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Addressing Complexity in Emerging Cyber-Ecosystems –
Exploring the Role of Autonomics in E-Science
Manish ParasharCenter for Autonomic Computing
The Applied Software Systems LaboratoryRutgers, The State University of New Jersey
&Office of CyberinfrastructureNational Science Foundation
5th EGEE User Forum – 04/13/10
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
Cyberinfrastructure => Cyber-Ecosystems
21st Century Science and Engineering: New Paradigms & Practices
• Transformed by CI• End-to-end – seamless access, aggregation, interactions• Fundamentally collaborative & data-driven/data intensive
• Unprecedented opportunities• New requirements, challenges • New thinking in/approaches to computation science
• How can it benefit current applications?• How can it enable new thinking in science?
5th EGEE User Forum – 04/13/10The Instrumented Oil Field (with UT-CSM, UT-IG, OSU, UMD, ANL)
Detect and track changes in data during production.Invert data for reservoir properties.Detect and track reservoir changes.
Assimilate data & reservoir properties into the evolving reservoir model.
Use simulation and optimization to guide future production.
Data Driven
ModelDriven
5th EGEE User Forum – 04/13/10
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, www.dddas.org Source: M. Rotea, NSF
5th EGEE User Forum – 04/13/10
The Challenge: Managing Complexity, Uncertainty (I)
• Increasing application, data/information, system complexity– Scale, heterogeneity, dynamism, unreliability, …, disruptive
trends, …
• 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, …
5th EGEE User Forum – 04/13/10
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…)
5th EGEE User Forum – 04/13/10
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
5th EGEE User Forum – 04/13/10
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. 247-259, 2005.
5th EGEE User Forum – 04/13/10Motivations for Autonomic Computing
Source: http:idc 2006
8/12/07: 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.pdf
Key ChallengeCurrent 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.
5th EGEE User Forum – 04/13/10
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 0-8493-9367-1, 2007.
5th EGEE User Forum – 04/13/10
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. 2006.)
5th EGEE User Forum – 04/13/10
Autonomics for Science and Engineering ?
• Manage application/information/system complexity• not just hide it!
• Enabling new thinking, formulations• how do I think about/formalize my
problem differently?
5th EGEE User Forum – 04/13/10
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
5th EGEE User Forum – 04/13/10
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
5th EGEE User Forum – 04/13/10
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– …
5th EGEE User Forum – 04/13/10
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
5th EGEE User Forum – 04/13/10
Autonomics for Science and Engineering – Application-level Examples
5th EGEE User Forum – 04/13/10
Coupled Fusion Simulations: A Data Intensive Workflow
5th EGEE User Forum – 04/13/10Autonomic 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.
5th EGEE User Forum – 04/13/10
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 nodeSimulation
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
5th EGEE User Forum – 04/13/10
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
ADSSArchSDAS
DAS
CBMS DTSDAS
SS
ORNL
ADSS
Data In-Transit
Data Consumers
SLAMS
DTS
PS
PPPL
FFT
DAS DAS
Rutgers University
VisSDASBMS
SLAMS
BudjS
SLAMS
Sink
SLAMS
FFT
• Simulations executes on leadership class machines at ORNL and NERSC
• In-transit nodes located at PPPL and Rutgers
5th EGEE User Forum – 04/13/10
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 19th controller interval.
Controller Interval0 2 4 6 8 10 12 14 16 18 20 22 24
Dat
a T
ran
sfer
rred
by
DT
S(M
B)
0
20
40
60
80
100
120
140
Ban
dw
idth
(M
b/s
ec)
0
20
40
60
80
100
120
DTS to WANDTS to LANBandwidthCongestion
5th EGEE User Forum – 04/13/10
Exploring Hybrid HPC-Grid/Cloud Usage Modes [eScience’09]
• Production computation infrastructures will be (are) hybrid integrating HPC Grids and Clouds
• What are appropriate usage modes for hybrid infrastructure?– Acceleration
• Clouds can be used as accelerators to improve the application time to completion
– To alleviate the impact of queue wait times– “Strategically Off load” appropriate tasks to Cloud resources– All whilst respecting budget constraints.
