4.7 Article

Multiobjective Optimization Approach for a Portable Development of Reconfigurable Real-Time Systems: From Specification to Implementation

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2017.2781460

关键词

Mixed integer linear programming (MILP); POSIX; real-time system; reconfiguration; RT-Java

资金

  1. National Natural Science Foundation of China [61603285, 61472295, 61672400]
  2. Recruitment Program of Global Experts
  3. Science and Technology Development Fund, MSAR [078/2015/A3, 106/20156/A3]
  4. International Scientific Partnership Program ISPP at King Saud University [ISPP 0079]

向作者/读者索取更多资源

This paper deals with the reconfigurable real-time systems that should be adapted to their environment under real-time constraints. The reconfiguration allows moving from one implementation to another by adding/removing/modifying parameters of real-time software tasks which should meet related deadlines. Implementing those systems as threads generates a complex system code due to the large number of threads, which may lead to a reconfiguration time overhead as well as the energy consumption and the memory allocation increase. Thus this paper proposes a multiobjective optimization approach for reconfigurable systems called MO2R2S for the development of a reconfigurable real-time system. Given a specification, the proposed approach aims to produce an optimal design while ensuring the system feasibility. We focus on three optimization criteria: 1) response time; 2) memory allocation; and 3) energy consumption. To address the portability issue, the optimal design is then transformed to an abstract code that may in turn be transformed to a concrete code which is specific to a procedural programming (i.e., POSIX) or an object-oriented language (i.e., RT-Java). The (MORS)-R-2-S-2 approach allows reducing the number of threads by minimizing the redundancy between the implementation sets. By an experimental study, such optimization permits to decrease the memory allocation by 28.89%, the energy consumption by 40.2%, and the response time by 61.32%.

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