Byzantine Fault Tolerance for Nondeterministic Applications

All practical applications contain some degree of nondeterminism. When such applications are replicated to achieve Byzantine fault tolerance (BFT), their nondeterministic operations must be controlled to ensure replica consistency. To the best of our knowledge, only the most simplistic types of replica nondeterminism have been dealt with. Furthermore, there lacks a systematic approach to handling common types of nondeterminism. In this paper, we propose a classification of common types of replica nondeterminism with respect to the requirement of achieving Byzantine fault tolerance, and describe the design and implementation of the core mechanisms necessary to handle such nondeterminism within a Byzantine fault tolerance framework.Comment: To appear in the proceedings of the 3rd IEEE International Symposium on Dependable, Autonomic and Secure Computing, 200

Intensional and Extensional Semantics of Bounded and Unbounded Nondeterminism

We give extensional and intensional characterizations of nondeterministic functional programs: as structure preserving functions between biorders, and as nondeterministic sequential algorithms on ordered concrete data structures which compute them. A fundamental result establishes that the extensional and intensional representations of non-deterministic programs are equivalent, by showing how to construct a unique sequential algorithm which computes a given monotone and stable function, and describing the conditions on sequential algorithms which correspond to continuity with respect to each order. We illustrate by defining may and must-testing denotational semantics for a sequential functional language with bounded and unbounded choice operators. We prove that these are computationally adequate, despite the non-continuity of the must-testing semantics of unbounded nondeterminism. In the bounded case, we prove that our continuous models are fully abstract with respect to may and must-testing by identifying a simple universal type, which may also form the basis for models of the untyped lambda-calculus. In the unbounded case we observe that our model contains computable functions which are not denoted by terms, by identifying a further "weak continuity" property of the definable elements, and use this to establish that it is not fully abstract

Constructing programs or processes

We define interacting sequential programs, motivated originally by constructivist considerations. We use them to investigate notions of implementation and determinism. Process algebras do not define what can be implemented and what cannot. As we demonstrate it is problematic to do so on the set of all processes. Guided by constructivist notions we have constructed interacting sequential programs which we claim can be readily implemented and are a subset of processes

An expectation transformer approach to predicate abstraction and data independence for probabilistic programs

In this paper we revisit the well-known technique of predicate abstraction to characterise performance attributes of system models incorporating probability. We recast the theory using expectation transformers, and identify transformer properties which correspond to abstractions that yield nevertheless exact bound on the performance of infinite state probabilistic systems. In addition, we extend the developed technique to the special case of "data independent" programs incorporating probability. Finally, we demonstrate the subtleness of the extended technique by using the PRISM model checking tool to analyse an infinite state protocol, obtaining exact bounds on its performance

Flexible refinement

To help make refinement more usable in practice we introduce a general, flexible model of refinement. This is defined in terms of what contexts an entity can appear in, and what observations can be made of it in those contexts. Our general model is expressed in terms of an operational semantics, and by exploiting the well-known isomorphism between state-based relational semantics and event-based labelled transition semantics we were able to take particular models from both the state- and event-based literature, reflect on them and gradually evolve our general model. We are also able to view our general model both as a testing semantics and as a logical theory with refinement as implication. Our general model can used as a bridge between different particular special models and using this bridge we compare the definition of determinism found in different special models. We do this because the reduction of nondeterminism underpins many definitions of refinement found in a variety of special models. To our surprise we find that the definition of determinism commonly used in the process algebra literature to be at odds with determinism as defined in other special models. In order to rectify this situation we return to the intuitions expressed by Milner in CCS and by formalising these intuitions we are able to define determinism in process algebra in such a way that it no longer at odds with the definitions we have taken from other special models. Using our abstract definition of determinism we are able to construct a new model, interactive branching programs, that is an implementable subset of process algebra. Later in the chapter we show explicitly how five special models, taken from the literature, are instances of our general model. This is done simply by fixing the sets of contexts and observations involved. Next we define vertical refinement on our general model. Vertical refinement can be seen both as a generalisation of what, in the literature, has been called action refinement or non-atomic refinement. Alternatively, by viewing a layer as a logical theory, vertical refinement is a theory morphism, formalised as a Galois connection. By constructing a vertical refinement between broadcast processes and interactive branching programs we can see how interactive branching programs can be implemented on a platform providing broadcast communication. But we have been unable to extend this theory morphism to implement all of process algebra using broadcast communication. Upon investigation we show the problem arises with the examples that caused the problem with the definition of determinism on process algebra. Finally we illustrate the usefulness of our flexible general model by formally developing a single entity that contains events that use handshake communication and events that use broadcast communication

Classes of Terminating Logic Programs

Termination of logic programs depends critically on the selection rule, i.e. the rule that determines which atom is selected in each resolution step. In this article, we classify programs (and queries) according to the selection rules for which they terminate. This is a survey and unified view on different approaches in the literature. For each class, we present a sufficient, for most classes even necessary, criterion for determining that a program is in that class. We study six classes: a program strongly terminates if it terminates for all selection rules; a program input terminates if it terminates for selection rules which only select atoms that are sufficiently instantiated in their input positions, so that these arguments do not get instantiated any further by the unification; a program local delay terminates if it terminates for local selection rules which only select atoms that are bounded w.r.t. an appropriate level mapping; a program left-terminates if it terminates for the usual left-to-right selection rule; a program exists-terminates if there exists a selection rule for which it terminates; finally, a program has bounded nondeterminism if it only has finitely many refutations. We propose a semantics-preserving transformation from programs with bounded nondeterminism into strongly terminating programs. Moreover, by unifying different formalisms and making appropriate assumptions, we are able to establish a formal hierarchy between the different classes.Comment: 50 pages. The following mistake was corrected: In figure 5, the first clause for insert was insert([],X,[X]