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Information-Driven Science in Pervasive Grid Environments Manish Parashar The Applied Software Systems Laboratory ECE/CAIP, 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, Do. E, NIH)

Outline • Pervasive Grid Environments - Unprecedented Opportunities • Pervasive Grid Environments - Unprecedented Outline • Pervasive Grid Environments - Unprecedented Opportunities • Pervasive Grid Environments - Unprecedented Challenges, Opportunities • Project Auto. Mate @ 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 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 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 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 Experts mine archive, match realtime data with history Automated mining & matching Real-time data assimilation/injection (sensors, instruments, experiments, data archives), Models dynamically composed. “Web. Services” discovered & invoked.

Information-driven Management of Subsurface Geosystems: The Instrumented Oil Field (with UT-CSM, UT-IG, OSU, UMD, 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 Dynamic, Data Driven Reservoir Management Dynamic Decision System Dynamic Data-Driven Assimilation Optimize • Economic revenue Management decision • Environmental hazard • … Based on the present subsurface knowledge and numerical model Subsurface characterization Improve knowledge of subsurface to reduce uncertainty Data assimilation Acquire remote sensing data Update knowledge of model Improve numerical model Experimental design START Plan optimal data acquisition

Vision: Diverse Geosystems – Similar Solutions Landfills Oilfields Models Simulation Underground Pollution Control Data 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 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 40 K measurements/day

Dynamic Data-Driven Waste Management Sensors Optimization Algorithms Ruby Gulch Waste Repository Actuators Controllable input 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, 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 Network design bad Comparison With observations good Application

Data-Driven Forest Fire Simulation (U of AZ) • Predict the behavior and spread of 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 5 th 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 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. Synthetic Environment for Continuous Experimentation – Purdue University Source: A. Chaturvedi, Purdue Univ.

Integrated Wireless Phone Based Emergency Response Syst • • Detect abnormal patterns in mobile Integrated Wireless Phone Based Emergency Response Syst • • Detect abnormal patterns in mobile call activity and locations Initiate dynamic data driven simulations to predict the evolution of the abn 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, 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 Outline • Pervasive Grid Environments - Unprecedented Opportunities • Pervasive Grid Environments - Unprecedented Challenges – Uncertainty • System uncertainty • Application uncertainty • Information uncertainty • Project Auto. Mate @ 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 Pervasive Grid Applications – Unprecedented Challenges: Uncertainty • System Uncertainty – Very large scales – Ad hoc (amorphous) structures/behaviors • p 2 p, hierarchical, etc, architectures – Dynamic • entities join, leave, move, change behavior – Heterogeneous • capability, connectivity, reliability, guarantees, Qo. S – 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 • 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 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 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 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 Outline • Pervasive Grid Environments - Unprecedented Opportunities • Pervasive Grid Environments - Unprecedented Challenges, Opportunities • Project Auto. Mate @ 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 + 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 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 Auto. Mate: Enabling Autonomic Applications • Conceptual models and implementation architectures – programming Project Auto. Mate: 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 Auto. Mate: Components • Accord – A Programming System for Autonomic Grid Applications Project Auto. Mate: 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 “Auto. Mate: 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 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 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 Interaction rules The Accord Runtime Infrastructure Application strategies Application requirements Application workflow Composition manager Interaction rules Behavior rules

LLC Based Self Management within Accord LLC Controller Self-Managing Element LLC Controller Optimization Function LLC Based Self Management within Accord LLC Controller Self-Managing Element LLC Controller Optimization Function Computational Element Model Advice 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 Decentralized (Decoupled/Asynchronous) Content-based Middleware Services

Squid. TON: Reliability and Fault Tolerance • Pervasive Grid systems are dynamic, with nodes Squid. TON: 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 • Squid. TON = 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

Content Descriptors and Information Space • Data element = a piece of information that 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