– Conservation• Clouds can be used to conserve HPC Grid allocations, given
appropriate runtime and budget constraints. – Resilience
• Clouds can be used to handle:– General: Response to dynamic execution environments– Specific: Unanticipated HPC Grid downtime, inadequate allocations
or unexpected Queue delays/QoS change
5th EGEE User Forum – 04/13/10
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
5th EGEE User Forum – 04/13/10
Exploring Hybrid HPC-Grid/Cloud Usage Modes using CometCloud
EnKF application
CometCloud
Cloud
GridAgent
Pull TasksPull Tasks
Push Tasks
HPC Grid
Mgmt. Info. Mgmt. Info.
HPC GridCloudCloud
CloudAgent
Workflowmanager
Runtimeestimator
Autonomicscheduler
Monitor
Analysis
Adaptation
AdaptivityManager
Applicationadaptivity
Infrastructureadaptivity
5th EGEE User Forum – 04/13/10
Objective I: Using Clouds as Acceleratorsfor 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
5th EGEE User Forum – 04/13/10
Objective II: Using Clouds for ConservingCPU-Time on the TeraGrid• Explore how to conserve fixed allocation of CPU hours
by offloading tasks that perhaps don’t 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.
5th EGEE User Forum – 04/13/10
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.
5th EGEE User Forum – 04/13/10
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.
5th EGEE User Forum – 04/13/10
The Instrumented Oil Field• 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 productionInvert data for reservoir propertiesDetect and track reservoir changes
Assimilate data & reservoir properties into the evolving reservoir model
Use simulation and optimization to guide future production, future data acquisition strategy
“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.
5th EGEE User Forum – 04/13/10
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
5th EGEE User Forum – 04/13/10
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
Man
agem
ent
dec
isio
n
START
Dynamic Decision Dynamic Decision SystemSystem
Dynamic Data-Dynamic Data-Driven Assimilation Driven Assimilation
Data assimilation
Subsurface characterization
Experimental design
Autonomic Autonomic Grid Grid MiddlewareMiddleware
Grid Data ManagementGrid Data ManagementProcessing MiddlewareProcessing Middleware
Autonomic Reservoir Management: “Closing the Loop” using Optimization
5th EGEE User Forum – 04/13/10
Autonomic Formulations/Programming
Element Manager
Functional Port
Autonomic Element
Control Port
Operational Port
ComputationalElement
Element Manager
Functional Port
Autonomic Element
Control Port
Operational Port
ComputationalElement
Element Manager
Event generation
Actuatorinvocation
OtherInterface
invocation
Internalstate
Contextualstate
Rules
Element Manager
Event generation
Actuatorinvocation
OtherInterface
invocation
Internalstate
Contextualstate
Rules
Application workflow
Composition manager
Application strategiesApplication requirements
Interaction rules
Interaction rules
Interaction rules
Interaction rules
Behavior rules
Behavior rules
Behavior rules
Behavior rules
Application workflow
Composition manager
Application strategiesApplication requirements
Interaction rules
Interaction rules
Interaction rules
Interaction rules
Behavior rules
Behavior rules
Behavior rules
Behavior rules
5th EGEE User Forum – 04/13/10
An Autonomic Well Placement/Configuration Workflow
If guess not in DBinstantiate IPARS
with guess asparameter
Send guesses
MySQLDatabase
If guess in DB:send response to Clientsand get new guess fromOptimizer
OptimizationService
IPARSFactory
SPSA
VFSA
ExhaustiveSearch
DISCOVERclient
client
Generate Guesses Send GuessesStart Parallel
IPARS InstancesInstance connects to
DISCOVER
DISCOVERNotifies ClientsClients interact
with IPARS
AutoMate Programming System/Grid Middleware
History/ Archived Data
Sensor/ContextData
Oil prices, Weather, etc.
5th EGEE User Forum – 04/13/10
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.
5th EGEE User Forum – 04/13/10
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– New formulations necessary….