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Addressing Complexity in Emerging Cyber-Ecosystems – Experiments with Autonomic Computational Science Manish Parashar* Center for Autonomic Computing The.

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Presentation on theme: "Addressing Complexity in Emerging Cyber-Ecosystems – Experiments with Autonomic Computational Science Manish Parashar* Center for Autonomic Computing The."— Presentation transcript:

1 Addressing Complexity in Emerging Cyber-Ecosystems – Experiments with Autonomic Computational Science Manish Parashar* Center for Autonomic Computing The Applied Software Systems Laboratory Rutgers, The State University of New Jersey *In collaboration with S. Jha & O. Rana

2 eSI Visitor Seminar – 01/13/10 Outline of My Presentation Computational Ecosystems –Unprecedented opportunities, challenges Autonomic computing – A pragmatic approach for addressing complexity! Experiments with autonomics for science and engineering Concluding Remarks

3 eSI Visitor Seminar – 01/13/10 The Cyberinfrastructure Vision Cyberinfrastructure integrates hardware for computing, data and networks, digitally-enabled sensors, observatories and experimental facilities, and an interoperable suite of software and middleware services and tools… - NSFs Cyberinfrastructure Vision for 21st Century Discovery A global phenomenon; several LARGE deployments –UK National Grid Service (NGS) /European Grid Infrastructure (EGI), TeraGrid, Open Science Grid (OSG), EGEE, Cybera, DEISA, etc., etc. New capabilities for computational science and engineering –seamless access resources, services, data, information, expertise, … –seamless aggregation –seamless (opportunistic) interactions/couplings

4 Cyberinfrastructure => Cyber-Ecosystems 21 st century Science and Engineering: New Paradigms & Practices Fundamentally data-driven/data intensive Fundamentally collaborative

5 eSI Visitor Seminar – 01/13/10 Unprecedented opportunities for Science/Engineering Knowledge-based, information/data-driven, context/content- aware computationally intensive, pervasive applications –Crisis management, monitor and predict natural phenomenon, monitor and manage engineered systems, optimize business processes Addressing applications in an end-to-end manner! –Opportunistically combine computations, experiments, observations, data, to manage, control, predict, adapt, optimize, … New paradigms and practices in science and engineering? –How can it benefit current applications? –How can it enable new thinking in science?

6 eSI Visitor Seminar – 01/13/10 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 Model Driven

7 eSI Visitor Seminar – 01/13/10 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, Source: M. Rotea, NSF

8 eSI Visitor Seminar – 01/13/10 The Challenge: Managing Complexity, Uncertainty System –Very large scales –Disruptive trends many/multi-cores, accelerators, clouds –Heterogeneity capability, connectivity, reliability, guarantees, QoS –Dynamics Ad hoc structures, failure –Distributed system! Lack of guarantees, common/complete knowledge, … –Emerging concerns Power, resilience, … Data and Information –Scale, heterogeneity –Availability, resolution, quality –Semantics, meta data, data models, provenance –Trust in data, …. Application –Compositions –Dynamic behaviors –Dynamic and complex couplings –Software/systems engineering issues Emergent rather than by design

9 eSI Visitor Seminar – 01/13/10 The Challenge: Managing Complexity, Uncertainty (I) Increasing application, data/information, system complexity –Scale, heterogeneity, dynamism, unreliability, … New application formulations, practices –Data intensive and data driven, coupled, multiple physics/scales/resolution, adaptive, compositional, workflows, etc. Complexity/uncertainty must be simultaneously addressed at multiple levels –Algorithms/Application formulations Asynchronous/chaotic, failure tolerant, … –Abstractions/Programming systems Adaptive, application/system aware, proactive, … –Infrastructure/Systems Decoupled, self-managing, resilient, …

10 eSI Visitor Seminar – 01/13/10 The Challenge: Managing Complexity, Uncertainty (II) The ability of scientists to realize the potential of computational ecosystems is being severely hampered due to the increased complexity and dynamism of the applications and computing environments. To be productive, scientists often have to comprehend and manage complex computing configurations, software tools and libraries as well as application parameters and behaviors. Autonomics and self-* can help ? (with the plumbing for starters…)

