Detecting Locally Distributed Predicates

  • Authors:
  • Michael De Rosa;Seth Copen Goldstein;Peter Lee;Jason Campbell;Padmanabhan S. Pillai

  • Affiliations:
  • Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Intel Labs, Pittsburgh;Intel Labs, Pittsburgh

  • Venue:
  • ACM Transactions on Autonomous and Adaptive Systems (TAAS)
  • Year:
  • 2011

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Abstract

In this article, we formalize locally distributed predicates, a concept previously introduced to address specific challenges associated with modular robotics and distributed debugging. A locally distributed predicate (LDP) is a novel construction for representing and detecting distributed properties in sparse-topology systems. Our previous work on LDPs presented empirical validation; here we show a formal model for two variants of the LDP algorithm, LDP-Basic and LDP-Snapshot, and establish performance bounds for these variants. We prove that LDP-Basic can detect strong stable predicates, that LDP-Snapshot can detect all stable predicates, and discuss their applicability to various distributed programming domains and to spatial computing in general. LDP detection in bounded-degree networks is shown to be scale-free, making the approach particularly attractive for specific topologies, even though LDPs are less efficient than snapshot algorithms in general distributed systems.