Communication-Aware Prediction-Based Online Scheduling in High-Performance Real-Time Embedded Systems

  • Baptiste Goupille-LescarEmail author
  • Eric Lenormand
  • Nikos Parlavantzas
  • Christine Morin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11336)


Current high-end, data-intensive real-time embedded sensor applications (e.g., radar, optronics) require very specific computing platforms. The nature of such applications and the environment in which they are deployed impose numerous constraints, including real-time constraints, and computing throughput and latency needs. Static application placement is traditionally used to deal with these constraints. However, this approach fails to provide adaptation capabilities in an environment in constant evolution. Through the study of an industrial radar use-case, our work aims at mitigating the aforementioned limitations by proposing a low-latency online resource manager derived from techniques used in large-scale systems, such as cloud and grid environments. The resource manager introduced in this paper is able to dynamically allocate resources to fulfill requests coming from several sensors, making the most of the computing platform while providing guaranties on non-functional properties and Quality of Service (QoS) levels. Thanks to the load prediction implemented in the manager, we are able to achieve a 83% load increase before overloading the platform while managing to reduce ten times the incurred QoS penalty. Further methods to reduce the impact of the overload are as well as possible future improvements are proposed and discussed.


Embedded systems Real-time Scheduling Dynamic resource management 



This work was made possible thanks to the support of the Surface Radar Business Line of Thales.


