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Center for Autonomic Computing

Enabling End-to-end Data-driven Sensor-based Scientific and Engineering Applications

DDDAS at ICCS 2009 Rutgers University, Center for Autonomic Computing

Nanyan Jiang and Manish Parashar May 25, 2009

Acknowledge: University of Texas at Austin (M. Wheeler, H. Klie, et al.), NSF, DoE.

Motivations   Enable the integration of physical and

computational worlds for management, optimization and control   Integrate simulations and data with (near) real time

sensors and actuators   Data acquisition, assimilation and coupling with

computational models for end-to-end decision processes.

Kriging (Err<0.01)

Sensor data Simulation grid

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Observation data Scientific Application/ Computational Model

Simulation model

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Application Scenarios: Examples   Oil reservoir management and optimization

  Processes   Detect and track changes   Invert data to reservoir properties   Assimilate data and reservoir properties, …

  Feedback loop between measured data and computational models to enable optimal oil management and control

  Contaminant modeling and control   Ruby Gulch Waste Repository

  Understanding the evolution of sites   Impact on drinking water supplies   Autonomous monitoring:

  Temperature, moisture, chemical sensors, etc.   Extract application interested information from complex coupled

transport models which utilized data gathered by field sensors.

  Structural health monitoring   Temperature, pressure sensors, etc.   Using models and real time data to predict the status of the

bridge

  Challenges   Data volumes and rate   Constrained heterogeneous sensor systems   Dynamics and uncertainty of applications, systems, and data

Key Requirements   Application level requirements

  Multi-scale, multi-resolution data access   Spatial temporal variation, e.g. online spatial and temporal interpolation

  Data quality and uncertainty management

  System level requirement   Adaptive runtime management of in-network processing   Resource management and computation/communication/energy

tradeoffs

  Programming requirements   Simple, extensible end-to-end programming models and

interfaces

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Objectives   Provide programming abstractions and systems

software support for sensor-driven applications   Programming abstractions and runtime systems for

integrating sensor systems with applications processes in an end-to-end dynamic sensor-driven application.

  Efficient in-network computation/communication mechanisms for resource constrained heterogeneous sensor networks.   Support for tradeoff between data quality, resource

consumptions and performance.

Our Approach   Support end-to-end sensor-driven applications and

the interactions between computational models and the sensor system.   The GridMap/iZone programming system provides

semantically meaningful abstractions and runtime mechanisms for integrating sensor systems with computational models for scientific processes

  Abstractions and in-network mechanisms to support different sensor selection and interpolation approaches, e.g., aggregation, adaptive interpolation and assimilations.

  Use virtualization to address the mismatch between the instrumentation of the physical domain and the presentation used with computational models.

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Contributions

  The GridMap/iZone Programming System   Provides programming abstractions for integrating

sensor systems with computational models for scientific processes (e.g. biophysical, geophysical processes) and with other application components in an end-to-end experiment.

  Provides programming abstractions and system software support for developing in-network data processing mechanisms.

  Enabling End-to-end Sensor-driven Scientific and Engineering Applications   End-to-end oil reservoir

Overview of Programming System

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Interpolation/ Regression/ Modeling

Scientific Application/ Computational Model

Content Overlay Content-based routing engine

Self-organized overlay

X

System data

CyberInfrastructure

Location aware Content-based Middleware

Discovery, Associative Rendezvous Messaging

Clustering/Geo-routing Wireless overlay, mesh, etc.

CyberPhysical Applications

In-network algorithms IDW, Kriging, regression, etc.

In-network Data Processing Space, Time and Resource aware Opt Dissemination, aggregation, collaboration

Observation data

Programming Abstraction

iZone

GridMap Programming Abstraction

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Outline

  Motivation   The GridMap/iZone Programming System

  Programming abstractions and support   Enabling end-to-end data-driven sensor-based

applications   An end-to-end oil reservoir application

  Conclusion

Content Overlay

GridMap Programming Abstraction

Location aware Content-based Middleware

Clustering/Geo-routing

CyberPhysical Applications

In-network algorithms

Programming Abstraction iZone

In-network Data Processing Space, Time and Resource aware Opt

GridMap Programming Abstraction   A virtualization of the physical sensor grid to match

the representation of the physical domain used by the application   Sensor data can be simply and seamlessly integrated into

computational models   Changes in the underlying sensor network is transparent to

the applications   In-network spatial-temporal data processing can help remove

computational bias   Flexible and powerful operators for querying and processing

sensor data   simple query interface using content and supporting wildcards,

ranges, etc.

Kriging (Err<0.01)

Sensor data Simulation grid

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iZone Programming Abstraction   Abstraction and operators to implement in-network

mechanisms to map raw sensor data to application representations   Hide details/irregularities of measurement infrastructures and

sensed data using user-defined functions (e.g. regression, interpolation)

  Provide a consistent representation of sensor data in time and space

  Defining iZone based on interpolation/regression/model   Can be specified using a reference point (or line, polygon),

ranges, and/or number of readings

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Assume a two-level tiered sensor network with sensor clusters and cluster heads �

In-network Programming Primitives

iZone operators

Applications

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Programming primitives

  GridMap representation   E.g. <118:8:223, 23:10:333>,

pressure,   GridMap operators

  E.g. query, retrieve, notify, delete, init, refine, coarsen, etc.

