
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, future
data acquisition strategy
“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
Bad Management
Better Management
Much Bypassed Oil
Less 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
Generate Guesses
Start Parallel Instance connects to
DISCOVER
IPARS Instances
Send Guesses
DISCOVER
Notifies Clients
Clients interact
with IPARS
SPSA
Send guesses
VFSA
DISCOVER
Optimization
Service
IPARS
Factory
client
Exhaustive
Search
If guess in DB:
send response to Clients
and get new guess from
Optimizer
History/
Archived
Data
client
MySQL
Database
If guess not in DB
instantiate IPARS
with guess as
parameter
Sensor/C
ontext
Data
AutoMate Programming System/Grid Middleware
Oil prices,
weather,etc.
Autonomic Oil Well Placement/Configuration
Contours of NEval(y,z,500)(10)
permeability
Pressure contours
3 wells, 2D profile
Requires NYxNZ (450)
evaluations. Minimum
appears here.
VFSA solution: “walk”:
found after 20 (81) evaluations
Autonomic Oil 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/
– parashar@caip.rutgers.edu
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 Applications Collaborators
–
•
–
–
Key CE/CS Collaborators
–
Rutgers Univ.
•
–
Univ. of Arizona
•
–
T. Kurc, J. Saltz
GA Tech
•
–
S. Hariri
Ohio State Univ.
•
–
D. Silver, D. Raychaudhuri, P. Meer, M.
Bushnell, etc.
K. Schwan, M. Wolf
University of Maryland
•
A. Sussman, C. Hansen
T. –C. J. Yeh, J. Daniels, A. Kruger
Idaho National Laboratory
•
R. Versteeg
PPPL
•
–
J. Ray, J. Steensland
Univ. of Arizona/Univ. of Iowa, OSU
•
–
S. Klasky, C.S. Chang
CRL, Sandia National Lab., Livermore
•
–
H. Klie, M. Wheeler, M. Sen, P. Stoffa
ORNL, NYU
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D. Foran, M. Reisse
CSM/IG, Univ. of Texas at Austin
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R. Levy, S. Garofilini
UMDNJ
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Rutgers Univ.
R. Samtaney
ASCI/CACR, Caltech
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J. Cummings
Thank you !