Distributed 2-Approximation Algorithm for the Semi-matching Problem

Czygrinow, Andrzej; Hańćkowiak, Michał; Szymańska, Edyta; Wawrzyniak, Wojciech

doi:10.1007/978-3-642-33651-5_15

Cited by 22 publications

(26 citation statements)

References 10 publications

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“…However, if we are happy with a near-optimal assignment (a constant-factor approximation of the optimum), it turns out that task becomes local; see Czygrinow et al [2] for a distributed algorithm that solves precisely this task. (Concretely, the running of their algorithm depends on the maximum degree of the bipartite graph G, i.e., the maximum number of links per customer and links per point-of-presence.…”

Section: Link Assignmentmentioning

confidence: 99%

Exploiting locality in distributed SDN control

Schmid

Suomela

2013

Proceedings of the Second ACM SIGCOMM Workshop on Hot Topics in Software Defined Networking

171

131

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Large SDN networks will be partitioned in multiple controller domains; each controller is responsible for one domain, and the controllers of adjacent domains may need to communicate to enforce global policies. This paper studies the implications of the local network view of the controllers. In particular, we establish a connection to the field of local algorithms and distributed computing, and discuss lessons for the design of a distributed control plane. We show that existing local algorithms can be used to develop efficient coordination protocols in which each controller only needs to respond to events that take place in its local neighborhood. However, while existing algorithms can be used, SDN networks also suggest a new approach to the study of locality in distributed computing. We introduce the so-called supported locality model of distributed computing. The new model is more expressive than the classical models that are commonly used in the design and analysis of distributed algorithms, and it is a better match with the features of SDN networks.

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Section: Link Assignmentmentioning

confidence: 99%

Exploiting locality in distributed SDN control

Schmid

Suomela

2013

Proceedings of the Second ACM SIGCOMM Workshop on Hot Topics in Software Defined Networking

171

131

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“…The approximation ratio is min (2, 2s+n n ), where s is the number of servers and n is the number of all nodes. In [7] they also provided a constant approximation local algorithm that runs in O(min{∆ 2 , ∆ log 4 n}) rounds.…”

Section: Related Workmentioning

confidence: 98%

“…A 2-approximation of MaxCov with k = 1 and uniform file sizes can be computed using the matching algorithm of [22]. Regarding MinLoad, we are only aware of distributed algorithms which use large messages (i.e., they run in the local model [28]): in [7] it is shown how to find an approximation of optimal semi-matchings in O(∆ 5 ) rounds under the L2 norm. The approximation ratio is min (2, 2s+n n ), where s is the number of servers and n is the number of all nodes.…”

Section: Related Workmentioning

confidence: 99%

Distributed Backup Placement in Networks

Halldórsson

Köhler

Patt-Shamir

et al. 2015

Proceedings of the 27th ACM Symposium on Parallelism in Algorithms and Architectures

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We consider the backup placement problem, defined as follows. Some nodes (processors) in a given network have objects (e.g., files, tasks) whose backups should be stored in additional nodes for increased fault resilience. To minimize the disturbance in case of a failure, it is required that a backup copy should be located at a neighbor of the primary node. The goal is to find an assignment of backup copies to nodes which minimizes the maximum load (number or total size of copies) over all nodes in the network. It is known that a natural selfish local improvement policy has approximation ratio Ω(log n/ log log n); we show that it may take this policy Ω( √ n) time to reach equilibrium in the distributed setting. Our main result in this paper is a distributed algorithm which finds a placement in polylogarithmic time and achieves approximation ratio O( log n log log n ). We obtain this result using a distributed approximation algorithm for f -matching in bipartite graphs that may be of independent interest.

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“…Czygrinow et al [8] presented a distributed algorithm for finding a locally optimal semimatching in time poly(∆); this also implies a factor-2 approximation of globally optimal semi-matchings.…”

Section: Related Workmentioning

confidence: 99%

Locally Optimal Load Balancing

Feuilloley

Hirvonen

Suomela

2015

Lecture Notes in Computer Science

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Abstract. This work studies distributed algorithms for locally optimal load-balancing: We are given a graph of maximum degree ∆, and each node has up to L units of load. The task is to distribute the load more evenly so that the loads of adjacent nodes differ by at most 1.If the graph is a path (∆ = 2), it is easy to solve the fractional version of the problem in O(L) communication rounds, independently of the number of nodes. We show that this is tight, and we show that it is possible to solve also the discrete version of the problem in O(L) rounds in paths.For the general case (∆ > 2), we show that fractional load balancing can be solved in poly(L, ∆) rounds and discrete load balancing in f (L, ∆) rounds for some function f , independently of the number of nodes.

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Distributed 2-Approximation Algorithm for the Semi-matching Problem

Cited by 22 publications

References 10 publications

Exploiting locality in distributed SDN control

Exploiting locality in distributed SDN control

Distributed Backup Placement in Networks

Locally Optimal Load Balancing

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