Aviation electronics (avionics) are sophisticated and distributed systems aboard an airplane. The complexity of these systems is constantly growing as an increasing amount of functionalities is realized in software. T...Aviation electronics (avionics) are sophisticated and distributed systems aboard an airplane. The complexity of these systems is constantly growing as an increasing amount of functionalities is realized in software. Thanks to the performance increase, a hardware unit must no longer be dedicated to a single system function. Multicore processors for example facilitate this trend as they are offering an increased system performance in a small power envelope. In avionics, several system functions could now be integrated on a single hardware unit, if all safety requirements are still satisfied. This approach allows for further optimizations of the system architecture and substantial reductions of the space, weight and power (SWaP) footprint, and thus increases the transportation capacity. However, the complexity found in current safety-critical systems requires an automated software deployment process in order to tap this potential for further SWaP reductions. This article used a realistic flight control system as an example to present a new model-based methodology to automate the software deployment process. This methodology is based on the correctness-by-construction principle and is implemented as part of a systems engineering toolset. Furthermore, metrics and optimization criteria are presented which further help in the automatic assessment and refinement of a generated deployment. A discussion regarding a tighter integration of this approach in the entire avionics systems engineering workflow concludes this article.展开更多
Reliability is one of the most critical properties of software system.System deployment architecture is the allocation of system software components on host nodes.Software Architecture(SA) based software deployment mo...Reliability is one of the most critical properties of software system.System deployment architecture is the allocation of system software components on host nodes.Software Architecture(SA) based software deployment models help to analyze reliability of different deployments.Though many approaches for architecture-based reliability estimation exist,little work has incorporated the influence of system deployment and hardware resources into reliability estimation.There are many factors influencing system deployment.By translating the multi-dimension factors into degree matrix of component dependence,we provide the definition of component dependence and propose a method of calculating system reliability of deployments.Additionally,the parameters that influence the optimal deployment may change during system execution.The existing software deployment architecture may be ill-suited for the given environment,and the system needs to be redeployed to improve reliability.An approximate algorithm,A*_D,to increase system reliability is presented.When the number of components and host nodes is relative large,experimental results show that this algorithm can obtain better deployment than stochastic and greedy algorithms.展开更多
文摘Aviation electronics (avionics) are sophisticated and distributed systems aboard an airplane. The complexity of these systems is constantly growing as an increasing amount of functionalities is realized in software. Thanks to the performance increase, a hardware unit must no longer be dedicated to a single system function. Multicore processors for example facilitate this trend as they are offering an increased system performance in a small power envelope. In avionics, several system functions could now be integrated on a single hardware unit, if all safety requirements are still satisfied. This approach allows for further optimizations of the system architecture and substantial reductions of the space, weight and power (SWaP) footprint, and thus increases the transportation capacity. However, the complexity found in current safety-critical systems requires an automated software deployment process in order to tap this potential for further SWaP reductions. This article used a realistic flight control system as an example to present a new model-based methodology to automate the software deployment process. This methodology is based on the correctness-by-construction principle and is implemented as part of a systems engineering toolset. Furthermore, metrics and optimization criteria are presented which further help in the automatic assessment and refinement of a generated deployment. A discussion regarding a tighter integration of this approach in the entire avionics systems engineering workflow concludes this article.
基金Supported by the High Technology Research and Development Program of China(No.2008AA01A201)National High Technology Research,Development Plan of China (No.2006AA01A103)the High Technology Research and Development Program of China(No.2009AA01A404)
文摘Reliability is one of the most critical properties of software system.System deployment architecture is the allocation of system software components on host nodes.Software Architecture(SA) based software deployment models help to analyze reliability of different deployments.Though many approaches for architecture-based reliability estimation exist,little work has incorporated the influence of system deployment and hardware resources into reliability estimation.There are many factors influencing system deployment.By translating the multi-dimension factors into degree matrix of component dependence,we provide the definition of component dependence and propose a method of calculating system reliability of deployments.Additionally,the parameters that influence the optimal deployment may change during system execution.The existing software deployment architecture may be ill-suited for the given environment,and the system needs to be redeployed to improve reliability.An approximate algorithm,A*_D,to increase system reliability is presented.When the number of components and host nodes is relative large,experimental results show that this algorithm can obtain better deployment than stochastic and greedy algorithms.
