SearcharxivSearch

arXiv · 1601.04213

Partial-Match Queries with Random Wildcards: In Tries and Distributed Hash Tables

Abstract

Consider an $m$-bit query $q$ to a bitwise trie $T$. A wildcard $*$ is an unspecified bit in $q$ for which the query asks the membership for both cases $*=0$ and $*=1$. It is common that such partial-match queries with wildcards are issued in tries. With uniformly random occurrences of $w$ wildcards in $q$ assumed, the obvious upper bound on the average number of traversal steps in $T$ is $2^w m$. We show that the average does not exceed \[ \frac{m+1}{w+1} \left( 2^{w+2} - 2 w - 4 \right) + m = O \left( \frac{2^w m}{w} \right), \] and equals the value exactly when $T$ includes all the $m$-bit keys as the worst case. Here the query $q$ performs with the naive backtracking algorithm in $T$. It is similarly shown that the average is $O \left( \frac{k^w m}{w} \right)$ in a general trie of maximum out-degree $k$. Our analysis for tries is extended to a distributed hash table (DHT), which is among the most frequently used decentralized data structures in networking. We show, under a natural probabilistic assumption for the largest class of DHTs, that the average number of hops required by an $m$-bit query $q$ to a DHT $D$ with random $w$ wildcards meets the same asymptotic bound. As a result, $q$ is answered with average $O \left( \frac{2^w m}{w} \right)$ hops rather than $\Theta \left( 2^w m \right)$ in the four major DHTs Chord, Pastry, Tapestry and Kademlia. In addition, with a uniform key distribution for sufficiently many entries, we prove that a lookup request to the DHT Chord is answered correctly with $O(m)$ hops and probability $1 - 2^{-\Omega (m)}$. To the author's knowledge, the probability $1 - 2^{-\Omega (m)}$ of correct lookup in Chord has not been identified so far.

Explore related subjects

Keep this discovery

BibTeXRIS

Junichiro Fukuyama. 2016-01-16. Partial-Match Queries with Random Wildcards: In Tries and Distributed Hash Tables. https://arxiv.org/abs/1601.04213

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Quasi-Monte Carlo Beyond Hardy-Krause II: $(1 + \varepsilon)n$ Samples Suffice

Numerical integration studies how well one can estimate the integral of a function $f$ over $[0,1)^d$ using $n$ sample points. The two classical methods, Monte Carlo (MC) and quasi-Monte Carlo (QMC), have complementary strengths and weaknesses, and a fundamental question is to design an approach that combines the benefits of both. Recently, building on the transference principle in discrepancy theory, Bansal and Jiang~\cite{BJ25a} gave a randomized QMC method that bridges MC and QMC guarantees using only i.i.d.\ samples. Their method also goes beyond the classical Koksma--Hlawka inequality: it achieves integration error $\widetilde{O}_d(\sigma_{\mathsf{SO}}(f)/n)$, where the smoothed-out variation $\sigma_{\mathsf{SO}}(f)$ can be substantially smaller than the Hardy--Krause variation that governs the classical bound. However, their algorithm requires $n^2$ i.i.d.\ samples as input, and this quadratic blowup is inherent to any method based on the transference principle. In this work, we bypass the quadratic blowup: for any constant $\varepsilon > 0$, we show that $(1+\varepsilon)n$ i.i.d.\ samples suffice to both obtain the beyond-Hardy--Krause guarantee of~\cite{BJ25a}, resolving an open problem posed there, and to produce low-discrepancy point sequences. Our algorithms are variants of the online Haar-thinning method of Dwivedi, Feldheim, Gurel-Gurevich, and Ramdas~\cite{DFG+19}.

cs.DS

Single-Exponential Algorithms and a Polynomial Kernel for Strong Connectivity Augmentation

Strong Connectivity Augmentation (SCA) asks whether a directed acyclic graph can be made strongly connected by adding at most $k$ prescribed links whose total weight is within a given budget. Klinkby, Misra, and Saurabh (SODA 2021) gave an $O^*(2^{O(k\log k)})$-time algorithm and asked whether the problem admits a single-exponential parameterized algorithm and a polynomial kernel. We answer both questions affirmatively: SCA can be solved in $O^*(9^k)$ time and admits a polynomial kernel with $O(k^4)$ vertices and $O(k^{16})$ bits. For unweighted SCA, we obtain $O^*(4^k)$ time and a kernel with $O(k^3)$ vertices. Our algorithms are based on a particularly simple reduction to Strongly Connected Spanning Subgraph with two edge costs.

cs.DS