The design and implementation of a declarative sensor network system

Chu, David; Popa, Lucian; Tavakoli, Arsalan; Hellerstein, Joseph M.; Levis, Philip; Shenker, Scott; Stoica, Ion

doi:10.1145/1322263.1322281

Cited by 115 publications

(104 citation statements)

References 37 publications

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“…We have developed substantial experience building significant distributed systems [2,3,8,20] using hybrid declarative/imperative languages such as Overlog [20]. While our experience with those languages was largely positive, the combination of Datalog and imperative constructs often clouded our understanding of the "correct" execution of single-node programs that performed state updates.…”

Section: Resultsmentioning

confidence: 99%

“…Significant recent work ( [2,5,8,20], etc.) has focused on applying deductive database languages extended with networking primitives to the problem of specifying and implementing network protocols and distributed systems.…”

Section: Distributed Systemsmentioning

confidence: 99%

“…In recent years, there has been a resurgence of interest in Datalog as the foundation for applied, domain-specific languages in a wide variety of areas, including networking [20], distributed systems [5,8], natural language processing [11], robotics [4], compiler analysis [14], security [13,17,32] and computer games [31]. The resulting languages have been promoted for their compact and natural representations of tasks in their respective domains, in many cases leading to code that is orders of magnitude shorter than equivalent imperative programs.…”

Section: Introductionmentioning

confidence: 99%

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Dedalus: Datalog in Time and Space

et al. 2011

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Recent research has explored using Datalog-based languages to express a distributed system as a set of logical invariants [2,19]. Two properties of distributed systems proved difficult to model in Datalog. First, the state of any such system evolves with its execution. Second, deductions in these systems may be arbitrarily delayed, dropped, or reordered by the unreliable network links they must traverse. Previous efforts addressed the former by extending Datalog to include updates, key constraints, persistence and events, and the latter by assuming ordered and reliable delivery while ignoring delay. These details have a semantics outside Datalog, which increases the complexity of the language or its interpretation, and forces programmers to think operationally. We argue that the missing component from these previous languages is a notion of time.In this paper we present Dedalus, a foundation language for programming and reasoning about distributed systems. Dedalus reduces to a subset of Datalog [30] with negation, aggregate functions, successor and choice, and admits an explicit representation of time into the logic language. We show that Dedalus provides a declarative foundation for the two signature features of distributed systems: mutable state, and asynchronous processing and communication. Given these two features, we address three important properties of programs in a domain-specific manner: a notion of safety appropriate to non-terminating computations, stratified monotonic reasoning with negation over time, and efficient evaluation over time via a simple execution strategy. We also provide conservative syntactic checks for our temporal notions of safety and stratification. Our experience implementing full-featured systems in variants of Datalog suggests that Dedalus is well-suited to the specification of rich distributed services and protocols, and provides both cleaner semantics and richer tests of correctness.

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Section: Resultsmentioning

confidence: 99%

Section: Distributed Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dedalus: Datalog in Time and Space

et al. 2011

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“…Significant recent work ( [4,9,14,32]) has focused on applying deductive database languages extended with networking primitives to the problem of specifying and implementing network protocols and distributed systems. Theorem 1 resembles the correctness proof of "pipelined semi-naive evaluation" for distributed Datalog presented by Loo et al [31].…”

Section: Related Workmentioning

confidence: 99%

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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“…Prior work on viewing the sensor network as a distributed database provides a declarative programming framework which is amenable to optimizations. However, it lacks expressive power, and the developed query engines (TinyDB [21], Cougar [5], SNLog [12]) for sensor networks implement only a limited functionality. On the other hand, more expressive frameworks such as Kairos [26] are based on procedural languages and hence, are difficult to translate to efficient distributed code.…”

Section: Introductionmentioning

confidence: 99%

Deductive Framework for Programming Sensor Networks

Gupta

Zhu

2009

2009 IEEE 25th International Conference on Data Engineering

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Abstract-Developing powerful paradigms for programming sensor networks is critical to realize the full potential of sensor networks as collaborative data processing engines. In this article, we motivate and develop a deductive framework for programming sensor networks, extending the prior vision of viewing sensor network as a distributed database. The deductive programming approach is declarative, very expressive, and amenable to automatic optimizations. Such a framework allows users to program sensor network applications at a high-level without worrying about the low-level tedious details. Our system translates a given deductive program to efficient distributed code that runs on individual nodes. To facilitate the above translation, we develop techniques for distributed and asynchronous evaluation of deductive programs in sensor networks. Our techniques generalize to recursive programs without negations, arbitrary nonrecursive programs with negations, and in general to arbitrary "locally non-recursive" programs with function symbols. We present performance results on TOSSIM, a network simulator, and a small network testbed.

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The design and implementation of a declarative sensor network system

Cited by 115 publications

References 37 publications

Dedalus: Datalog in Time and Space

Dedalus: Datalog in Time and Space

Confluence Analysis for Distributed Programs: A Model-Theoretic Approach

Deductive Framework for Programming Sensor Networks

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