1. An Adaptive Middleware for Supporting Time-Critical Event Response
Qian Zhu and Gagan Agrawal
Department of Computer Science and Engineering
The Ohio State University
ICAC 2008 Conference
June 4th, Chicago, Illinois
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2. Motivating Application: Real-time Volume Rendering (VR)
Flexibility: image quality, image size…
Time constraints
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3. Motivating Application: Great Lake Nowcasting and Forecasting (POM)
Flexibility
Grid resolution
Internal time step
External time step
Time Constraints
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4. Motivating Applications
Time-Critical Event Handling
Intense computation and communication
Time and resource constraints
Application-specific flexibility
benefit function
VR application
POM application
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5. Need for Autonomic Middleware
To Optimize the Benefit Function Within the Time Constraint
Numerous Performance-Related Parameters
Complex and Dynamic in Behaviors
Self-managing
Self-optimizing

6. Outline Motivation and Introduction Overall Middleware Design
The Autonomic Adaptation Algorithm
Experimental Evaluation
Related Work
Conclusion
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7. Middleware Design Goals
To Enable the Time-critical Event Handling to Achieve the Maximum Benefit, While Satisfying the Time Constraint
To be Compatible with Grid and Web Services
To Enable Easy Deployment and Management with Minimum Human Intervention
To be Used in a Heterogeneous Distributed Environment
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8. Application Model – Real-time Volume Rendering
Data Packet
Error tolerance
Image size
Wavelet coefficient S1 S2 S3 S4 S5 S6
Each service component is deployed on a single node
Multiple processing round
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9. Middleware Design
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10. AutoServiceWrapper Design
T: The relationship between adaptable service parameter and execution time
B: The relationship between adaptable service parameter and the benefit function
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11. Outline Motivation and Introduction Overall Middleware Design
The Autonomic Adaptation Algorithm
Experimental Evaluation
Related Work
Conclusion
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12. Autonomic Adaptation Algorithm
To Optimize the Benefit Function Within the Time Constraints by Adapting Service Parameters
In the Normal Processing Phase
Multiple processing rounds
For each checkpoint of parameter X in service S
Learn the Estimators of the value of X with
execution time
benefit function
Update the system model
In the Time Critical Event Handling Phase
Adjust X based on the system model
Accelerate the adaptation if violating the time deadline
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13. Parameter Adaptation to Optimal Control Model
System Model Definitions
Variable
Description
x(k)
Adjustable service parameters
u(k)
Increase/Decrease to parameters
w(k)
Estimated overall response time
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14. System Model Constraints State Equation Performance Measure
time constraint
adaptation
overhead
benefit
Constraints
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15. Policy Without Learning
It is simple and straightforward
Parameter convergence depends on the learning rate
It may incur a large adaptation overhead
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16. Policy with Learning Reinforcement Learning Based
Normal Processing Phase – Explore
Q-learning
Discrete and continuous parameters
Global Pattern
Correlation between adaptable service parameters
if x is continuous
otherwise
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17. Outline Motivation and Introduction Overall Middleware Design
The Autonomic Adaptation Algorithm
Experimental Evaluation
Related Work
Conclusion
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18. Experimental Evaluation Goals
Demonstrate that the service parameters converge quickly while meeting the time constraint.
Demonstrate that the overhead of adaptation is modest.
Demonstrate the overhead caused by learning is very small.
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19. Application Volume Rendering Dataset 7.5GB(block size = 32*32*16=64KB)
30 time steps
Service Parameters
Error tolerance, image size
Wavelet coefficients
Benefit Function
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20. Task Time constraint: 20 minutes Initial view angles: 45, 90, 135
Image size: 256*256,
error tolerance < 0.03
Result
Actual view angles: 45, 75, 90, 135
Error tolerance = 0.02
Image size = 256*256
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21. Error Tolerance
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22. Image Size
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23. Wavelet Coefficient
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24. Overhead of the Adaptation Algorithm
12%
11% 9%
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25. Overhead of the Adaptation Algorithm (Learning Phase)
Normal Execution
(Min)
Number of Adapted Parameters (Min) 48 1 2 3
49.06
49.52
51.48
The overhead of the adaptation algorithm for tuning 1,2 and 3 parameters is 2.2%, 3.0% and 4.8%.
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26. Outline Motivation and Introduction Overall Middleware Design
The Autonomic Adaptation Algorithm
Experimental Evaluation
Related Work
Conclusion
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27. Related Work Middleware with Autonomic Properties Autonomic Adaptation
AutoMate, QFabric, OceanStore
Autonomic Adaptation
Lee et al. (CCNC06)
Wang et al. (ICAC06)
Reinforcement Learning
Tesaura et al. (ICAC06)
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28. Outline Motivation and Introduction Overall Middleware Design
The Autonomic Adaptation Algorithm
Experimental Evaluation
Related Work
Conclusion
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29. Conclusion
An Adaptive Middleware Supporting Time-Critical Event Response
An Autonomic Adaptation Algorithm
Fast Convergence of Adaptable Parameters
Modest Overhead Associated with Adaptation Algorithm
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30. Thank you!
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