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Control-schedule co-design for fast stabilization in real time systems facing repeated reconfigurations

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Abstract

Efficient scheduling of tasks in cyber-physical systems or internet-of-things is a challenging prospect primarily due to their demands to meet critical performance goals in the face of stringent resource constraints. In addition, to enhance ease of implementation and more efficient usage of resources, these schedulers are many-a-times restricted to be non-preemptive, where jobs once started must be continuously executed until completion. In this work, we address the following resource allocation issue. Given, (i) a set of functionalities (tasks) whose performance qualities are directly proportional to the rates at which they receive service from a resource, and (ii) a discrete set of allowable alternative execution rates for each task, the objective is to determine a non-preemptive execution schedule for the tasks with appropriately chosen rates over time, such that the performance of the overall system combining all functionalities, is optimized. In this work, performance of the system is considered to be directly proportional to the time taken to re-stabilize all functionalities within stipulated thresholds, subsequent to reconfigurations in the desired outputs of a subset of these functionalities. We first propose branch and bound based techniques for determining optimal schedules under different restrictions on the adaptability of execution rates for the tasks. However, although optimal, branch and bound based solutions incur significant computational overheads, which often make them prohibitively expensive towards online application, especially for large task-set sizes. Hence, we further propose two fast and efficient heuristic strategies to quickly obtain near optimal schedules. Experimental results show that the proposed schemes are able to achieve significant performance gain, 30–55% in case of optimal strategy and 10–50% for heuristic methods compared to traditional fixed rate execution mode.

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References

  1. Benhai Z, Dapeng Y (2013) Research on reserved real-time scheduling approach for cyber and physical system. In: IEEE 6th international conference on intelligent networks and intelligent systems (ICINIS), pp 62–65

  2. Buttazzo GC, Lipari G, Abeni L (1998) Elastic task model for adaptive rate control. In: Proceedings of the IEEE 19th real-time systems symposium, pp 286–295

  3. Buttazzo GC, Lipari G, Caccamo M, Abeni L (2002) Elastic scheduling for flexible workload management. IEEE Trans Comput 51(3):289–302

    Article  Google Scholar 

  4. Caccamo M, Buttazzo G, Sha L (2000) Elastic feedback control. In: IEEE 12th Euromicro conference on real-time systems, pp 121–128

  5. Cao X, Cheng P, Chen J, Sun Y (2013) An online optimization approach for control and communication codesign in networked cyber-physical systems. IEEE Trans Ind Inform 9(1):439–450

    Article  Google Scholar 

  6. Cervin A, Velasco M, Martí P, Camacho A (2011) Optimal online sampling period assignment: theory and experiments. IEEE Trans Control Syst Technol 19(4):902–910

    Article  Google Scholar 

  7. Du C, Tan L, Dong Y (2015) Period selection for integrated controller tasks in cyber-physical systems. Chin J Aeronaut 28(3):894–902

    Article  Google Scholar 

  8. Eker J, Hagander P, Årzén KE (2000) A feedback scheduler for real-time controller tasks. Control Eng Pract 8(12):1369–1378

    Article  Google Scholar 

  9. Gaid El Mongi Ben, Çela AS, Hamam Y (2009) Optimal real-time scheduling of control tasks with state feedback resource allocation. IEEE Trans Control Syst Technol 17(2):309–326

    Article  Google Scholar 

  10. Görges D, Izák M, Liu S (2007) Optimal control of systems with resource constraints. In: IEEE 46th conference on decision and control, pp 1070–1075

  11. Goswami D, Masrur A, Schneider R, Xue CJ, Chakraborty S (2013) Multirate controller design for resource-and schedule-constrained automotive ecus. In: Proceedings of the conference on design, automation and test in Europe, EDA consortium, pp 1123–1126

  12. Henriksson D, Cervin A (2005) Optimal on-line sampling period assignment for real-time control tasks based on plant state information. In: IEEE 44th conference on decision and control, European control conference (CDC-ECC), IEEE, pp 4469–4474

  13. Jeffay K, Stanat DF, Martel CU (1991) On non-preemptive scheduling of period and sporadic tasks. In: Proceedings of the IEEE 12th real-time systems symposium, pp 129–139

