1. Analysis of Real-Time Multi-Modal FP-Scheduled Systems with Non-Preemptible Regions Authors: Masud Ahmed (presenting) Pradeep Hettiarachchi Nathan Fisher Department of Computer Science Wayne State University. This research was supported by NSF, Wayne State University, and MathWorks Inc

2. 2 Outline  Introduction:  Adaptive Cruise Control (ACC) Systems  Multi-Modal System (MMS)  Models:  Sporadic Tasks  Periodic Resources  Contributions:  Protocol for a Mode-Change  Determination of FP Schedulability  Non-preemptive execution  Usability  Future Work

3. 3 Motivation: Real-Time ACC  Automotive ACC Systems  Alerts driver if front vehicle is too close  Use 77GHz Radar Transmission/Receiving  Design constraints  Non-preemptive radar sweep  Max Sweep Time  Number of sweep 7 µ s and 2 µ s for 200m and 50m respectively Higher number implies better accuracy Radar Sweep

4. 4 Multi-Mode ACC  We consider Multi-Mode System for ACC  Software mode Exploit smaller sweep-time Use higher number of sweep Utilize low priority task to reclaim idle cycles Tasks (with non-preemptive region) scheduled by FP  Hardware mode Enable shared platform FP-Schedulability analysis of a MMS is computationally expensive. No MMS support for non-preemption

5. 5 Related Work Santinelli et al. (2011), Stoimenov et al. (2009), and Phan et al. (2009, 2010). Fu et al. (2010a, 2010b), Timmons and Scanlon (2009), and Kim (2007). Tindell et al. (1996), Pedro and Burns (1998), and Real and Crespo (2004). Multi-Modal Systems Control Systems Dedicated Platform High Computation Time Soft real-time systems.

6. 6 Models: Sporadic Task Sporadic Task Execution Period Deadline DBF t Execution RBF

7. 7 Models: Periodic Resources Capacity Period-of-repetition Supply Bound Function (SBF) t Supply

8. 8 Mode Definition M (i) Hardware Software MMS Protocol for Mode-Change

9. 9 Mode Change tktk M (i) M (j) M (k) mcr k =(M (j), M (k),t k ) Mode Change Request Mode Change Request Mode Change Request Mode Change Request Transition time Transition time Transition time Transition time Old Mode New Mode Old Mode mcr k-1 =(M (i), M (j),t k-1 ) t k-1 MMS Protocol for Mode-Change

10. 10 Different Tasks t k-1 + tktk M (j) M (k) Immediately Aborted Tasks A (ij) Non-Aborted Tasks Unchanged Tasks τ (ij) X X MMS Protocol for Mode-Change

11. 11 Schedulability Analysis  Multi-modal FP-schedulability requires high computation time.  No support for non-preemptive execution. Schedulability Analysis Set of real-time modes Yes: All deadlines are met. No: There could be a deadline miss. Pseudo-polynomial-time FP Schedulability Analysis

12. 12 Problem Definition Check FP-schedulability for any legal sequence of job arrivals and mode- change requests. Given M 1, M 2, … … M q, resources Ω ij, transition duration δ ij, unchanged tasks τ ij, aborted tasks A ij : Pseudo-polynomial-time FP Schedulability Analysis

13. 13 Schedulability Conditions t Execution Request Bound Function (RBF) Supply Bound Function (SBF)

14. 14 Schedulability Conditions MjMj Condition “SC 5 ” Condition “SC 1 ” Condition “SC 2 ” Condition “SC 3 ” Condition “SC 4 ”

15. 15 FP Schedulability Conditions Pseudo-polynomial-time FP Schedulability Analysis No existing MMS supports non-preemptible execution Non-preemptive and iterative FB-Schedulability Goal 1. Find Largest Busy-Intervals (Davis et al.2007) for any task 2. Response time considering multiple jobs in the busy-interal 3. Evaluate vulnerable jobs in all busy interval tktk All Higher Priority Tasks Supply from Periodic Resource Initial Condition All Higher Priority Tasks Response Time Blocking Factor

16. 16 FP Schedulability Conditions Pseudo-polynomial-time FP Schedulability Analysis tktk x t a b Carry-In New mode tasks Supply

17. 17 Radar Simulation Performance Evaluation No loss of performance Reclaimed cpu cycles could be used with low criticality tasks. Figure Courtesy: Mathworks FMCW Radar 77 GHz MATLAB Phased Array Toolbox FMCW Radar 77 GHz MATLAB Phased Array Toolbox

18. 18 Simulation Settings  Environment  MATLAB  Unchanged Tasks  Tasks 5  Aborted Tasks  Task 1  Resource  Tasks Set Compared against state-of- the-art algorithm by Phan et al. (2010). Performance Evaluation

19. 19 Simulation Results Phan approach: Schedulability using reachability (SURG) Phan approach: Schedulability using reachability (SURG) Our approach: Schedulability using Bounded Iteration SUBI requires 2 sec to finish Performance Evaluation

20. 20 Conclusion  Problem:  Existing MMS cannot exploit features of a control system.  Goal:  Develop a multi-mode systems for a shared platform  Non-preemptive executions with FP  Contributions:  Designed a protocol, developed schedulability analysis, and determined parameters of a MMS.

21. 21 Future Work  MMS Upon Multi-Core  MMS Protocol to Exploit Multi-Core  Schedulability  Resource Allocation  Thermal-Resilient Multicore Systems  Mixed Criticality Scheduling  Exploit MMS Carry-In Concepts  Exploit MMS Resource Allocation

22. 22 Multi-Modal Systems

23. 23 Design Choices for ACC  Radar Sweep  Sanding and receiving signal to measure distance and velocity  Requires non-preemptive execution during Sweep  Sweep Time  Interval between start sending and finish receiving Signals 8 micro second for max 200m distance. 2.5 micro second for max 50m distance.  Higher number of sweeps enable higher accuracy

24. 24 References Paper IdTitle Shin and Lee, ACM-X 2008 Compositional real-time scheduling framework with periodic model. Easwaren et al., RTSS 2007 Compositional analysis framework using EDP resource models. Real and Crespo, RTS 2004 Mode change protocols for real-time systems: A survey and a new proposal. Pedro and Burns, ECRTS 1998 Schedulability analysis for mode changes in flexible real- time systems. Tindell et al., RTSS 1992 Mode changes in priority preemptively scheduled systems. Sha et al., RTS 1988 Mode change protocols for priority-driven preemptive scheduling. Mok et al., MIT 1983 Fundamental design problems of distributed systems for the hard-real-time environment. Liu and Layland, ACM 1973 Scheduling algorithms for multiprogramming in a hard real-time environment.

25. 25 Schedulability Requirements  A deadline miss is preceded by a busy interval.  In the busy interval, resource demand > resource supply. To avoid deadlines: check demand ≤ supply for all interval lengths. Pseudo-polynomial-time FP Schedulability Analysis Demand Bound Function (DBF) t Execution Supply Bound Function (SBF) Calculation of DBF and SBF for multi-modal systems are not straightforward.