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, 2008 Chicago, Illinois ICAC 2008

Motivating Application: Real-time Volume Rendering (VR) Flexibility: image quality, image size… Time constraints ICAC 2008

Motivating Application: Great Lake Nowcasting and Forecasting (POM) Flexibility Grid resolution Internal time step External time step Time Constraints ICAC 2008

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

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

Outline Motivation and Introduction Overall Middleware Design The Autonomic Adaptation Algorithm Experimental Evaluation Related Work Conclusion ICAC 2008

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

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

Middleware Design ICAC 2008

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

Outline Motivation and Introduction Overall Middleware Design The Autonomic Adaptation Algorithm Experimental Evaluation Related Work Conclusion ICAC 2008 11 11

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

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

System Model Constraints State Equation Performance Measure time constraint adaptation overhead benefit Constraints ICAC 2008

Policy Without Learning It is simple and straightforward Parameter convergence depends on the learning rate It may incur a large adaptation overhead ICAC 2008

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

Outline Motivation and Introduction Overall Middleware Design The Autonomic Adaptation Algorithm Experimental Evaluation Related Work Conclusion ICAC 2008 17 17

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

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

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

Error Tolerance ICAC 2008 21 21

Image Size ICAC 2008 22 22

Wavelet Coefficient ICAC 2008 23 23

Overhead of the Adaptation Algorithm 12% 11% 9% ICAC 2008 24 24

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

Outline Motivation and Introduction Overall Middleware Design The Autonomic Adaptation Algorithm Experimental Evaluation Related Work Conclusion ICAC 2008 26 26

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

Outline Motivation and Introduction Overall Middleware Design The Autonomic Adaptation Algorithm Experimental Evaluation Related Work Conclusion ICAC 2008 28 28

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

Thank you! ICAC 2008