  14. Karmakar G, Kabra A, Ramamritham K (2013) Coordinated scheduling of thermostatically controlled real-time systems under peak power constraint. In: IEEE 19th real-time and embedded technology and applications symposium (RTAS), pp 33–42

  15. Kohler WH, Steiglitz K (1974) Characterization and theoretical comparison of branch-and-bound algorithms for permutation problems. J ACM (JACM) 21(1):140–156

    Article  MathSciNet  Google Scholar 

  16. Lu C, Stankovic JA, Tao G, Son SH (1999) Design and evaluation of a feedback control edf scheduling algorithm. In: IEEE 1999 20th proceedings of the real-time systems symposium, IEEE, pp 56–67

  17. Lu C, Stankovic JA, Abdelzaher TF, Tao G, Son SH, Marley M (2000) Performance specifications and metrics for adaptive real-time systems. In: Proceedings of the IEEE 21st real-time systems symposium, pp 13–23

  18. Lu C, Stankovic JA, Son SH, Tao G (2002) Feedback control real-time scheduling: framework, modeling, and algorithms. Real-Time Syst 23(1–2):85–126

    Article  Google Scholar 

  19. Marti P, Lin C, Brandt SA, Velasco M, Fuertes JM (2004) Optimal state feedback based resource allocation for resource-constrained control tasks. In: Proceedings of the IEEE 25th international real-time systems symposium, pp 161–172

  20. Mayank J, Mondal A (2015) Performance optimization of real time control systems using variable time period. In: IEEE 2015 19th international symposium on VLSI design and test (VDAT), pp 1–6

  21. Palopoli L, Pinello C, Bicchi A, Sangiovanni-Vincentelli A (2005) Maximizing the stability radius of a set of systems under real-time scheduling constraints. IEEE Trans Autom Control 50(11):1790–1795

    Article  MathSciNet  Google Scholar 

  22. Pelusi D (2012) Improving settling and rise times of controllers via intelligent algorithms. In: IEEE 2012 14th international conference on modelling and simulation, pp 187–192

  23. Phavorin G, Richard P (2015) Cache-related preemption delays and real-time scheduling: a survey for uniprocessor systems. Technical Report, Citeseer

  24. Russell SJ, Norvig P (2016) Artificial intelligence: a modern approach. Pearson Education Limited, Kuala Lumpur

    MATH  Google Scholar 

  25. Seto D, Lehoczky JP, Sha L, Shin KG (1996) On task schedulability in real-time control systems. In: IEEE 17th real-time systems symposium, pp 13–21

  26. Seto D, Lehoczky JP, Sha L (1998) Task period selection and schedulability in real-time systems. In: Proceedings of the IEEE 19th real-time systems symposium, pp 188–198

  27. Stankovic JA, He T, Abdelzaher T, Marley M, Tao G, Son S, Lu C (2001) Feedback control scheduling in distributed real-time systems. In: IEEE 2001 22nd proceedings of the real-time systems symposium, (RTSS 2001), IEEE, pp 59–70

  28. Yepez J, Fuertes J, Marti P (2003) The large error first (lef) scheduling policy for real-time control systems. In: Work in progress proceedings of the 24th IEEE real-time systems symposium (RTSS WIP 2003), pp 63–66

  29. Zhang F, Szwaykowska K, Wolf W, Mooney V (2008) Task scheduling for control oriented requirements for cyber-physical systems. In: IEEE real-time systems symposium, pp 47–56

  30. Zhang F, Shi Z, Wolf W (2009) A dynamic battery model for co-design in cyber-physical systems. In: IEEE 29th international conference on distributed computing systems workshops, pp 51–56

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Authors and Affiliations

  1. Indian Institute of Technology Patna, Patna, India

    Jaishree Mayank & Arijit Mondal

  2. Indian Institute of Technology Guwahati, Guwahati, India

    Arnab Sarkar

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  1. Jaishree Mayank
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  2. Arijit Mondal
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  3. Arnab Sarkar
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Correspondence to Jaishree Mayank.

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Mayank, J., Mondal, A. & Sarkar, A. Control-schedule co-design for fast stabilization in real time systems facing repeated reconfigurations. Des Autom Embed Syst 23, 79–101 (2019). https://doi.org/10.1007/s10617-019-09221-6

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