11 eSI Visitor Seminar – 01/13/10 Outline of My Presentation Computational Ecosystems –Unprecedented opportunities, challenges Autonomic computing – A pragmatic approach for addressing complexity! Experiments with autonomics for science and engineering Concluding Remarks

12 eSI Visitor Seminar – 01/13/10 The Autonomic Computing Metaphor Current paradigms, mechanisms, management tools are inadequate to handle the scale, complexity, dynamism and heterogeneity of emerging systems and applications Nature has evolved to cope with scale, complexity, heterogeneity, dynamism and unpredictability, lack of guarantees –self configuring, self adapting, self optimizing, self healing, self protecting, highly decentralized, heterogeneous architectures that work !!! Goal of autonomic computing is to enable self-managing systems/applications that addresses these challenges using high level guidance –Unlike AI duplication of human thought is not the ultimate goal! Autonomic Computing: An Overview, M. Parashar, and S. Hariri, Hot Topics, Lecture Notes in Computer Science, Springer Verlag, Vol. 3566, pp , 2005.

13 eSI Visitor Seminar – 01/13/10 Motivations for Autonomic Computing Source: /12/07: 20K people + 60 planes held at LAX after computer failure prevented customs from screening arrivals 8/3/07: (EPA) datacenter energy use by 2011 will cost $7.4 B, 15 power plants, 15 Gwatts/hour peak Source:http://www.almaden.ibm.com/almaden/talks/Morris_AC_10-02.pdfhttp://www.almaden.ibm.com/almaden/talks/Morris_AC_10-02.pdf Key Challenge Current levels of scale, complexity and dynamism make it infeasible for humans to effectively manage and control systems and applications 2/27/07: Dow fell 546. Since worst plunge took place after 2:30 pm, trading limits were not activated 8/1/06: UK NHS hit with massive computer outage. 72 primary care + 8 acute hospital trusts affected.

14 eSI Visitor Seminar – 01/13/10 Autonomic Computing – A Pragmatic Approach Separation + Integration + Automation ! Separation of knowledge, policies and mechanisms for adaptation The integration of self–configuration, – healing, – protection,– optimization, … Self-* behaviors build on automation concepts and mechanisms –Increased productivity, reduced operational costs, timely and effective response System/Applications self-management is more than the sum of the self-management of its individual components M. Parashar and S. Hariri, Autonomic Computing: Concepts, Infrastructure, and Applications, CRC Press, Taylor & Francis Group, ISBN , 2007.

15 eSI Visitor Seminar – 01/13/10 Autonomic Computing Theory Integrates and advances several fields –Distributed computing Algorithms and architectures –Artificial intelligence Models to characterize, predict and mine data and behaviors –Security and reliability Designs and models of robust systems –Systems and software architecture Designs and models of components at different IT layers –Control theory Feedback-based control and estimation –Systems and signal processing theory System and data models and optimization methods Requires experimental validation (From S. Dobson et al., ACM Tr. on Autonomous & Adaptive Systems, Vol. 1, No. 2, Dec )

16 eSI Visitor Seminar – 01/13/10 Some Information Sources Autonomic Computing: Concepts, Infrastructure and Applications, M. Parashar and S. Hariri (Ed.), CRC Press, ISBN (Available at NSF Center on Autonomic Computing –http://nsfcac.rutgers.eduhttp://nsfcac.rutgers.edu –http://www.nsfcac.orghttp://www.nsfcac.org Autonomic Computing Portal –http://www.autnomiccomputing.orghttp://www.autnomiccomputing.org IEEE International Conference on Autonomic Computing –http://www.autonomic-conference.orghttp://www.autonomic-conference.org IEEE Task Force on Autonomous and Autonomic Systems –http://tab.computer.org/aas/http://tab.computer.org/aas/

17 eSI Visitor Seminar – 01/13/10 Autonomics for Science and Engineering ? Autonomic computing aims at developing systems and application that can manage and optimize themselves using only high-level guidance or intervention from users –dynamically adapt to changes in accordance with business policies and objectives and take care of routine elements of management Separation of management and optimization policies from enabling mechanisms –allows a repertoire of a mechanisms to be automatically orchestrated at runtime to respond to heterogeneity, dynamics, etc. E.g., develop strategies that are capable of identifying and characterizing patterns at design and at runtime and, using relevant (dynamically defined) policies, managing and optimizing the patterns. Application, Middleware, Infrastructure Manage application/information/system complexity not just hide it! Enabling new thinking, formulations how do I think about/formalize my problem differently?

