euro-par, 2006 1 haste: an adaptive middleware for supporting time-critical event handling in...
DESCRIPTION
Euro-Par, 2006 Context – Cyber-Physical Systems (CPS) Physical Objects –Embedded computation, sensing and actuation – cyber capability Computing –Monitor and control physical process Cyber-Physical System (CPS) –Integrations of computation, human and physical processes ICAC 2008TRANSCRIPT
Euro-Par, 2006 1
HASTE: An Adaptive Middleware for Supporting Time-Critical Event Handling
in Distributed Environments
ICAC 2008 ConferenceJune 2nd, 2008 Chicago, Illinois
ICAC 2008
Qian Zhu and Gagan AgrawalDepartment of Computer Science and Engineering
The Ohio State University
Euro-Par, 2006
Context – Cyber-Physical Systems (CPS)
ICAC 2008
Euro-Par, 2006
Context – Cyber-Physical Systems (CPS)
• Physical Objects – Embedded computation, sensing and actuation
– cyber capability• Computing
– Monitor and control physical process• Cyber-Physical System (CPS)
– Integrations of computation, human and physical processes
ICAC 2008
Euro-Par, 2006
Motivation – Cyber-Physical Applications
• Medical Devices and Health Management Networks• Tele-physical Operations• Vehicular Networks and Smart Highways• Physical Infrastructure Monitoring and Control• Electricity Generation and Distribution• Robotic Manufacturing• Aviation and Airspace Management• Defense and Aerospace Systems• ……• Welcoming Remarks from NSF Workshop on Cyber-Physical Systems• (http://varma.ece.cmu.edu/cps/)
Dependable Secure Safe Efficient
Real-time Scalable
Cost-effective Adaptive Computational Intensive
ICAC 2008
Euro-Par, 2006 5
Motivating Application: Real-timeVolume Rendering (VR)
ICAC 2008
• Flexibility: image quality, image size…• Time constraints
Euro-Par, 2006 6
Motivating Application: Great Lake Nowcasting and Forecasting (POM)
ICAC 2008
Model
WeatherData
WaterQuality
20 km20
km
1 km
1 km
• Flexibility – Grid resolution– Internal time step– External time step
• Time Constraints
Euro-Par, 2006 7
Motivation – Cyber-Physical Applications (Cont’d)
ICAC 2008
• Time-Critical Event Handling– Intense computation and communication– Time and resource constraints– Application-specific flexibility– benefit function
• VR application
• POM application
• Grid Resources
angles_view_number
)error_image_penalty)blocks_data_all_oncontributi(max(
output_number
)elmod_per_tcos)output_per_reward(max(
Euro-Par, 2006 8
Outline
ICAC 2008
• Motivation and Introduction
• Related Work
• Proposed Approach
• Future Work
• Conclusion
Euro-Par, 2006
Related Work
• Middleware with Autonomic Properties– AutoMate (Rutgers Univ.)– QFabric (Georgia Tech. )– OceanStore (UC Berkeley)…
• Autonomic Adaptation– Lee et al. (CCNC06)– Wang et al. (ICAC06)
ICAC 2008
Euro-Par, 2006 10
Outline
ICAC 2008
• Motivation and Introduction
• Related Work
• Proposed Approach– HASTE Middleware– Autonomic Adaptation Algorithm– Resource Allocation
• Future Work
• Conclusion
Euro-Par, 2006 11
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
Euro-Par, 2006 12
Middleware Design
ICAC 2008
Application Layer
Service Layer
OGSA Infrastructure (Globus Toolkit 4.0)
Application Deployment Service
AUTONOMIC SERVICECOMPONENTS
App.Service 1
Agent/Controller
...
...App.
Service 3
Agent/Controller
App.Service 4
Agent/Controller
App.Service 5
Agent/ControllerApp.
Service 2
Agent/Controller
Application
Code ConfigurationFile
BenefitFunction
Time-CriticalEvent
Resource Allocation Service
Resource Monitoring Service
CPU Memory Bandwidth
SchedulingEfficiency
ValueEstimation
Autonomic Adaptation ServiceSystemModel Estimator
Euro-Par, 2006 13
Autonomic Adaptation Algorithm
ICAC 2008
• 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– relative workload– 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
Euro-Par, 2006
Resource Allocation
• Heterogeneous and Dynamic Resources • Different CPU, Memory, and/or Bandwidth
Usage – Different service components– Different values of adjustable service
parameters• Schedule the Service Components to
Maximize the Benefit Function Within the Time Constraint
ICAC 2008
Euro-Par, 2006
Proposed Solution
• Efficiency Value Definition– Service component and Processing node
• Adaptation overhead• Contribution to the overall benefit• Current status of node
• Efficiency Value Estimation• Greedy Scheduling Algorithm
j,iE
iS jN
jN
ICAC 2008
Euro-Par, 2006
Experimental Evaluation –Normalized Benefit Comparison
* Our algorithm achieves an average of 87% normalized benefit comparing to the Optimal and it is 32% higher than GrADS.
ICAC 2008
Euro-Par, 2006
Experimental Evaluation –Success-rate Comparison
ICAC 2008
* Our algorithm achieves 90% to 100% success-rate comparing to the Optimal. While GrADS can achieve 80% to 90%.
Euro-Par, 2006 18
Outline
ICAC 2008
• Motivation and Introduction
• Related Work
• Proposed Approach
• Future Work
• Conclusion
Euro-Par, 2006
Future Work: Scheduling Parallel Service Components
• Scheduling Parallel Tasks– Task parallelism– Data parallelism
• Heterogeneous and Dynamic Resources• Proposed Approach
– Resource clusters– Function of execution time
• Number of assigned processing nodes• Heterogeneity of assigned processing nodes
ICAC 2008
Euro-Par, 2006
Future Work: Reliable Service-Oriented Computing
• Unreliable Grid Resources– Dynamic workload– Resources leave and join unexpectedly
• Faults– Hardware and Software
• Proposed Solution– Generate reliable resource configuration– Invariant extraction– Self-diagnosis and self-healing
ICAC 2008
Euro-Par, 2006
Future Work: Context-aware Adaptation in Mobile Grids
• Mobile Grid System– Powerful computation capability of grid computing– Ubiquitous accessibility of mobile computing
• Optimize the Benefit Function Within the Time Interval• Proposed Solution
– Context-ware adaptation– Workload prediction– Multi-level adaptation
ICAC 2008
Euro-Par, 2006 22
Outline
ICAC 2008
• Motivation and Introduction
• Related Work
• Proposed Approach
• Future Work
• Conclusion
Euro-Par, 2006
Conclusion
• Proposed Work– An Adaptive Middleware – Autonomic Adaptation Algorithm– Grid-based Scheduling Approach
• Future work– Scheduling Parallel Service Components – Reliability in Service Computing– Context-aware Adpatation
ICAC 2008
Euro-Par, 2006 24
Thank you!
ICAC 2008