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Published byGiulia Aleixo Lancastre Modified over 6 years ago
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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 ICAC 2008
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Motivating Application: Real-time Volume Rendering (VR)
Flexibility: image quality, image size… Time constraints ICAC 2008
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Motivating Application: Great Lake Nowcasting and Forecasting (POM)
Flexibility Grid resolution Internal time step External time step Time Constraints ICAC 2008
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Motivating Applications
Time-Critical Event Handling Intense computation and communication Time and resource constraints Application-specific flexibility benefit function VR application POM application ICAC 2008
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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
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Outline Motivation and Introduction Overall Middleware Design
The Autonomic Adaptation Algorithm Experimental Evaluation Related Work Conclusion ICAC 2008
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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 ICAC 2008
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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 ICAC 2008
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Middleware Design ICAC 2008
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AutoServiceWrapper Design
T: The relationship between adaptable service parameter and execution time B: The relationship between adaptable service parameter and the benefit function ICAC 2008
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Outline Motivation and Introduction Overall Middleware Design
The Autonomic Adaptation Algorithm Experimental Evaluation Related Work Conclusion ICAC 2008 11 11
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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 ICAC 2008
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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 ICAC 2008
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System Model Constraints State Equation Performance Measure
time constraint adaptation overhead benefit Constraints ICAC 2008
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Policy Without Learning
It is simple and straightforward Parameter convergence depends on the learning rate It may incur a large adaptation overhead ICAC 2008
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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 ICAC 2008
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Outline Motivation and Introduction Overall Middleware Design
The Autonomic Adaptation Algorithm Experimental Evaluation Related Work Conclusion ICAC 2008 17 17
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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. ICAC 2008
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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 ICAC 2008
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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 ICAC 2008
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Error Tolerance ICAC 2008 21 21
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Image Size ICAC 2008 22 22
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Wavelet Coefficient ICAC 2008 23 23
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Overhead of the Adaptation Algorithm
12% 11% 9% ICAC 2008 24 24
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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%. ICAC 2008 25 25
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Outline Motivation and Introduction Overall Middleware Design
The Autonomic Adaptation Algorithm Experimental Evaluation Related Work Conclusion ICAC 2008 26 26
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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) ICAC 2008
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Outline Motivation and Introduction Overall Middleware Design
The Autonomic Adaptation Algorithm Experimental Evaluation Related Work Conclusion ICAC 2008 28 28
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Conclusion An Adaptive Middleware Supporting Time-Critical Event Response An Autonomic Adaptation Algorithm Fast Convergence of Adaptable Parameters Modest Overhead Associated with Adaptation Algorithm ICAC 2008
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Thank you! ICAC 2008
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