Design of reliable aerospace system architecture

Reliability and redundancy of safety-critical network systems is a paramount issue in system engineering. Be it in evaluating existing network systems or solving optimization problems for designing network systems, it is important to consider reliability and redundancy. This dissertation is in collaboration with AIRBUS Group, France, and they are very interest in the optimal design of safety-critical aircraft architecture systems which have to consider reliability and redundancy. To address the problem of optimally designing such systems, we chose to focus on one specific aircraft architecture system the door management system. It checks if all doors are properly closed and the cabin has the correct pressure. It is a safety-critical system since it is part of the pressurization system of an aircraft. To optimally design the DMS while considering reliability, a suitable reliability evaluation algorithm is necessary. In this dissertation, we begin by proposing a suitable reliability evaluation algorithm for a type of non series-parallel network system which includes the DMS and which can be used in an optimization model. The reliability evaluation algorithm is based on a simplification of the probability principle of inclusion-exclusion formula for intersections of unions. The simplification exploits the presence of many repeated events and has many fewer terms, which significantly reduces the number of operations needed. We compare its computational efficiency against the sum of disjoint products method KDH88 for a simple artificial example and for the DMS. Afterwards, we introduce the first MILP model for the DMS with k-redundancy. As the model is too difficult to be solved efficiently by standard MILP solvers, we discuss the issues of solving the model with general solving methods such as branch-and-bound and branch-and-price. We introduce specialized branching rules and new heuristics to solve the DMS problem with k-redundancy more efficiently and show results of computational tests which compare the specialized solving algorithms with general solving algorithms for example instances of the DMS problem. Lastly, we discuss the problems of considering reliability in MI(N)LP models for the DMS and how the new reliability evaluation algorithm can be used. In this discussion, we give different MI(N)LP models for the DMS problem with redundancy and reliability. Moreover, we propose a new heuristic for the DMS problem with redundancy and reliability. It is based on branch-and-bound, the Dantzig-Wolfe decomposition and on the new reliability evaluation algorithm. We show results of computational tests of the new heuristic for example instances of the DMS problem and discuss its validity

Exact two-terminal reliability of some directed networks

The calculation of network reliability in a probabilistic context has long been an issue of practical and academic importance. Conventional approaches (determination of bounds, sums of disjoint products algorithms, Monte Carlo evaluations, studies of the reliability polynomials, etc.) only provide approximations when the network's size increases, even when nodes do not fail and all edges have the same reliability p. We consider here a directed, generic graph of arbitrary size mimicking real-life long-haul communication networks, and give the exact, analytical solution for the two-terminal reliability. This solution involves a product of transfer matrices, in which individual reliabilities of edges and nodes are taken into account. The special case of identical edge and node reliabilities (p and rho, respectively) is addressed. We consider a case study based on a commonly-used configuration, and assess the influence of the edges being directed (or not) on various measures of network performance. While the two-terminal reliability, the failure frequency and the failure rate of the connection are quite similar, the locations of complex zeros of the two-terminal reliability polynomials exhibit strong differences, and various structure transitions at specific values of rho. The present work could be extended to provide a catalog of exactly solvable networks in terms of reliability, which could be useful as building blocks for new and improved bounds, as well as benchmarks, in the general case

Taming Uncertainty in the Assurance Process of Self-Adaptive Systems: a Goal-Oriented Approach

Goals are first-class entities in a self-adaptive system (SAS) as they guide the self-adaptation. A SAS often operates in dynamic and partially unknown environments, which cause uncertainty that the SAS has to address to achieve its goals. Moreover, besides the environment, other classes of uncertainty have been identified. However, these various classes and their sources are not systematically addressed by current approaches throughout the life cycle of the SAS. In general, uncertainty typically makes the assurance provision of SAS goals exclusively at design time not viable. This calls for an assurance process that spans the whole life cycle of the SAS. In this work, we propose a goal-oriented assurance process that supports taming different sources (within different classes) of uncertainty from defining the goals at design time to performing self-adaptation at runtime. Based on a goal model augmented with uncertainty annotations, we automatically generate parametric symbolic formulae with parameterized uncertainties at design time using symbolic model checking. These formulae and the goal model guide the synthesis of adaptation policies by engineers. At runtime, the generated formulae are evaluated to resolve the uncertainty and to steer the self-adaptation using the policies. In this paper, we focus on reliability and cost properties, for which we evaluate our approach on the Body Sensor Network (BSN) implemented in OpenDaVINCI. The results of the validation are promising and show that our approach is able to systematically tame multiple classes of uncertainty, and that it is effective and efficient in providing assurances for the goals of self-adaptive systems

Integration of tools for the Design and Assessment of High-Performance, Highly Reliable Computing Systems (DAHPHRS), phase 1

Systems for Space Defense Initiative (SDI) space applications typically require both high performance and very high reliability. These requirements present the systems engineer evaluating such systems with the extremely difficult problem of conducting performance and reliability trade-offs over large design spaces. A controlled development process supported by appropriate automated tools must be used to assure that the system will meet design objectives. This report describes an investigation of methods, tools, and techniques necessary to support performance and reliability modeling for SDI systems development. Models of the JPL Hypercubes, the Encore Multimax, and the C.S. Draper Lab Fault-Tolerant Parallel Processor (FTPP) parallel-computing architectures using candidate SDI weapons-to-target assignment algorithms as workloads were built and analyzed as a means of identifying the necessary system models, how the models interact, and what experiments and analyses should be performed. As a result of this effort, weaknesses in the existing methods and tools were revealed and capabilities that will be required for both individual tools and an integrated toolset were identified