Relational transducers for declarative networking

Ameloot, Tom J.; Neven, Frank; Bussche, Jan Van den

doi:10.1145/1989284.1989321

Cited by 39 publications

(129 citation statements)

References 20 publications

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“…Intuitively, a monotonic program makes forward progress over time: it never "retracts" an earlier conclusion in the face of new information. We proposed the CALM theorem, which established that all monotonic programs are confluent (invariant to message reordering and retry) and hence eventually consistent [5,18,25]. Monotonicity of a Datalog program is straightforward to determine conservatively from syntax, so the CALM theorem provides the basis for a simple analysis of the consistency of distributed programs.…”

Section: Convergent Modulesmentioning

confidence: 99%

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Logic and lattices for distributed programming

Conway

Marczak

Alvaro

et al. 2012

Proceedings of the Third ACM Symposium on Cloud Computing

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In recent years there has been interest in achieving applicationlevel consistency criteria without the latency and availability costs of strongly consistent storage infrastructure. A standard technique is to adopt a vocabulary of commutative operations; this avoids the risk of inconsistency due to message reordering. A more powerful approach was recently captured by the CALM theorem, which proves that logically monotonic programs are guaranteed to be eventually consistent.In logic languages such as Bloom, CALM analysis can automatically verify that program modules achieve consistency without coordination.In this paper we present Bloom L , an extension to Bloom that takes inspiration from both these traditions. Bloom L generalizes Bloom to support lattices and extends the power of CALM analysis to whole programs containing arbitrary lattices. We show how the Bloom interpreter can be generalized to support efficient evaluation of lattice-based code using well-known strategies from logic programming. Finally, we use Bloom L to develop several practical distributed programs, including a key-value store similar to Amazon Dynamo, and show how Bloom L encourages the safe composition of small, easy-to-analyze lattices into larger programs.

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Section: Convergent Modulesmentioning

confidence: 99%

“…All monotonic programs are "eventually consistent" or confluent: for any given input, all program executions result in the same final state regardless of network non-determinism [5,25]. Hence, monotonic logic is a useful building block for loosely consistent distributed programming.…”

Section: Calm Analysismentioning

confidence: 99%

Logic and lattices for distributed programming

Conway

Marczak

Alvaro

et al. 2012

Proceedings of the Third ACM Symposium on Cloud Computing

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“…Recently, Ameloot et al explored Hellerstein's CALM theorem using relational transducers [8]. They proved that monotonic firstorder queries are exactly the set of queries that can be computed in a coordination-free fashion in that transducer formalism.…”

Section: Related Workmentioning

confidence: 99%

“…This has led to activity in language and system design (e.g., [4,9,14,32]), as well as formal models for distributed computation using such languages (e.g., [8,36,37]). …”

Section: Introductionmentioning

confidence: 99%

“…This is inspired in part by the CALM theorem [5,24], which established a connection between confluence and monotonicity; subsequent formalizations proved that monotonic distributed programs are indeed guaranteed to be confluent [1,8]. In terms of expressivity, Immerman's celebrated result regarding the collapse of the fixpoint hierarchy established that PTIME is captured by a similar monotonic language: semipositive Datalog (Datalog¬ where negation is restricted to EDB relations) augmented with an ordering over the universe [25].…”

Section: Introductionmentioning

confidence: 99%

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Confluence Analysis for Distributed Programs: A Model-Theoretic Approach

Marczak

Alvaro

Conway

et al. 2012

Lecture Notes in Computer Science

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Building on recent interest in distributed logic programming, we take a model-theoretic approach to analyzing confluence of asynchronous distributed programs. We begin with a model-theoretic semantics for Dedalus and develop the concept of ultimate models to capture the non-deterministic eventual outcomes of distributed programs. After demonstrating the undecidability of checking confluence for Dedalus programs, we look for restricted sub-languages that guarantee confluence while providing adequate expressivity. We observe that a simple semipositive restriction called Dedalus + guarantees confluence while capturing PTIME, but demonstrate that the limited use of negation in Dedalus + makes certain simple and practical programs very difficult to express. To remedy this, we introduce Dedalus S , a restriction of Dedalus that allows a natural use of negation in the spirit of stratified negation, but retains the confluence of Dedalus + and similarly captures PTIME.

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Recent Advances in Declarative Networking

Loo

Gill

Liu

et al. 2012

Practical Aspects of Declarative Languages

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Declarative networking is a programming methodology that enables developers to concisely specify network protocols and services, and directly compile these specifications into a dataflow framework for execution. This paper describes recent advances in declarative networking, tracing its evolution from a rapid prototyping framework towards a platform that serves as an important bridge connecting formal theories for reasoning about protocol correctness and actual implementations. In particular, the paper focuses on the use of declarative networking for addressing four main challenges in the distributed systems development cycle: the generation of safe routing implementations, debugging, security and privacy, and optimizing distributed systems. Abstract. Declarative networking is a programming methodology that enables developers to concisely specify network protocols and services, and directly compile these specifications into a dataflow framework for execution. This paper describes recent advances in declarative networking, tracing its evolution from a rapid prototyping framework towards a platform that serves as an important bridge connecting formal theories for reasoning about protocol correctness and actual implementations. In particular, the paper focuses on the use of declarative networking for addressing four main challenges in the distributed systems development cycle: the generation of safe routing implementations, debugging, security and privacy, and optimizing distributed systems. Disciplines Computer Sciences

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Relational transducers for declarative networking

Cited by 39 publications

References 20 publications

Logic and lattices for distributed programming

Logic and lattices for distributed programming

Confluence Analysis for Distributed Programs: A Model-Theoretic Approach

Recent Advances in Declarative Networking

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