18 eSI Visitor Seminar – 01/13/10 A Conceptual Framework for ACS (GMAC 07, with S. Jha and O. Rana) Hierarchical Within and across level …

19 eSI Visitor Seminar – 01/13/10 Crosslayer Autonomics

20 eSI Visitor Seminar – 01/13/10 Existing Autonomic Practices in Computational Science (GMAC 09, SOAR 09, with S. Jha and O. Rana) Autonomic tuning by the application Autonomic tuning of the application

21 eSI Visitor Seminar – 01/13/10 Spatial, Temporal and Computational Heterogeneity and Dynamics in SAMR Simulation of combustion based on SAMR (H2-Air mixture; ignition via 3 hot-spots) Temperature OH Profile Temporal Heterogeneity Spatial Heterogeneity Courtesy: Sandia National Lab

22 eSI Visitor Seminar – 01/13/10 Autonomics in SAMR Tuning by the application –Application level: when and where to refine –Runtime/Middleware level: When, where, how to partition and load balance –Runtime level: When, where, how to partition and load balance –Resource level: Allocate/de-allocate resources Tuning of the application, runtime –When/where to refine –Latency aware ghost synchronization –Heterogeneity/Load-aware partitioning and load-balancing –Checkpoint frequency –Asynchronous formulations –…

23 eSI Visitor Seminar – 01/13/10 Outline of My Presentation Computational Ecosystems –Unprecedented opportunities, challenges Autonomic computing – A pragmatic approach for addressing complexity! Experiments with autonomics for science and engineering Concluding Remarks

24 eSI Visitor Seminar – 01/13/10 Autonomics for Science and Engineering – Application-level Examples Autonomic to address complexity in science and engineering Autonomic as a paradigm for science and engineering Some examples: –Autonomic runtime management – multiphysics, adaptive mesh refinement –Autonomic data streaming and in-network data processing – coupled simulations –Autonomic deployment/scheduling – HPC Grid/Cloud integration –Autonomic workflows – simulation based optimization (Many system level examples not presented here …)

25 eSI Visitor Seminar – 01/13/10 Adaptive Methods in Science and Engineering Multi-block grid structure and oil concentrations contours (IPARS, M. Peszynska, UT Austin) Blast wave in the presence of a uniform magnetic field) – 3 levels of refinement. (Zeus + GrACE + Cactus, P. Li, NCSA, UCSD) Mixture of H 2 and Air in stoichiometric proportions with a non-uniform temperature field (GrACE + CCA, Jaideep Ray, SNL, Livermore) Richtmyer-Meshkov - detonation in a deforming tube - 3 levels. Z=0 plane visualized on the right (VTF + GrACE, R. Samtaney, CIT)

26 eSI Visitor Seminar – 01/13/10 Autonomic (Physics/Model/System Driven) Runtime Management Optimization Monitoring & Context-aware Services Characterization System State Capability Synthesizer Resource Monitoring Service CPU Memory Bandwidth Availability Access Policy Nature of Adaptation Application Dynamics Application State Decision-making engine Execution Application Runtime Manager Partition/Compose Application Monitoring Service Deductive Engine Policy Repository Knowledge Base Self-learning VCU Application Computation Unit VCU Processing Unit Mapping Distribution Redistribution Repartition/Recompose Computation/ communication Hybrid Runtime Management of Space-Time Heterogeneity for Dynamic SAMR Applications, X. Li and M. Parashar, IEEE TPDS 18(8), pp – 1214, August 2007.

