An Adaptive Approach for Real-Time Embedded System Design, Analysis and Optimization

Year : 2024 | Volume :01 | Issue : 01 | Page : 8-14
By

    Bodhare Nitin Madhukar

  1. Krishna T. Madrewar

  1. Student, Electronics and Telecommunication Engineering Department, Deogiri Institute of Engineering and Management Studies (DIEMS) College, Maharashtra, India
  2. Assistant Professor, Electronics and Telecommunication Engineering Department, Deogiri Institute of Engineering and Management Studies (DIEMS) College, Maharashtra, India

Abstract

Real-time embedded systems are critical components in various domains, such as automotive, aerospace, healthcare, and industrial automation. The design, analysis, and optimization of these systems are vital to ensure their reliable and efficient operation. In this paper, we propose an adaptive approach for real-time embedded systems that aims to address the challenges faced during the development process while maintaining high-quality results. Our approach leverages adaptive techniques to dynamically adjust the system design based on changing environmental conditions, workload variations, and resource constraints. By incorporating adaptivity into the system, we can enhance its responsiveness, reliability, and energy efficiency. Additionally, our approach considers real-time constraints, ensuring that the system meets its timing requirements. The design phase involves modeling the system using appropriate formalisms, such as UML (Unified Modeling Language) and SML (Systems Modeling Language), to capture its architecture, components, and their interactions. Furthermore, we propose the use of modeldriven engineering (MDE) techniques to generate efficient and reliable code from the models, reducing the development time and improving maintainability. To analyze the system, we employ formal verification techniques to ensure that it adheres to specified safety and correctness properties. Model checking, theorem proving, and simulation-based approaches are utilized to validate the system’s behavior and detect potential design flaws or runtime errors. Optimization plays a crucial role in enhancing system performance. We employ techniques such as task scheduling, resource allocation, and power management to optimize the system’s execution and resource utilization. Dynamic adaptation mechanisms are employed to adjust the system’s behavior in response to varying workload conditions, ensuring optimal performance in real-time environments.

Keywords: Real-time embedded systems, adaptive approach, design, analysis, optimization, model-driven engineering, formal verification, dynamic adaptation, task scheduling, resource allocation, power management.

[This article belongs to International Journal of Solid State Innovations & Research(ijssir)]

How to cite this article: Bodhare Nitin Madhukar, Krishna T. Madrewar , An Adaptive Approach for Real-Time Embedded System Design, Analysis and Optimization ijssir 2024; 01:8-14
How to cite this URL: Bodhare Nitin Madhukar, Krishna T. Madrewar , An Adaptive Approach for Real-Time Embedded System Design, Analysis and Optimization ijssir 2024 {cited 2024 Feb 21};01:8-14. Available from: https://journals.stmjournals.com/ijssir/article=2024/view=133425


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References

  1. Gan J, Pop P, Madsen J. Tradeoff analysis for dependable real-time embedded systems during the early design phases. DTU Compute; 2014.
  2. Chang W, Goswami D, Chakraborty S, Ju L, Xue CJ, Andalam S. Memory-aware embedded control systems design. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2016 Sep 27;36(4):586-99.
  3. Li Z, Samanta A, Li Y, Soltoggio A, Kim H, Liu C. R3 On-Device Real-Time Deep Reinforcement Learning for Autonomous Robotics. In2023 IEEE Real-Time Systems Symposium (RTSS) 2023 Dec 5 (pp. 131-144). IEEE.
  4. Davis RI, Cucu-Grosjean L. A survey of probabilistic timing analysis techniques for real-time systems. LITES: Leibniz Transactions on Embedded Systems. 2019 May 14:1-60.
  5. Doerr BS, Venturella T, Jha R, Gill CD, Schmidt DC. Adaptive scheduling for real-time, embedded information systems. InGateway to the New Millennium. 18th Digital Avionics Systems Conference. Proceedings (Cat. No. 99CH37033) 1999 Oct 24 (Vol. 1, pp. 2-D). IEEE
  6. Islam S, Suri N, Balogh A, Csertán G, Pataricza A. An optimization-based design for integrated dependable real-time embedded systems. Design Automation for Embedded Systems. 2009 Dec; 13:245-85.
  7. Daghsen A. Methodology of analysis and optimization of real-time embedded systems: application to automotive field (Doctoral dissertation, Compiègne).
  8. Shin I, Lee I, Min SL. Embedded system design framework for minimizing code size and guaranteeing real-time requirements. In23rd IEEE Real-Time Systems Symposium, 2002. RTSS 2002. 2002 Dec 3 (pp. 201-211). IEEE.
  9. Baruah SK, Rosier LE, Howell RR. Algorithms and complexity concerning the preemptive scheduling of periodic, real-time tasks on one processor. Real-time systems. 1990 Nov;2(4):301-24.
  10. KARP R. chapter Reducibility among Combinatorial Problems. Complexity of Computer Computations. 1972.
  11. Racu R, Hamann A, Ernst R, Mochocki B, Hu XS. Methods for power optimization in distributed embedded systems with real-time requirements. In Proceedings of the 2006 international conference on Compilers, architecture, and synthesis for embedded systems 2006 Oct 22 (pp. 379-388).
  12. Pop P, Eles P, Peng Z, Izosimov V, Hellring M, Bridal O. Design optimization of multi-cluster embedded systems for real-time applications. In Proceedings Design, Automation and Test in Europe Conference and Exhibition 2004 Feb 16 (Vol. 2, pp. 1028-1033). IEEE.
  13. Hsiu PC, Hsieh CK, Lee DN, Kuo TW. Multilayer bus optimization for real-time embedded systems. IEEE Transactions on Computers. 2011 Oct 13;61(11):1638-50.
Regular Issue Subscription Review Article
Volume 01
Issue 01
Received May 12, 2023
Accepted December 11, 2023
Published February 21, 2024
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