1. Euro-Par, 2006 1 HASTE: An Adaptive Middleware for Supporting Time-Critical Event Handling in Distributed Environments ICAC 2008 Conference June 2 nd, 2008 Chicago, Illinois ICAC 2008 Qian Zhu and Gagan Agrawal Department of Computer Science and Engineering The Ohio State University

2. Euro-Par, 2006 Context – Cyber-Physical Systems (CPS) ICAC 2008

3. 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

4. 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

5. Euro-Par, 2006 5 Motivating Application: Real-time Volume Rendering (VR) ICAC 2008 Flexibility: image quality, image size… Time constraints

6. Euro-Par, 2006 6 Motivating Application: Great Lake Nowcasting and Forecasting (POM) ICAC 2008 Flexibility –Grid resolution –Internal time step –External time step Time Constraints

7. 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

8. Euro-Par, 2006 8 Outline ICAC 2008 Motivation and Introduction Related Work Proposed Approach Future Work Conclusion

9. 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

10. Euro-Par, 2006 10 Outline ICAC 2008 Motivation and Introduction Related Work Proposed Approach –HASTE Middleware –Autonomic Adaptation Algorithm –Resource Allocation Future Work Conclusion

11. 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

12. Euro-Par, 2006 12 Middleware Design ICAC 2008

13. 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

14. 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

15. 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 ICAC 2008

16. 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

17. 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%.

18. Euro-Par, 2006 18 Outline ICAC 2008 Motivation and Introduction Related Work Proposed Approach Future Work Conclusion

19. 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

20. 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

21. 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

22. Euro-Par, 2006 22 Outline ICAC 2008 Motivation and Introduction Related Work Proposed Approach Future Work Conclusion

23. 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

24. Euro-Par, 2006 24 Thank you! ICAC 2008