27 eSI Visitor Seminar – 01/13/10 Cross-layer Adaptations for SAMR Efficiency Performance Survivability When resources are under-utilized When resources are scarce ALP: Trade in space (resource) for time (performance) ALOC: Trade in time (performance) for space (resource)

28 eSI Visitor Seminar – 01/13/10 Experimental Results - ALP Experiment Setup: IBM SP4 cluster (DataStar at San Diego Supercomputing Center, total 1632 processors) SP4 (p655) node: 8 processors(1.5 GHz), memory 16 GB, 6.0 GFlops Performance gain up to 40% on 512 processors

29 eSI Visitor Seminar – 01/13/10 Effects of Finite Memory - ALOC Intel Pentium 4 CPU 1.70GHz, Linux 2.4 kernel Cache size: 256 KB, Physical memory: 512 M, Swap space: 1 G.

30 eSI Visitor Seminar – 01/13/10 Experimental Results - ALOC Boewulf Cluster (Frea at Rutgers, 64 processors) Intel Pentium 4 CPU 1.70GHz, Linux 2.4 kernel Cache size: 256 KB, Physical memory: 512 M, Swap space: 1 G.

31 eSI Visitor Seminar – 01/13/10 Coupled Fusion Simulations: A Data Intensive Workflow

32 eSI Visitor Seminar – 01/13/10 Autonomic Data Streaming and In-Transit Processing for Data-Intensive Workflows Large-scale distributed environments and data intensive workflows –Applications entities separated in space and time –Seamless interactions and couplings across entities Distributed application entities need to interact at runtime –Data processing, interactive data monitoring, online data analysis, visualization, data/service/vm migration, data archiving, collaboration, etc. Large data volumes and rates, heterogeneous data types –Must be streamed efficiently and effectively between distributed application components –Application-specific manipulations need to be applied in-transit An Self-Managing Wide-Area Data Streaming Service, V. Bhat*, M. Parashar, H. Liu*, M. Khandekar*, N. Kandasamy, S. Klasky, and S. Abdelwahed, Cluster Computing: The Journal of Networks, Software Tools, and Applications, Volume 10, Issue 7, pp. 365 – 383, December 2007.

33 eSI Visitor Seminar – 01/13/10 Autonomic Data Streaming and In-Transit Processing for Data-Intensive Workflows Workflow with coupled simulation codes, i.e., the edge turbulence particle-in-cell (PIC) code (GTC) and the microscopic MHD code (M3D) -- run simultaneously on separate HPC resources Data streamed and processed enroute -- e.g. data from the PIC codes filtered through noise detection processes before it can be coupled with the MHD code Efficiently data streaming between live simulations -- to arrive just-in- time -- if it arrives too early, times and resources will have to be wasted to buffer the data, and if it arrives too late, the application would waste resources waiting for the data to come in Opportunistic use of in-transit resources An Self-Managing Wide-Area Data Streaming Service, V. Bhat*, M. Parashar, H. Liu*, M. Khandekar*, N. Kandasamy, S. Klasky, and S. Abdelwahed, Cluster Computing: The Journal of Networks, Software Tools, and Applications, Volume 10, Issue 7, pp. 365 – 383, December 2007.

34 eSI Visitor Seminar – 01/13/10 Autonomic Data Streaming & In-Transit Processing –Application level Proactive QoS management strategies using model-based LLC controller Capture constraints for in-transit processing using slack metric –In-transit level Opportunistic data processing using dynamic in-transit resource overlay Adaptive run-time management at in-transit nodes based on slack metric generated at application level –Adaptive buffer management and forwarding Application Level Proactive management Simulation LLC Controller Slack metric Generator In-Transit node Simulation Slack metric Generator In-Transit Level Reactive management Slack metric corrector Coupling Slack metric corrector Budget estimation Slack metric adjustment metric updates Sink Data flow

35 eSI Visitor Seminar – 01/13/10 Autonomics for Coupled Fusion Simulation Workflows

