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DEX: Self-Healing Expanders

DEX: self-healing expanders
Authors: Gopal Pandurangan; Peter Robinson 0002; Amitabh Trehan;

DEX: Self-Healing Expanders

Abstract

We present a fully-distributed self-healing algorithm DEX, that maintains a constant degree expander network in a dynamic setting. To the best of our knowledge, our algorithm provides the first efficient distributed construction of expanders --- whose expansion properties hold {\em deterministically} --- that works even under an all-powerful adaptive adversary that controls the dynamic changes to the network (the adversary has unlimited computational power and knowledge of the entire network state, can decide which nodes join and leave and at what time, and knows the past random choices made by the algorithm). Previous distributed expander constructions typically provide only {\em probabilistic} guarantees on the network expansion which {\em rapidly degrade} in a dynamic setting; in particular, the expansion properties can degrade even more rapidly under {\em adversarial} insertions and deletions. Our algorithm provides efficient maintenance and incurs a low overhead per insertion/deletion by an adaptive adversary: only $O(\log n)$ rounds and $O(\log n)$ messages are needed with high probability ($n$ is the number of nodes currently in the network). The algorithm requires only a constant number of topology changes. Moreover, our algorithm allows for an efficient implementation and maintenance of a distributed hash table (DHT) on top of DEX, with only a constant additional overhead. Our results are a step towards implementing efficient self-healing networks that have \emph{guaranteed} properties (constant bounded degree and expansion) despite dynamic changes.

Countries
United Kingdom, United Kingdom
Keywords

FOS: Computer and information sciences, /dk/atira/pure/subjectarea/asjc/1700/1703; name=Computational Theory and Mathematics, Computer Networks and Communications, C.2.4; E.1; G.2.2, Self-healing, G.2.2, Theoretical Computer Science, 68W15, CONGEST Model, Computer Science - Data Structures and Algorithms, C.2.4, Data Structures and Algorithms (cs.DS), Deterministic Expander, /dk/atira/pure/subjectarea/asjc/1700/1705, Distributed algorithm, /dk/atira/pure/subjectarea/asjc/1700/1708, /dk/atira/pure/subjectarea/asjc/1700/1705; name=Computer Networks and Communications, name=Computer Networks and Communications, /dk/atira/pure/subjectarea/asjc/2600/2614; name=Theoretical Computer Science, name=Theoretical Computer Science, name=Computational Theory and Mathematics, 004, Computational Theory and Mathematics, Computer Science - Distributed, Parallel, and Cluster Computing, /dk/atira/pure/subjectarea/asjc/1700/1708; name=Hardware and Architecture, Hardware and Architecture, Graph theory (including graph drawing) in computer science, name=Hardware and Architecture, /dk/atira/pure/subjectarea/asjc/2600/2614, Distributed algorithms, Distributed, Parallel, and Cluster Computing (cs.DC), E.1, /dk/atira/pure/subjectarea/asjc/1700/1703

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
14
Top 10%
Top 10%
Top 10%
Green
hybrid