Abstract
A new scheme for dependability assurance in digital systems that allows highly selective use of redundancy with low additional overhead is introduced. This scheme, which can be described as data-driven, contrasts with and complements the conventional structure-driven and function-driven approaches to dependability procurement for digital systems. The proposed approach is based on attaching a dependability tag (d-tag) to each data object and updating pertinent d-tag values as a computation unfolds. Normal operations on data objects tend to lower d-tag values while comparisons and voting on redundant versions of a result work in the opposite direction. Judicious intermixing of dependability-lowering and dependability-raising operations, driven dynamically by the dependability requirements for various data objects, can lead to a desired overall dependability for computation results. Following an exposition of basic concepts of the proposed method with the assumption of perfect d-tags and operations, various issues in the formulation of a strategy for dealing with erroneous d-tags and imperfect operations are outlined. Applications of the proposed data-driven approach in connection with data and design diversity are presented through simple examples. Some benefits of the proposed scheme for fault tolerance in a class of critical systems are discussed.
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Parhami, B. (1991). A Data-Driven Dependability Assurance Scheme with Applications to Data and Design Diversity. In: Avižienis, A., Laprie, JC. (eds) Dependable Computing for Critical Applications. Dependable Computing and Fault-Tolerant Systems, vol 4. Springer, Vienna. https://doi.org/10.1007/978-3-7091-9123-1_12
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DOI: https://doi.org/10.1007/978-3-7091-9123-1_12
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