36 eSI Visitor Seminar – 01/13/10 Autonomic Streaming: Implementation/Deployment Simulation Workflow –SS = Simulation Service (GTC) –ADSS = Autonomic Data Streaming Service CBMS = LLC Controller based buffer management service DTS = Data Transfer service –DAS = Data Analysis Service –SLAMS = Slack Manager Service –PS = Processing Service –BMS = Buffer Management Service –ArchS = Archiving data at sink Sort data Scale data Data Producers SS NERSC Rutgers University ADSS ArchS DAS CBMSDTS DAS SS ORNL ADSS Data In-Transit Data Consumers SLAMS DTS PS PPPL FFT DAS Rutgers University VisS DAS BMS SLAMS BudjS SLAMS Sink SLAMS FFT Simulations executes on leadership class machines at ORNL and NERSC In-transit nodes located at PPPL and Rutgers

37 eSI Visitor Seminar – 01/13/10 Adaptive Data Transfer No congestion in intervals 1-9 –Data transferred over WAN Congested at intervals 9-19 –Controller recognizes this congestion and advises the Element Manager, which in turn adapts DTS to transfer data to local storage (LAN). Adaptation continues until the network is not congested –Data sent to the local storage by the DTS falls to zero at the 19 th controller interval.

38 eSI Visitor Seminar – 01/13/10 Adaptation of the Workflow Create multiple instances of the Autonomic Data Streaming Service (ADSS) –Effective Network Transfer Rate dips below the threshold (our case around 100Mbs) Transfer Simulation ADSS-0 Data Transfer Buffer Data Transfer Buffer Data Transfer Buffer ADSS-1 ADSS-2 % Network throughput is difference between the max and current network transfer rate

39 eSI Visitor Seminar – 01/13/10 %Buffer In-Transit Nodes w & w/o Coupling %Buffer occupancy at in-transit nodes before congestion is around 50% During congestion application level controller throttles data items –%Buffer occupancy at in-transit nodes reduces from 80% without coupling to 60.8% with coupling Higher %buffer occupancies at in-transit nodes lead to failures & loss of data

40 eSI Visitor Seminar – 01/13/10 Reservoir Characterization: EnKF-based History Matching (with S. Jha) Black Oil Reservoir Simulator –simulates the movement of oil and gas in subsurface formations Ensemble Kalman Filter –computes the Kalman gain matrix and updates the model parameters of the ensembles Hetergeneous, dynamic workflows Based on Cactus, PETSc

41 eSI Visitor Seminar – 01/13/10 Experiment Background and Set-Up (2/2) Key metrics –Total Time to Completion (TTC) –Total Cost of Completion (TCC) Basic assumptions –TG gives the best performance but is relatively more restricted resource. –EC2 is a relatively more freely available but is not as capable. Note that the motivation of our experiments is to understand each of the usage scenarios and their feasibility, behaviors and benefits, and not to optimize the performance of any one scenario.

42 eSI Visitor Seminar – 01/13/10 Establishing Baseline Performance Baseline TTC for EC2 and TG for a 1-stage, 128 ensemble member EnKF run. The first 4 bars represent the TTC as the number of EC2 VMs increase; the next 4 bars represent the TTC as the number of CPUs (nodes) used increases.

43 eSI Visitor Seminar – 01/13/10 Autonomic Integration of HPC Grids & Clouds (with S. Jha) Acceleration: Clouds used as accelerators to improve the application time-to-completion –alleviate the impact of queue wait times or exploit an additionally level of parallelism by offloading appropriate tasks to Cloud resources Conservation: Clouds used to conserve HPC Grid allocations, given appropriate runtime and budget constraints Resilience: Clouds used to handle unexpected situations –handle unanticipated HPC Grid downtime, inadequate allocations or unanticipated queue delays

44 eSI Visitor Seminar – 01/13/10 Objective I: Using Clouds as Accelerators for HPC Grids (1/2) Explore how Clouds (EC2) can be used as accelerators for HPC Grid (TG) work-loads –16 TG CPUs (1 node on Ranger) –average queuing time for TG was set to 5 and 10 minutes. –the number of EC2 nodes from 20 to 100 in steps of 20. –VM start up time was about 160 seconds

