Distributed approximation of k-service assignment

Halldórsson, Magnús M.; Köhler, Sven; Rawitz, Dror

doi:10.1007/s00446-017-0321-3

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…They give a randomized CONGEST algorithm that provides an approximation ratio of (log /log log ) in polylog( ) rounds. In a similar vein, Halldórsson et al [16] gave randomized polylog( )-time approximation algorithms for the -server assignment problem, where servers also have maximum capacity, and the objective is to maximize the total profit of satisfied clients subject to the server capacities.…”

Section: Distributed Load Balancingmentioning

confidence: 99%

Efficient Load-Balancing through Distributed Token Dropping

Brandt

Keller

Rybicki

et al. 2021

Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures

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We introduce a new graph problem, the token dropping game, and we show how to solve it efficiently in a distributed setting. We use the token dropping game as a tool to design an efficient distributed algorithm for stable orientations and more generally for locally optimal semi-matchings. The prior work by Czygrinow et al. (DISC 2012) finds a stable orientation in (Δ 5 ) rounds in graphs of maximum degree Δ, while we improve it to (Δ 4 ) and also prove a lower bound of Ω(Δ). For the more general problem of locally optimal semi-matchings, the prior upper bound is ( 5 ) and our new algorithm runs in ( • 4 ) rounds, which is an improvement for = ( ); here and are the maximum degrees of customers and servers, respectively.

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Section: Distributed Load Balancingmentioning

confidence: 99%

Efficient Load-Balancing through Distributed Token Dropping

Brandt

Keller

Rybicki

et al. 2021

Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures

View full text Add to dashboard Cite

show abstract

“…Previous techniques for distributed backup placement and related problems were based on selfish improvement policies, generalizations of Maximal Matching, and Maximal Packing [13,14]. In contrast, in this paper we employ an entirely different approach.…”

Section: Our Upper-bound Techniquesmentioning

confidence: 99%

Fast Distributed Backup Placement in Sparse and Dense Networks

Barenboim¹,

Oren²

2020

Symposium on Algorithmic Principles of Computer Systems

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We consider the Backup Placement problem in networks in the CON GEST distributed setting. Given a network graph G = (V, E), the goal of each vertex v ∈ V is selecting a neighbor, such that the maximum number of vertices in V that select the same vertex is minimized. The backup placement problem was introduced by Halldorsson, Kohler, Patt-Shamir, and Rawitz [12], who obtained an O(log n/ log log n) approximation with randomized polylogarithmic time. Their algorithm remained the state-of-the-art for general graphs, as well as specific graph topologies. In this paper we obtain significantly improved algorithms for various graph topologies. Specifically, we show that O(1)-approximation to optimal backup placement can be computed deterministically in O(1) rounds in graphs that model wireless networks, certain social networks, claw-free graphs, and more generally, in any graph with neighborhood independence bounded by a constant. At the other end, we consider sparse graphs, such as trees, forests, planar graphs and graphs of constant arboricity, and obtain a constant approximation to optimal backup placement in O(log n) deterministic rounds.Clearly, our constant-time algorithms for graphs with constant neighborhood independence are asymptotically optimal. Moreover, we show that our algorithms for sparse graphs are not far from optimal as well, by proving several lower bounds. Specifically, optimal backup placement of unoriented trees requires Ω(log n) time, and approximate backup placement with a polylogarithmic approximation factor requires Ω( log n/ log log n) time. Our results extend the knowledge regarding the question of "what can be computed locally?" [20], and reveal surprising gaps between complexities of distributed symmetry breaking problems.

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Fast Distributed Backup Placement in Sparse and Dense Networks

Barenboim¹,

Oren²

2019

Preprint

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Distributed approximation of k-service assignment

Cited by 3 publications

References 28 publications

Efficient Load-Balancing through Distributed Token Dropping

Efficient Load-Balancing through Distributed Token Dropping

Fast Distributed Backup Placement in Sparse and Dense Networks

Fast Distributed Backup Placement in Sparse and Dense Networks

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