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Resource Management for Real-Time Environments

Resource Management for Real-Time Environments. Instructor: Dr. Subra Ganesan Presented by: Pooja Mehta Date: 10/16/06. Presentation outline. Motivation Problem illustrations of Radar systems Basic Radar model Tasks with Harmonic Periods Offline Template Generation

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Resource Management for Real-Time Environments

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  1. Resource Management for Real-Time Environments Instructor: Dr. Subra Ganesan Presented by: Pooja Mehta Date: 10/16/06

  2. Presentation outline • Motivation • Problem illustrations of Radar systems • Basic Radar model • Tasks with Harmonic Periods • Offline Template Generation • Schedule construction on Hyperperiod • Some Proposed Solutions • Feasible Intervals • Online Template Generation • Finite Horizon Scheduling • Conclusions

  3. TASK 1 0 T1 2T1 3T1 TASK 2 0 T2 2T2 3T2 4T2 Periodic tasks Known periods Known execution times Known deadlines Motivation • The traditional notion of real-time systems • However, many important applications lack this simple structure • Complexity arises because of • Stringent task requirements • Scale of systems

  4. Presentation outline • Motivation • Problem illustrations of Radar systems • Basic Radar model • Tasks with Harmonic Periods • Offline Template Generation • Schedule construction on Hyperperiod • Some Proposed Solutions • Feasible Intervals • Online Template Generation • Finite Horizon Scheduling • Conclusions

  5. Basic Radar Model Ai : Transmit Power txi : Transmit pulse width twi: Wait time tri : Receive time Radar System Model

  6. End-to-end deadline FILTERING CLASSIFICATION COMMAND GENERATION Execution requirements on each node Processing requirements for radar tasks • Signals received at the antenna need to be processed (backend computations) • At multiple stages • Within an end-to-end deadline

  7. Radar dwell scheduling (N+1)th job Nth job Last illumination time Illumination window Processing window Temporal distance

  8. Radar dwell Dwell packing Power (kw) P(t) t Radar dwell scheduling Constraints on power Non-preemptible Reusable Question: How do we schedule many such tasks?

  9. Template-based Schedule

  10. Q-RAM & Scheduler Admission Control • Reduce the resource utilization bounds • Changes at irregular intervals

  11. Offline Template Generation

  12. Offline Template Generation • task types were restricted to a finite set • appropriate templates were chosen during online operation • Resource managers could only pick task types from the finite set.

  13. Presentation outline • Motivation • Problem illustrations of Radar systems • Basic Radar model • Tasks with Harmonic Periods • Offline Template Generation • Schedule construction on Hyperperiod • Some Proposed Solutions • Feasible Intervals • Online Template Generation • Finite Horizon Scheduling • Conclusions

  14. Dynamic Q-RAM Optimization

  15. Online Template Generation Arbitrary tasks can be interleaved or nested on-the-fly.

  16. Online Template Generation • arbitrary task types can be combined on-the-fly to produce a template; • provides greater freedom to a resource manager. • The resource manager can tune the parameters of each task with finer granularity. • Online template generation is carried out using a fast heuristic based on task characteristics.

  17. Resource management framework

  18. Dwell packing Radar dwell scheduling – issues Temporal distance constraints Constraints on power Non-preemptible

  19. Feasible intervals Synthetic period Temporal distance Fixed length templates for packing dwells Heuristics for building templates Template length divides the smallest period Dwell scheduling – solutions

  20. Modular Schedule Updates Without modular schedule update With modular schedule update

  21. Constraints • Temporal Constraints When new tasks are admitted, the schedule changes only within the templates in which new jobs are inserted. • Energy Constraints Since a job is inserted into a template only if it will not cause the energy level to exceed ETH, and since job insertions assume that the energy level at the start of a template is ETH, job insertions are guaranteed to be safe in terms of the energy constraint.

  22. Cool-down duration for Dwell A Cool-down duration for Dwell B Dealing with the energy constraint • Cooldown time ETH L

  23. horizon Finite horizon scheduling Task B arrives; is rejected A A A A A T+H T Task A departs Task B need not have been rejected Feasible intervals for Task B

  24. Scheduling overhead

  25. Reduced task rejection rates

  26. Utilization improvement Maximum achievable with energy bound

  27. Presentation outline • Motivation • Problem illustrations of Radar systems • Basic Radar model • Tasks with Harmonic Periods • Offline Template Generation • Schedule construction on Hyperperiod • Some Proposed Solutions • Feasible Intervals • Online Template Generation • Finite Horizon Scheduling • Conclusions

  28. Conclusions • All Real time systems doesn’t follow Ideal model • Determination of Schedulability Regions • Knowing the Schedule not just the schedulability • Systems should be able to handle unseen tasks, without violating the Temporal and Energy constraints

  29. References [1] C.-S. Shih, S. Gopalakrishnan, P. Ganti, M. Caccamo, L. Sha: “Template-based real-time dwell scheduling with energy constraint,” IEEE Real-Time Technology and Applications Symposium, Washington D.C., USA, May 2003. [2] C.-S. Shih, S. Gopalakrishnan, P. Ganti, M. Caccamo, L.Sha: “Scheduling real-time dwells using tasks withsynthetic periods,” IEEE Real-Time Systems Symposium, Cancun, Mexico, December 2003. [3] C.-G. Lee, P.-S. Kang, C.-S. Shih, L. Sha: “Radar dwell scheduling considering physical characteristics of phased array antenna,” IEEE Real-Time Systems Symposium,Cancun, Mexico, December 2003. [4] J. Hansen, S. Ghosh, R. Rajkumar, J. Lehoczky: “Resource management of highly configurable tasks,” Workshop on Parallel and Distributed Real-Time Systems, Santa Fe, USA, April 2004.

  30. References Contd.. [5] MURI on QoS in Surveillance and Control Radar Dwell Scheduling for Phased-Array Radars PIs Lui Sha Marco Caccamo Chang-Gun Lee [6] GOPALAKRISHNAN, S. Resource Management for Real-Time Environments. PhD thesis, University of Illinois, Urbana, Illinois, Dec. 2005. [7] GOPALAKRISHNAN, S., CACCAMO, M., SHIH, C.-S., SHA, L., AND LEE, C.-G. Finite horizon scheduling of radar dwells with online template construction. Real-Time Systems (2006).

  31. Thank you !!!!

  32. Questions and Answers

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