Consider a fully-connected synchronous distributed system consisting of @math nodes, where up to @math nodes may be faulty and every node starts in an arbitrary initial state. In the synchronous @math -counting problem, all nodes need to eventually agree on a counter that is increased by one modulo @math in each round for given @math . In the self-stabilising firing squad problem, the task is to eventually guarantee that all non-faulty nodes have simultaneous responses to external inputs: if a subset of the correct nodes receive an external "go" signal as input, then all correct nodes should agree on a round (in the not-too-distant future) in which to jointly output a "fire" signal. Moreover, no node should generate a "fire" signal without some correct node having previously received a "go" signal as input. We present a framework reducing both tasks to binary consensus at very small cost. For example, we obtain a deterministic algorithm for self-stabilising Byzantine firing squads with optimal resilience @math , asymptotically optimal stabilisation and response time @math , and message size @math . As our framework does not restrict the type of consensus routines used, we also obtain efficient randomised solutions, and it is straightforward to adapt our framework for other types of permanent faults.
In order to achieve better communication and state complexity, the use of computational algorithm design and synthesis techniques have also been investigated @cite_17 @cite_12 . While this line of research has produced novel optimal and computer-verified algorithms, so far the techniques have not scaled beyond @math faulty node due to the inherent combinatorial explosion in the search space of potential algorithms.
@cite_12 @cite_17
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Fault-tolerant distributed algorithms play an increasingly important role in many applications, and their correct and efficient implementation is notoriously difficult. We present an automatic approach to synthesise provably correct fault-tolerant distributed algorithms from formal specifications in linear-time temporal logic. The supported system model covers synchronous reactive systems with finite local state, while the failure model includes strong self-stabilisation as well as Byzantine failures. The synthesis approach for a fixed-size network of processes is complete for realisable specifications, and can optimise the solution for small implementations and short stabilisation time. To solve the bounded synthesis problem with Byzantine failures more efficiently, we design an incremental, CEGIS-like loop. Finally, we define two classes of problems for which our synthesis algorithm obtains solutions that are not only correct in fixed-size networks, but in networks of arbitrary size. Consider a complete communication network on n nodes. In synchronous 2-counting, the nodes receive a common clock pulse and they have to agree on which pulses are "odd" and which are "even". Furthermore, the solution needs to be self-stabilising (reaching correct operation from any initial state) and tolerate f Byzantine failures (nodes that send arbitrary misinformation). Prior algorithms either require a source of random bits or a large number of states per node. In this work, we give fast state-optimal deterministic algorithms for the first non-trivial case f = 1 . To obtain these algorithms, we develop and evaluate two different techniques for algorithm synthesis. Both are based on casting the synthesis problem as a propositional satisfiability (SAT) problem; a direct encoding is efficient for synthesising time-optimal algorithms, while an approach based on counter-example guided abstraction refinement discovers non-optimal algorithms quickly. We develop computational techniques to find algorithms for synchronous 2-counting.Automated synthesis yields state-optimal self-stabilising fault-tolerant algorithms.We give a thorough experimental comparison of our two SAT-based synthesis techniques.A direct SAT encoding is more efficient for finding time-optimal algorithms.An iterative CEGAR-based approach finds non-optimal algorithms quickly.