Independent Temporal Integration of ARINC653 Conformed Architecture — A Search Based Solution

Abstract

ARINC653-based integrated modular avionics (IMA) architecture has been widely adopted in the design of modern civil and military aircraft. IMA imposes various requirements on the underlying operating system, of which the temporal and spatial separation requirements are essential to task allocation. In practice, finding the optimal allocation configurations of tasks to enable processing modules to satisfy various temporal constraints is one of the greatest challenges. For that purpose, hundreds of tasks must be mapped into given processing modules, which has been proven to be a nonpolynomial problem. This paper introduces a search-based approach to aid in finding effective solutions for the task allocation problem in polynomial time. Two search techniques based on both population search (genetic algorithm) and neighbor search (simulated annealing), along with their multicore versions, are presented. A heuristic is designed specifically to validate whether candidate solutions fulfill various constraints implied by IMA, and thus to evaluate the fitness. Furthermore, the multicore version is designed to reduce the time delay of obtaining a new optimized configuration. The results show that both algorithms can ultimately find optimized solutions with utility rates above 90% in all configurations and can support the optimization over 100 tasks, which is an outstanding result. The result also reveals that simulated annealing can produce a better solution under limited resources, while the genetic algorithm will determine a valid solution within a shorter time period. Moreover, simulated annealing outperforms the genetic algorithm in terms of both effectiveness and efficiency with respect to solving this allocation problem with complicated constraints.

Keywords

• Temporal allocation
• Heuristic model
• IMA
• system integration