45 eSI Visitor Seminar – 01/13/10 Objective I: Using Clouds as Accelerators for HPC Grids (2/2) The TTC and TCC for Objective I with 16 TG CPUs and queuing times set to 5 and 10 minutes. As expected, more the number of VMs that are made available, the greater the acceleration, i.e., lower the TTC. The reduction in TTC is roughly linear, but is not perfectly so, because of a complex interplay between the tasks in the work load and resource availability

46 eSI Visitor Seminar – 01/13/10 Objective II: Using Clouds for Conserving CPU-Time on the TeraGrid Explore how to conserve fixed allocation of CPU hours by offloading tasks that perhaps dont need the specialized capabilities of the HPC Grid Distribution of tasks across EC2 and TG, TTC and TCC, as the CPU-minute allocation on the TG is increased.

47 eSI Visitor Seminar – 01/13/10 Objective III: Response to Changing Operating Conditions (Resilience) (1/4) Explore the situation where resources that were initially planned for, become unavailable at runtime, either in part or in entirety –How can Cloud services be used to address this situations and allow the system/application to respond to a dynamic change in availability of resources. Initially 16 TG CPUs for 800 minutes allocated. After about 50 minutes of execution (i.e., 3 Tasks were completed on the TG), available CPU time is change to only 20 CPU minutes remain

48 eSI Visitor Seminar – 01/13/10 Objective III: Response to Changing Operating Conditions (Resilience) (2/4) Allocation of tasks to TG CPUs and EC2 nodes for usage mode III. As the 16 allocated TG CPUs become unavailable after only 70 minutes rather than the planned 800 minutes, the bulk of the tasks are completed by EC2 nodes.

49 eSI Visitor Seminar – 01/13/10 Objective III: Response to Changing Operating Conditions (Resilience) (3/4) Number of TG cores and EC2 nodes as a function of time for usage mode III. Note that the TG CPU allocation goes to zero after about 70 minutes causing the autonomic scheduler to increase the EC2 nodes by 8.

50 eSI Visitor Seminar – 01/13/10 Objective III: Response to Changing Operating Conditions (Resilience) (4/4) Overheads of resilience on TTC and TCC.

51 eSI Visitor Seminar – 01/13/10 Autonomic Formulations/Programming

52 eSI Visitor Seminar – 01/13/10 LLC-based Self Management in 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 Computational Element LLC Controller Element Manager Internal State Contextual State Optimization Function LLC Controller Element Manager Advice Computational Element Model

53 eSI Visitor Seminar – 01/13/10 The Instrumented Oil Field Production of oil and gas can take advantage of installed sensors that will monitor the reservoirs state as fluids are extracted Knowledge of the reservoirs 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.

54 eSI Visitor Seminar – 01/13/10 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

55 eSI Visitor Seminar – 01/13/10 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 model Management decision START Dynamic Decision System Dynamic Data- Driven Assimilation Data assimilation Subsurface characterization Experimental design Autonomic Grid Middleware Grid Data Management Processing Middleware Autonomic Reservoir Management: Closing the Loop using Optimization

56 eSI Visitor Seminar – 01/13/10 An Autonomic Well Placement/Configuration Workflow AutoMate Programming System/Grid Middleware History/ Archive d Data Sensor/ Context Data Oil prices, Weather, etc.

57 eSI Visitor Seminar – 01/13/10 Autonomic Oil Well Placement/Configuration permeability Pressure contours 3 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

58 eSI Visitor Seminar – 01/13/10 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.

59 eSI Visitor Seminar – 01/13/10 Summary CI and emerging computational ecosystems –Unprecedented opportunity new thinking, practices in science and engineering –Unprecedented research challenges scale, complexity, heterogeneity, dynamism, reliability, uncertainty, … Autonomic Computing can address complexity and uncertainty –Separation + Integration + Automation Experiments with Autonomics for science and engineering –Autonomic data streaming and in-transit data manipulation, Autonomic Workflows, Autonomic Runtime Management, … However, there are implications –Added uncertainty –Correctness, predictability, repeatability –Validation

60 eSI Visitor Seminar – 01/13/10 Thank You!


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