Content Indexing: Hilbert SFC • f: Nd N, recursive generation 01 10 1 0100 Content Indexing: Hilbert SFC • f: Nd N, recursive generation 01 10 1 0100 0111 0010 00 0 11 0 1 0000 0001 00 01 1000 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 = *) => Content Routing/Discovery - Squid Bandwidth Query: (Storage space = 30, Bandwidth = *) => Binary query (011110, *) 1 11 0101 0110 1001 10 0100 11 00 0 1 (011, *) 110 10 01 01 0 111 1010 00 Storage space 0111 1000 1011 0010 1101 0000 011 0001 1110 1111 00 01 10 … 100 1100 010 001 000 11 000 001 010 011 100 101 110 111 00 0 000000 0 111000 0001 01 00, 01 000100 001 0110, 0111 0001, 0010 100001 001001 000101, 000110 0111 001111 001001, 001010 011111 011010, 011011, 011100

Query Engine – Experimental Evaluation • System size: 103 to 106 nodes • Data: Query Engine – Experimental Evaluation • System size: 103 to 106 nodes • Data: – Uniformly distributed, synthetic generated data – 4*105 Cite. Seer 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 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). 3 D Uniformly distributed data 3 D Cite. Seer data

Squid Content Routing/Discovery Engine – Nodes Queried • Percentage of nodes queried for queries Squid Content Routing/Discovery Engine – Nodes Queried • Percentage of nodes queried for queries with coverage 1%, 0. 1%, 0. 01%, with and without optimization 3 D Uniformly distributed data 3 D Cite. Seer data

Semantics of Associative Rendezvous Interactions profile credentials • Messages Header – (header, action, data) Semantics of Associative Rendezvous Interactions profile credentials • Messages Header – (header, action, data) – Symmetric post primitive: does not differentiating between interest/data Action • Associative selection [Data] – match between interest and data profiles message context TTL (time to live) • 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 (

, store, data)

(1) C 1

match (2) post(

, notify_data(C 2) ) notify_data(C 2) (3) notify(C 2) C 2

Comet Coordination Space • A virtual global shared-space is constructed from a semantic multi-dimensional 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 2 nd International Workshop on Hot Topics in Peer-to-Peer Systems (HOT-P 2 P 2005), San Diego, CA, USA, IEEE Computer Society Press, pp. 104 – 111, July 2005.

Coordination primitives • Basic primitives – Out, In, Rd • Tuple retrieval – Exact 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 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/Rd. All 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 – Squid. TON, Squid, Comet and Implementation/Deployment Overview • Current implementation builds on JXTA – Squid. TON, Squid, Comet and Meteor layers are implemented as event-driven JXTA services • Deployments include – Campus Grid @ Rutgers – Orbit wireless testbed (400 nodes) – Planet. Lab wide-area testbed • At least one node selected from each continent

Outline • Pervasive Grid Environments - Unprecedented Opportunities • Pervasive Grid Environments - Unprecedented Outline • Pervasive Grid Environments - Unprecedented Opportunities • Pervasive Grid Environments - Unprecedented Challenges, Opportunities • Project Auto. Mate @ 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) • 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/Configu • Why is it important – Better utilization/cost-effectiveness Effective Oil Reservoir Management: Well Placement/Configu • 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 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 History/ Archived Data Auto. Mate Programming System/Grid Middleware Sensor/C An Autonomic Well Placement/Configuration Workflow History/ Archived Data Auto. Mate Programming System/Grid Middleware Sensor/C ontext Data Oil prices, weather, etc.

Autonomic Oil Well Placement/Configuration Contours of NEval(y, z, 500)(10) permeability Pressure contours 3 wells, Autonomic Oil Well Placement/Configuration Contours of NEval(y, z, 500)(10) permeability Pressure contours 3 wells, 2 D profile Requires NYx. NZ (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 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 Outline • Pervasive Grid Environments - Unprecedented Opportunities • Pervasive Grid Environments - Unprecedented Challenges, Opportunities • Project Auto. Mate @ 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 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 Auto. Mate: 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] rutgers. edu

The Team • TASSL, CAIP/ECE Rutgers University – – – – – Viraj Bhat 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 – 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 • 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 • T. –C. J. Yeh, J. Daniels, A. Kruger – Idaho National Laboratory • R. Versteeg – PPPL • R. Samtaney – ASCI/CACR, Caltech • J. Cummings

Thank you ! Thank you !