  1. 1.
  2. 2.
  3. 3.
    Baruah, S., Li, H., Stougie, L.: Towards the design of certifiable mixed-criticality systems. In: 2010 16th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), pp. 13–22. IEEE (2010)Google Scholar
  4. 4.
    Braun, T.D., et al.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib. Comput. 61(6), 810–837 (2001)CrossRefGoogle Scholar
  5. 5.
    Cai, Z., Li, X., Ruiz, R., Li, Q.: A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds. Futur. Gener. Comput. Syst. 71, 57–72 (2017)CrossRefGoogle Scholar
  6. 6.
    Chen, H., Wang, F., Helian, N., Akanmu, G.: User-priority guided min-min scheduling algorithm for load balancing in cloud computing. In: 2013 National Conference on Parallel computing technologies (PARCOMPTECH), pp. 1–8. IEEE (2013)Google Scholar
  7. 7.
    Costache, S., Parlavantzas, N., Morin, C., Kortas, S.: Merkat: a market-based SLO-driven cloud platform. In: 2013 IEEE 5th International Conference on Cloud Computing Technology and Science (CloudCom). vol. 1, pp. 403–410, December 2013.
  8. 8.
    De Sensi, D., Torquati, M., Danelutto, M.: A reconfiguration algorithm for power-aware parallel applications. ACM Trans. Archit. Code Optim. 13(4), 43:1–43:25 (2016).,
  9. 9.
    Gadioli, D., Palermo, G., Silvano, C.: Application autotuning to support runtime adaptivity in multicore architectures. In: 2015 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS), pp. 173–180. IEEE (2015)Google Scholar
  10. 10.
    García-Valls, M., Cucinotta, T., Lu, C.: Challenges in real-time virtualization and predictable cloud computing. J. Syst. Arch. 60(9), 726–740 (2014)., Scholar
  11. 11.
    Giannopoulou, G., Stoimenov, N., Huang, P., Thiele, L.: Scheduling of mixed-criticality applications on resource-sharing multicore systems. In: 2013 Proceedings of the International Conference on Embedded Software (EMSOFT), pp. 1–15, September 2013.
  12. 12.
    Gupta, A., Kumar, A., Nagarajan, V., Shen, X.: Stochastic load balancing on unrelated machines. In: Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1274–1285. SIAM (2018)CrossRefGoogle Scholar
  13. 13.
    Khemka, B., et al.: Utility maximizing dynamic resource management in an oversubscribed energy-constrained heterogeneous computing system. Sustain. Comput. Inform. Syst. 5, 14–30 (2015)., Scholar
  14. 14.
    Kousalya, G., Balakrishnan, P., Pethuru Raj, C.: Workflow scheduling algorithms and approaches. In: Automated Workflow Scheduling in Self-Adaptive Clouds. CCN, pp. 65–83. Springer, Cham (2017). Scholar
  15. 15.
    Li, H., Baruah, S.: An algorithm for scheduling certifiable mixed-criticality sporadic task systems. In: 2010 IEEE 31st Real-Time Systems Symposium (RTSS), pp. 183–192, November 2010.
  16. 16.
    Liu, J., Pacitti, E., Valduriez, P., Mattoso, M.: A survey of data-intensive scientific workflow management. J. Grid Comput. 13(4), 457–493 (2015)CrossRefGoogle Scholar
  17. 17.
    Lucier, B., Menache, I., Naor, J.S., Yaniv, J.: Efficient online scheduling for deadline-sensitive jobs. In: Proceedings of the Twenty-Fifth Annual ACM Symposium on Parallelism in Algorithms and Architectures, pp. 305–314. ACM (2013)Google Scholar
  18. 18.
    Megow, N., Uetz, M., Vredeveld, T.: Models and algorithms for stochastic online scheduling. Math. Oper. Res. 31(3), 513–525 (2006)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Nasri, M., Brandenburg, B.B.: Offline equivalence: a non-preemptive scheduling technique for resource-constrained embedded real-time systems (outstanding paper). In: 2017 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), pp. 75–86. IEEE (2017)Google Scholar
  20. 20.
    Ptolemaeus, C. (ed.): System Design, Modeling, and Simulation using Ptolemy II. (2014).
  21. 21.
    Quan, W., Pimentel, A.D.: A hierarchical run-time adaptive resource allocation framework for large-scale mpsoc systems. Des. Autom. Embed. Syst. 20(4), 311–339 (2016)CrossRefGoogle Scholar
  22. 22.
    Ren, J., Phan, L.T.X.: Mixed-criticality scheduling on multiprocessors using task grouping. In: 2015 27th Euromicro Conference on Real-Time Systems (ECRTS), pp. 25–34. IEEE (2015)Google Scholar
  23. 23.
    Rodriguez, M.A., Buyya, R.: Deadline based resource provisioningand scheduling algorithm for scientific workflows on clouds. IEEE Trans. Cloud Comput. 2(2), 222–235 (2014)CrossRefGoogle Scholar
  24. 24.
    Skutella, M., Sviridenko, M., Uetz, M.: Unrelated machine scheduling with stochastic processing times. Math. Oper. Res. 41(3), 851–864 (2016)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Tang, X., Li, X., Fu, Z.: Budget-constraint stochastic task scheduling on heterogeneous cloud systems. Concurr. Comput. Pract. Exp. 29(19), e4210 (2017)CrossRefGoogle Scholar
  26. 26.
    Wang, Z., Su, X.: Dynamically hierarchical resource-allocation algorithm in cloud computing environment. J. Supercomput. 71(7), 2748–2766 (2015).,
  27. 27.
    Warneke, D., Kao, O.: Exploiting dynamic resource allocation for efficient parallel data processing in the cloud. IEEE Trans. Parallel Distrib. Syst. 22(6), 985–997 (2011). Scholar
  28. 28.
    Xu, R., Wang, Y., Huang, W., Yuan, D., Xie, Y., Yang, Y.: Near-optimal dynamic priority scheduling strategy for instance-intensive business workflows in cloud computing. Concurr. Comput. Pract. Exp. 29(18), e4167 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Baptiste Goupille-Lescar
    • 1
    • 2
    Email author
  • Eric Lenormand
    • 1
  • Nikos Parlavantzas
    • 2
    • 3
  • Christine Morin
    • 2
  1. 1.Thales Research and TechnologyPalaiseauFrance
  2. 2.Inria, IRISARennesFrance
  3. 3.INSA Rennes, IRISARennesFrance

Personalised recommendations