A Topological Perspective on Distributed Network Algorithms

Structural Information and Communication Complexity - Lecture Notes in Computer Science ◽

10.1007/978-3-030-24922-9_1 ◽

2019 ◽

pp. 3-18 ◽

Cited By ~ 2

Author(s):

Armando Castañeda ◽

Pierre Fraigniaud ◽

Ami Paz ◽

Sergio Rajsbaum ◽

Matthieu Roy ◽

...

Keyword(s):

Distributed Network ◽

Network Algorithms

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Specification of Synchronous Network Flooding in Temporal Logic

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/6/5 ◽

2020 ◽

Vol 17 (6) ◽

pp. 867-874

Author(s):

Ra’ed Bani Abdelrahman ◽

Rafat Alshorman ◽

Walter Hussak ◽

Amitabh Trehan

Keyword(s):

Temporal Logic ◽

Network Topology ◽

Distributed Network ◽

Model Checker ◽

Fixed Size ◽

Network Algorithms ◽

Worked Example ◽

Synchronous Network ◽

Termination Problem ◽

Network Flooding

In distributed network algorithms, network flooding algorithm is considered one of the simplest and most fundamental algorithms. This research specifies the basic synchronous memory-less network flooding algorithm where nodes on the network don’t have memory, for any fixed size of network, in Linear Temporal Logic. The specification can be customized to any single network topology or class of topologies. A specification of the termination problem is formulated and used to compare different topologies for earlier termination. This research gives a worked example of one topology resulting in earlier termination than another, for which we perform a formal verification using the model checker NuSMV

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Input-Dynamic Distributed Algorithms for Communication Networks

Proceedings of the ACM on Measurement and Analysis of Computing Systems ◽

10.1145/3447384 ◽

2021 ◽

Vol 5 (1) ◽

pp. 1-33

Author(s):

Klaus-Tycho Foerster ◽

Janne H. Korhonen ◽

Ami Paz ◽

Joel Rybicki ◽

Stefan Schmid

Keyword(s):

Communication Network ◽

Communication Networks ◽

Minimum Spanning Tree ◽

Upper Bounds ◽

Distributed Network ◽

Model Design ◽

Edge Label ◽

Network Algorithms ◽

General Picture ◽

Labeled Graph

Consider a distributed task where the communication network is fixed but the local inputs given to the nodes of the distributed system may change over time. In this work, we explore the following question: if some of the local inputs change, can an existing solution be updated efficiently, in a dynamic and distributed manner? To address this question, we define the batch dynamic \congest model in which we are given a bandwidth-limited communication network and a dynamic edge labelling defines the problem input. The task is to maintain a solution to a graph problem on the labeled graph under batch changes. We investigate, when a batch of α edge label changes arrive, \beginitemize \item how much time as a function of α we need to update an existing solution, and \item how much information the nodes have to keep in local memory between batches in order to update the solution quickly. \enditemize Our work lays the foundations for the theory of input-dynamic distributed network algorithms. We give a general picture of the complexity landscape in this model, design both universal algorithms and algorithms for concrete problems, and present a general framework for lower bounds. In particular, we derive non-trivial upper bounds for two selected, contrasting problems: maintaining a minimum spanning tree and detecting cliques.

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