  Interpolation functions   Kriging, IDW, user-defined, …

  iZone operators   E.g. Get/put, aggregate (i.e. sum,

weighted sum, …)

iZone operators

GridMap operators

Applications

Oil Reservoir Management and Optimization

Instrumented oil reservoir (2) request application

specified sensor data Data

Archive

(3a) Retrieve real-time interpolated pressures

Sensor data Simulation grid

(3b) Retrieve production rates

(3c) Retrieve historical information

(4) Optimize oil reservoir production with

simulation process

Pressure models

(7) Update data archive

Three oil well production distribution

(5) Adjust gas ingestion places/pressures

Before After adjustment

Water/gas ingestion placements

(6) Production rates’ update

Before After adjustment

(1) Start simulation processes

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Using GridMap/iZone programming primitives

Deployed sensor network in the oil field

An end-to-end simulation process

(a) init

(c) retrieve

Update production policy

(b) query

Experimental Evaluation   Objectives

  Demonstrate using GridMap/iZone to support the integration of computational processes with real-time in-network processing of sensor information

  Proof-of-concept performance evaluations: impact of accuracy and communication costs.

  Experiment setup   About 800 sensors distributed in a 800X2000 feet^2 area   About 40 clusters are initialized

Deployed sensor network

In-network computation

An end-to-end simulation process

1 2

3

4 5 gateway

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Impact on communication costs   Given accuracy requirements, what are the communication

costs of querying the GridMap of (different) multiple grid points.   For a given number of grid points, increasing the required accuracy

increases the volume of communication.   he number of grid points on the GridMap increases less proportionally

than the increasing of the volume of communication.�

Conclusions   The GridMap/iZone Programming System

  Provide programming abstractions for integrating sensor systems with computational models for scientific processes (e.g. biophysical, geophysical processes) and with other application components in an end-to-end experiment.

  Provide programming abstractions and system software support for developing in-network data processing mechanisms.

  Enabling End-to-end Sensor-driven Scientific and Engineering Applications:

  End-to-end oil reservoir

  Links   http://nsfcac.rutgers.edu/   http://nsfcac.rutgers.edu/doc/html/Programming_Sensor-

driven_Autonomic_Applications/   http://www.caip.rutgers.edu/~nanyanj/GridMap.html

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Related Publication 1.  Enabling End-to-end Data-driven Sensor-based Scientific and Engineering Applications,

Proceeding of the Workshop on Dyanmic Data-Driven Application Systems (DDDAS '09), in conjunction with the international conference on Computational Science (ICCS 2009), Pringer Verlag, Baton Rouge, Louisiana, May, 2009.

2.  Enabling Autonomic Power-Aware Management of Instrumented Data Centers. the Fifth Workshop on High-Performance, Power-Aware Computing (HPPAC 2009), in conjunction with the 23rd Annual International Parallel & Distributed Processing Symposium (IPDPS 2009), Rome, Italy. Accepted, 2009

3.  In-network Data Estimation Mechanisms for Sensor-driven Scientific Applications, 15th IEEE International Conference on High Performance Computing (HiPC 2008), Bangalore, India, December, 2008.

4.  Programming Sensor-based, Dynamic Data-driven Scientific Applications, IEEE International Parallel & Distributed Processing Symposium (IPDPS) Ph.D Forum, Miami, FL, April, 2008

5.  Programming Support for Sensor-based Scientific Applications, The NSF Next Generation Software (NGS) Workshop held in conjunction with IPDPS, Miami, FL, April, 2008

6.  Meteor: A Middleware Infrastructure for Content-based Decoupled Interactions in Pervasive Grid Environments, Concurrency and Computation: Practice and Experience, John Wiley and Sons, 2007.

7.  A Decentralized Content-based Aggregation Service for Pervasive Environments, International Conference of Pervasive Services (ICPS), June, 2006.

8.  Enabling Applications in sensor-based Pervasive Environments, (BaseNets 2004), San Jose, CA, USA October 25, 2004

Questions?   Acknowledgement

  The research presented in this paper is supported in part by National Science Foundation via grants numbers CNS 0723594, IIP 0758566, IIP 0733988, CNS 0305495, CNS 0426354, IIS 0430826 and ANI 0335244, and by Department of Energy via the grant number DE-FG02-06ER54857, and was conducted as part of the NSF Center for Autonomic Computing at Rutgers University.

Thank you

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Backup Slides

Autonomic Instrumented Data Center Management   Large scale and dense sensor networks, e.g. temperature,

humidity, etc.   Data center power efficiency and job allocation management

  Integrate snapshots of physical phenomenon monitored by sensor networks with computational models (e.g., heat distribution or online learning, power consumption models, etc.) to improve energy efficiency.

Simulation Model: optimize power consumption and job processing throughput

Sensor data Simulation grid

Wide-area network

Data Archive

Job requests

Power models

Job allocation policy

Job requests

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Instrumented DataCenter

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Programming Instrumented Data Center

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Instrumented DataCenter

(2) Request application specified sensor data Data

Archive (3a) Retrieve real-time interpolated data

Job requests

(3b) Retrieve current load distribution

(3c) Retrieve historical information

(4) Simulation processes: e.g., optimize power usage and job

throughput

(5a) update temperature control policy, e.g., cooling configuration

(5b) Update job allocation policy

Job requests (1) Job requests

Power models

(7) Update data archive

Job migration

(6) In-network Analysis, e.g., hotspots

Sensor data Simulation grid

Communication Costs

  As the area of the GridMap increases, so does the average number of messages required per update.