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Distributed Integrated Modular Avionics Resource Allocation and Scheduling Algorithm Supporting Task Migration

  • Qing ZhouEmail author
  • Kui Li
  • Guoquan Zhang
  • Liang Liu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 303)

Abstract

At present, the avionics system tends to be modularized and integrated, and the distributed integrated modular avionics system (DIMA) is proposed as the development direction of the next generation avionics system. In order to support the operation of complex tasks, DIMA needs to have an effective resource allocation and scheduling algorithm for task migration and reorganization to achieve reconstruction. However, many current resource allocation and scheduling algorithms, used in traditional avionics systems, are not available for DIMA. In view of the above problems, the paper analyzes the characteristics of the DIMA avionics system architecture model and builds abstract models of the computing resources, computing platforms and tasks. Based on the established model, an efficient task scheduling algorithm, resource allocation algorithm and task migration algorithm for DIMA avionics architecture are designed. And we do simulation experiments to establish the model, and compare the designed EWSA algorithm with the mainstream algorithm JIT-C. The results show better performance in terms of workflow average completion time, successful scheduling completion rate and optimization rate. In addition, considering the failure in the process of executing the mission, we proposed a mission migration and reorganization algorithm WMA and set different time and number of fault resources of the aircraft in the simulation experiments to evaluate the performance of WMA algorithm.

Keywords

Distributed integrated modular avionics systems Resource allocation Scheduling algorithm Task migration 

Notes

Acknowledgments

This work was supported in part by the Aeronautical Science Foundation of China under Grant 20165515001.

References

  1. 1.
    Wang, T., Qingfan, G.: Research on distributed integrated modular avionics system architecture design and implementation. In: IEEE AIAA Digital Avionics Systems Conference, pp. 1–53 (2013)Google Scholar
  2. 2.
    Annighofer, B., Thielecke, F.: A systems architecting framework for optimal distributed integrated modular avionics architectures. CEAS Aeronaut. J. 6(3), 485–496 (2015)CrossRefGoogle Scholar
  3. 3.
    Swanson, D.L.: Evolving avionics systems from federated to distributed architectures. In: Proceedings of the 17th DASC Digital Avionics Systems Conference 1998. The AIAA/IEEE/SAE, 1: D26/1-D26/8, vol. 1. IEEE (1998)Google Scholar
  4. 4.
    Han, P., Zhai, Z., Nielsen, B., et al.: A modeling framework for schedulability analysis of distributed avionics systems. arXiv: Software Engineering, pp. 150–168 (2018)CrossRefGoogle Scholar
  5. 5.
    Li, X., Xiong, H.: Modeling and analysis of integrated avionics processing systems. In: 2010 IEEE/AIAA 29th Digital Avionics Systems Conference (DASC), pp. 6.E.4-1–6.E.4-8. IEEE (2010)Google Scholar
  6. 6.
    Bao, L., Bois, G., Boland, J., et al.: Model-based method to automate the design of IMA avionics system based on cosimulation. SAE Int. J. Aerosp. 8(2), 234–242 (2015)CrossRefGoogle Scholar
  7. 7.
    Yunsheng, W., Savage, S., Hang, L., et al.: The architecture of airborne datalink system in distributed integrated modular avionics. In: Integrated Communications, Navigation and Surveillance Conference (2016)Google Scholar
  8. 8.
    Robati, T., Gherbi, A., Mullins, J., et al.: A modeling and verification approach to the design of distributed IMA architectures using TTEthernet. Procedia Comput. Sci. 83, 229–236 (2016)CrossRefGoogle Scholar
  9. 9.
    Wang, H., Niu, W.: A review on key technologies of the distributed integrated modular avionics system. Int. J. Wirel. Inf. Netw. 25(3), 358–369 (2018)CrossRefGoogle Scholar
  10. 10.
    Zhou, Q., Xiong, Z., Zhan, Z., et al.: The mapping mechanism between distributed integrated modular avionics and data distribution service. In: Fuzzy Systems and Knowledge Discovery, pp. 2502–2507 (2015)Google Scholar
  11. 11.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., et al.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw.: Pract. Exp. 41(1), 23–50 (2011)Google Scholar
  12. 12.
    Gupta, A., Faraboschi, P., Gioachin, F., et al.: Evaluating and improving the performance and scheduling of HPC applications in cloud. IEEE Trans. Cloud Comput. 4(3), 307–321 (2016)CrossRefGoogle Scholar
  13. 13.
    Dong, Z., Liu, N., Rojas-Cessa, R.: Greedy scheduling of tasks with time constraints for energy-efficient cloud-computing data centers. J. Cloud Comput. 4(1), 5 (2015)CrossRefGoogle Scholar
  14. 14.
    Panigrahy, R., Talwar, K., Uyeda, L., et al.: Heuristics for vector bin packing. research. microsoft.com (2011)Google Scholar
  15. 15.
    Li, K., Zheng, H., Wu, J.: Migration-based virtual machine placement in cloud systems. In: 2013 IEEE 2nd International Conference on Cloud Networking (CloudNet), pp. 83–90. IEEE (2013)Google Scholar
  16. 16.
    Khanna, G., Beaty, K., Kar, G.: Application performance management in virtualized server environments. In: 10th IEEE/IFIP, IEEE 2006 Network Operations and Management Symposium, 2006. NOMS 2006, pp. 373–381 (2006)Google Scholar
  17. 17.
    Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Comput.: Pract. Exp. 24(13), 1397–1420 (2012)CrossRefGoogle Scholar
  18. 18.
    Taheri, M.M., Zamanifar, K.: 2-phase optimization method for energy aware scheduling of virtual machines in cloud data centers. In: International Conference for Internet Technology and Secured Transactions, pp. 525–530 (2011)Google Scholar
  19. 19.
    Sahni, J., Vidyarthi, D.: A cost-effective deadline-constrained dynamic scheduling algorithm for scientific workflows in a cloud environment. IEEE Trans. Cloud Comput. 6, 2–18 (2015)CrossRefGoogle Scholar
  20. 20.
    Wang, Y., Cui, L., Wang, J., et al.: Spatial and temporal partitioning validation for ARINC635-based avionics software. In: International Conference on Electronics and Information Engineering (2015)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.National Key Laboratory of Science and Technology on Avionics IntegrationChina Aeronautical Radio Electronics Research InstituteShanghaiChina
  2. 2.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina

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