Abstract
Integrated Modular Avionics (IMA) systems, contrary to classical avionics systems, enable the execution of multiple aircraft functions on the same hardware modules. This leads to reductions, e. g. in cost and weight, but it becomes also challenging for the design space exploration, in particular due to many system deployment choices. The system management concept of IMA systems allows the expert in advance to manually partition the system into a hierarchical structure, consisting of groups (or clusters) of closely related system components. To automatically partition the software architecture of such IMA systems, we introduce an approach based on data mining methods, such as hierarchical clustering. To determine the closeness between software components, thus, to cluster components with dense intercommunication, the execution time interval (period) and the amount of data transmitted during such intercommunications are used. Leading to favourable effects w.r.t. network load at the deployment level. Furthermore, we propose a method to define cut points on the resultant clustering, in order to determine the final number of clusters, thus, the partitioning of the system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Note that we use throughout the paper the term similarity instead of distance to describe the closeness between clusters.
- 2.
The utilisation U(z) is usually used to check the schedulability of tasks bound to a processor z, e. g. for \(U(z) \le 1\) means that the binding is schedulable for EDF.
References
Aleti, A., Buhnova, B., Grunske, L., Koziolek, A., Meedeniya, I.: Software architecture optimization methods: a systematic literature review. IEEE Trans. Softw. Eng. 39(5), 658–683 (2013). https://doi.org/10.1109/TSE.2012.64
Annighöfer, B., Thielecke, F.: A systems architecting framework for optimal distributed integrated modular avionics architectures. CEAS Aeronaut. J. 6(3), 485–496 (2015). https://doi.org/10.1007/s13272-015-0156-1
ARINC 653-1: Avionics application software standard interface, October 2003
Dougherty, B., Schmidt, D.C., White, J., Kegley, R., Preston, J.: Deployment optimization for embedded flight avionics systems. In: CrossTalk, p. 31 (2011)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco (2000)
Januzaj, V.: Data mining meets system modelling. In: Proceedings of the 21st ACM/IEEE International Conference on Model Driven Engineering Languages and Systems MODELS 2018: Companion Proceedings, MODELS 2018, pp. 55–56. ACM, New York (2018). https://doi.org/10.1145/3270112.3270133
Januzaj, V., Kugele, S., Biechele, F., Mauersberger, R.: A configuration approach for IMA systems. In: Eleftherakis, G., Hinchey, M., Holcombe, M. (eds.) SEFM 2012. LNCS, vol. 7504, pp. 203–217. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33826-7_14
Kinneer, C., Herzig, S.J.I.: Dissimilarity measures for clustering space mission architectures. In: Proceedings of the 21st ACM/IEEE International Conference on Model Driven Engineering Languages and Systems ACM/IEEE MODELS 2018, pp. 392–402. ACM, New York (2018). https://doi.org/10.1145/3239372.3239390
Kugele, S., Pucea, G., Popa, R., Dieudonné, L., Eckardt, H.: On the deployment problem of embedded systems. In: ACM/IEEE International Conference on Formal Methods and Models for Codesign, MEMOCODE 2015, Austin, TX, USA, 21–23 September 2015, pp. 158–167. IEEE (2015). https://doi.org/10.1109/MEMCOD.2015.7340482
Liu, C.L., Layland, J.W.: Scheduling algorithms for multiprogramming in a hard-real-time environment. J. ACM 20(1), 46–61 (1973). https://doi.org/10.1145/321738.321743
Mehiaoui, A., Wozniak, E., Babau, J.P., Tucci-Piergiovanni, S., Mraidha, C.: Optimizing the deployment of tree-shaped functional graphs of real-time system on distributed architectures. Autom. Softw. Eng. 26(1), 1–57 (2019). https://doi.org/10.1007/s10515-018-0244-7
Özisikyilmaz, B., Memik, G., Choudhary, A.: Efficient system design space exploration using machine learning techniques. In: 2008 45th ACM/IEEE Design Automation Conference, pp. 966–969. IEEE (2008). https://doi.org/10.1145/1391469.1391712
RTCA DO-297: Integrated Modular Avionics (IMA) Development Guidance and Certification Considerations, August 2005
Yu, L., Ramaswamy, S.: Verifying design modularity, hierarchy, and interaction locality using data clustering techniques. In: Proceedings of the 45th Annual Southeast Regional Conference, ACM-SE 45, pp. 419–424. ACM, New York (2007). https://doi.org/10.1145/1233341.1233417
Zaki, M.J., Wagner Meira, J.: Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, Cambridge (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Januzaj, V., Kugele, S. (2019). On the Structure of Avionics Systems Architecture. In: Fonseca i Casas, P., Sancho, MR., Sherratt, E. (eds) System Analysis and Modeling. Languages, Methods, and Tools for Industry 4.0. SAM 2019. Lecture Notes in Computer Science(), vol 11753. Springer, Cham. https://doi.org/10.1007/978-3-030-30690-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-30690-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30689-2
Online ISBN: 978-3-030-30690-8
eBook Packages: Computer ScienceComputer Science (R0)