Real-Time Capacity of Networked Data Fusion University of Illinois at Urbana-Champaign

1. Real-Time Capacity of Networked Data FusionUniversity of Illinois at Urbana-Champaign • Forrest Iandola (University of Illinois) • Fatemeh Saremi (University of Illinois) • Tarek Abdelzaher (University of Illinois) • Praveen Jayachandran (IBM Research) • Aylin Yener (Pennsylvania State University)

2. Motivation and Goals • Develop a theoretical bound for the capacity of data fusion systems • Enable data fusion systems to run at full capacity without missing deadlines Forrest Iandola Illustration of a data fusion system with merging

3. Outline • Introduce data fusion system model • Scheduling theory background: Feasible Region Calculus • Derive a capacity utilization bound for data fusion pipelines • Extend this bound to capture merging pipelines • Performance evaluation Forrest Iandola

4. Data Fusion System Model (1/3) • “Data Fusion System” refers to… • Distributed sensor networks • Control systems that receive one or more data feeds • “Real-Time Capacity” = data packets transmitted within time constraints Forrest Iandola

5. Data Fusion System Model (2/3) • Workflow i is denoted as Fi • Invocation of Fi is a “job” q • Di = deadline of Fi • Pi = period of Fi • Ri = 1/Pi = “Rate” • Ci,j = computation of Fi on stage j Forrest Iandola

6. Data Fusion System Model (3/3) • System constraints reflect a realistic data fusion system • Non-preemptive earliest deadline first (EDF) scheduling • Workflows are periodic. • Di >> Pi (in other words, many invocations of Fi may be active simultaneously.) Forrest Iandola

7. Scheduling Theory Background: Feasible Region Calculus (FRC) • A pipeline task set can be reduced to a uniprocessor equivalent: • Assume qN is the lowest-priority workflow Forrest Iandola

8. Scheduling Theory Background: Feasible Region Calculus (FRC) • For simplicity, let us refer to the “modified” equivalent of the lowest-priority task as q Forrest Iandola

9. Deriving Capacity Bound from FRC • Testing schedulability of equivalent uniprocessor from as defined by FRC • Remember: we assume non-preemptive EDF scheduling Forrest Iandola

10. Deriving Capacity Bound from FRC • Testing schedulability of equivalent uniprocessor from as defined by FRC • Remember: we assume non-preemptive EDF scheduling Basic utilization formula: Combining utilization formula with FRC definitions: To avoid deadline misses, utilization must be less than 1. Forrest Iandola

11. Simplifying the Capacity Bound to Reduce Computation Overhead • Stage-additive component is very small when Di >> Pi • Can approximate the utilization even if we ignore stage-additive component Replace ceiling function with (DiRi+1): Reduce computation time by dropping lowest-priority invocation: Forrest Iandola

12. Handling Merging Flows Forrest Iandola

13. Handling Merging Flows Forrest Iandola

14. Handling Merging Flows • Let’s discuss the intuition behind this. • Step 1: Reduce child pipelines to equivalent uniprocessor workflow sets • Step 2: Obtain two-stage pipeline • Ignore all but the largest equivalent pipeline per workflow • Step 3: Calculate equivalent uniprocessor for two-stage pipeline Forrest Iandola

15. Fundamental Results Forrest Iandola

16. Evaluation of Capacity Bound • Comparing predicted useful work of a data fusion tree to actual useful work (just before onset of deadline misses) • Note: Utilization due to jobs/flows that miss deadlines is not counted as useful work. • Observations: • Capacity bound accurately predicts ability to do useful work Forrest Iandola

17. Evaluation of Overload Behavior • Comparing overload behavior of a data fusion tree with admission control (based on new capacity result) to one without • Note: Utilization due to jobs/flows that miss deadlines is not counted as useful work. • Observations: • Capacity bound accurately predicts ability to do useful work • At high load, significant degradation is observed in the absence of admission control due to excessive deadline misses Forrest Iandola

18. Conclusions • Derived a capacity utilization bound for data fusion systems • Simplified the bound into an easy-to-use approximation • Extended this result for merging workflows • Evaluation demonstrates accuracy of bound Forrest Iandola