Prototype of Fault Adaptive Embedded Software for Large-Scale Real-Time Systems

This paper describes a comprehensive prototype of large-scale fault adaptive embedded software developed for the proposed Fermilab BTeV high energy physics experiment. Lightweight self-optimizing agents embedded within Level 1 of the prototype are responsible for proactive and reactive monitoring and mitigation based on specified layers of competence. The agents are self-protecting, detecting cascading failures using a distributed approach. Adaptive, reconfigurable, and mobile objects for reliablility are designed to be self-configuring to adapt automatically to dynamically changing environments. These objects provide a self-healing layer with the ability to discover, diagnose, and react to discontinuities in real-time processing. A generic modeling environment was developed to facilitate design and implementation of hardware resource specifications, application data flow, and failure mitigation strategies. Level 1 of the planned BTeV trigger system alone will consist of 2500 DSPs, so the number of components and intractable fault scenarios involved make it impossible to design an `expert system' that applies traditional centralized mitigative strategies based on rules capturing every possible system state. Instead, a distributed reactive approach is implemented using the tools and methodologies developed by the Real-Time Embedded Systems group.Comment: 2nd Workshop on Engineering of Autonomic Systems (EASe), in the 12th Annual IEEE International Conference and Workshop on the Engineering of Computer Based Systems (ECBS), Washington, DC, April, 200

A Model-based transformation process to validate and implement high-integrity systems

Despite numerous advances, building High-Integrity Embedded systems remains a complex task. They come with strong requirements to ensure safety, schedulability or security properties; one needs to combine multiple analysis to validate each of them. Model-Based Engineering is an accepted solution to address such complexity: analytical models are derived from an abstraction of the system to be built. Yet, ensuring that all abstractions are semantically consistent, remains an issue, e.g. when performing model checking for assessing safety, and then for schedulability using timed automata, and then when generating code. Complexity stems from the high-level view of the model compared to the low-level mechanisms used. In this paper, we present our approach based on AADL and its behavioral annex to refine iteratively an architecture description. Both application and runtime components are transformed into basic AADL constructs which have a strict counterpart in classical programming languages or patterns for verification. We detail the benefits of this process to enhance analysis and code generation. This work has been integrated to the AADL-tool support OSATE2