1. Euro-Par, 2006 1 A Resource Allocation Approach for Supporting Time-Critical Applications in Grid Environments Qian Zhu and Gagan Agrawal Department of Computer Science and Engineering The Ohio State University IPDPS 2009 IPDPS 2009 Conference May 28 th, 2009 Rome, Italy

2. Euro-Par, 2006 Context Ongoing research on supporting time-critical adaptive applications Fixed time, flexible computations –Maximize a QoS/Benefit function Previous work –Middleware design –Self-adaptation algorithm (ICAC 2008) 2

3. Euro-Par, 2006 3 Motivating Application: Real-time Volume Rendering (VR) Flexibility: image quality, image size… Time constraints IPDPS 2009

4. Euro-Par, 2006 4 Motivating Application: Great Lake Nowcasting and Forecasting (GLFS) Flexibility –Grid resolution –Internal time step –External time step Time Constraints IPDPS 2009

5. Euro-Par, 2006 5 Summary of Application Needs Time-Critical Event Handling –Intense computation and communication –Time and resource constraints –Application-specific flexibility –benefit function VR application GLFS application Grid Resources IPDPS 2009

6. Euro-Par, 2006 Overview of Our Research To Optimize the Benefit Function within the Time Constraint Parameter Adaptation –VR application: error tolerance, image size –GLFS application: internal/external time step Resource Allocation –Heterogeneous and dynamic resources IPDPS 2009

7. Euro-Par, 2006 7 Outline Motivation and Introduction Resource Allocation Approach –Approach Overview –Efficiency Value –Scheduling Algorithm Experimental Evaluation Related Work Conclusion IPDPS 2009

8. Euro-Par, 2006 Experimental Study: Real-time Volume Rendering The CPU/memory usage increases as ErrorTolerance value decreases or the ImageSize value increases. The change in the value of ErrorTolerance has a more significant impact, compared to the ImageSize parameter. IPDPS 2009

9. Euro-Par, 2006 Experimental Study: Great Lake Nowcasting and Forecasting The CPU usage changes as the values of ExternalTimeStep and InternalTimeStep vary. The memory usage remains roughly the same. IPDPS 2009

10. Euro-Par, 2006 Problem Description Heterogeneous and Dynamic Resources Different CPU, Memory, and/or Bandwidth Usage –Different service components –Different values of adjustable service parameters within the same service component Schedule the Service Components to Maximize the Benefit Function Within the Time Constraint IPDPS 2009

11. Euro-Par, 2006 11 Outline Motivation and Introduction Resource Allocation Approach –Approach Overview –Efficiency Value –Scheduling Algorithm Experimental Evaluation Related Work Conclusion IPDPS 2009

12. Euro-Par, 2006 12 Application Model CAC 2008 S1S1 S2S2 S3S3 S4S4 S5S5 S6S6 Each service component is deployed on a single node Multiple processing round Error tolerance Image size Wavelet coefficient Data Packet IPDPS 2009

13. Euro-Par, 2006 Resource Allocation Approach Overview Allocate Heterogeneous Resources to Services to Maximize the Benefit Within the Time Constraint –Unique characteristics of resource usage –Extra resource usage by varying the values of adaptive parameters Normal Execution Phase –Train rules for Efficiency Value estimation –Assign service priority Event Handling Phase –Apply the learned rules to infer Efficiency Value –Priority-based scheduling IPDPS 2009

14. Euro-Par, 2006 Efficiency Value To capture the suitability of executing the Service on the Processing Node Definition –Benefit contribution, where –Adaptation overhead, where –Node status Weighted sum of standard deviation of the workload and resource variance every 30 seconds IPDPS 2009

15. Euro-Par, 2006 Efficiency Value – Cont’d standard deviation of workload and resource variance how efficient is for supporting parameter adaptation of for overall benefit optimization Efficiency value estimation –Fuzzy logic rules Calculating Efficiency Value IPDPS 2009

16. Euro-Par, 2006 16 Efficiency Value -- Example IPDPS 2009 Figure: Example of Efficiency Value Calculation: (a) Computed Values (b) Normalized Benefit with Different Allocations Assigning to and to yields the maximum benefit Our definition of efficiency value captures the suitability of different nodes for different services

17. Euro-Par, 2006 Scheduling Algorithm Greedy Scheduling –Service priority based Benefit Optimization and Meeting the Time Deadline –Adjust and communication time ofcomputation time of IPDPS 2009

18. Euro-Par, 2006 18 Outline Motivation and Introduction Resource Allocation Approach –Approach Overview –Efficiency Value –Scheduling Algorithm Experimental Evaluation Related Work Conclusion IPDPS 2009

19. Euro-Par, 2006 Experiments Setup Algorithms Compared –GrADS (UCSD) –Optimal Metrics –Normalized benefit –Success-rate Simulated Grid Environments –HighReHetero, ModReHetero, and LowReHetero IPDPS 2009

20. Euro-Par, 2006 Experiment1: Effectiveness of Our Learning Approach MSE converges within 20mins, 35mins and 1hour for a 5-hour run IPDPS 2009

21. Euro-Par, 2006 Experiment2: Normalized Benefit Comparison (VolumeRendering) * Our algorithm achieves an average of 87% normalized benefit comparing to the Optimal and it is 32% higher than GrADS. IPDPS 2009 Figure 10: Normalized Benefit Comparison of Our Approach with GrADS and Optimal: Highly Heterogeneous Environment

22. Euro-Par, 2006 Experiment2: Success-Rate Comparison (VolumeRendering) * Our algorithm achieves 90% to 100% success-rate comparing to the Optimal. While GrADS can achieve 80% to 90%. IPDPS 2009 Figure 10: Success-Rate Comparison of Our Approach with GrADS and Optimal: Highly Heterogeneous Environment

23. Euro-Par, 2006 Experiment2: Overhead Comparison * The overhead caused by our algorithm is within 10% and 7% of that of the GrADS for VR and GLFS applications. IPDPS 2009 Figure 14: Resource Allocation Overhead Comparison of Our Approach with GrADS: (a) Volume Rendering Application (b) GLFS Application (a)(b)

24. Euro-Par, 2006 Experiment 3: Scalability An average slowdown of 9%, 7%, and 3%, respectively, in the three grid environments Scheduling 160 service components is 26.4 seconds Figure 15: Scalability of Different Resource Allocation Approaches IPDPS 2009

25. Euro-Par, 2006 25 Outline Motivation and Introduction Resource Allocation Approach –Approach Overview –Efficiency Value –Scheduling Algorithm Experimental Evaluation Related Work Conclusion IPDPS 2009

26. Euro-Par, 2006 26 Related Work Resource Allocation in Grid Computing – Iosup et al. (SC2007) – Xu et al. (ICAC2007) – Huang et al. (SC2007) – Tesauro et al. (ICAC2006) Real-Time Scheduling – Survey (Real Time Systems, 2004) – Q-RAM (RTSS1998) – Gopalan et al. (MMCN2002) IPDPS 2009

27. Euro-Par, 2006 27 Outline Motivation and Introduction Resource Allocation Approach –Approach Overview –Efficiency Value –Scheduling Algorithm Experimental Evaluation Related Work Conclusion IPDPS 2009

28. Euro-Par, 2006 28 Capture How Effectively of Processing a Service on a Node –Efficiency value estimation –Greedy scheduling Evaluate Our Resource Allocation Approach using Two Adaptive Applications –32% more benefit comparing to GrADS –Within 10% overhead comparing to GrADS –Our approach is scalable Conclusion IPDPS 2009

29. Euro-Par, 2006 29 Thank you! IPDPS 2009