scholarly journals Randomized Shortest Paths with Net Flows and Capacity Constraints

2020 ◽  
Author(s):  
Sylvain Courtain ◽  
Pierre Leleux ◽  
Ilkka Kivimäki ◽  
Guillaume Guex ◽  
Marco Saerens
Keyword(s):  
Shortest Paths ◽  
Capacity Constraints ◽  
Net Flows

scholarly journals Resilient Capacity-Aware Routing

2021 ◽  
pp. 411-429
Author(s):  
Stefan Schmid ◽  
Nicolas Schnepf ◽  
Jiří Srba
Keyword(s):  
Communication Networks ◽  
Search Algorithm ◽  
Shortest Paths ◽  
Capacity Constraints ◽  
High Availability ◽  
Worst Case ◽  
Shortest Path Routing ◽  
Link Failures ◽  
Algorithmic Question ◽  
Improved Performance

AbstractTo ensure a high availability, communication networks provide resilient routing mechanisms that quickly change routes upon failures. However, a fundamental algorithmic question underlying such mechanisms is hardly understood: how to verify whether a given network reroutes flows along feasible paths, without violating capacity constraints, for up to k link failures? We chart the algorithmic complexity landscape of resilient routing under link failures, considering shortest path routing based on link weights as e.g. deployed in the ECMP protocol. We study two models: a pessimistic model where flows interfere in a worst-case manner along equal-cost shortest paths, and an optimistic model where flows are routed in a best-case manner, and we present a complete picture of the algorithmic complexities. We further propose a strategic search algorithm that checks only the critical failure scenarios while still providing correctness guarantees. Our experimental evaluation on a benchmark of Internet and datacenter topologies confirms an improved performance of our strategic search by several orders of magnitude.

New York Flow Control with Deterministic En Route Capacity Constraints

2009 ◽  
Vol 17 (2) ◽  
pp. 149-172
Author(s):  
Shon Grabbe ◽  
Banavar Sridhar ◽  
Avijit Mukherjee
Keyword(s):  
New York ◽  
Flow Control ◽  
Capacity Constraints

eMap: A Web Application for Identifying and Visualizing Electron or Hole Hopping Pathways in Proteins

2019 ◽  
Author(s):  
Ruslan N. Tazhigulov ◽  
James R. Gayvert ◽  
Melissa Wei ◽  
Ksenia B. Bravaya
Keyword(s):  
Web Application ◽  
Shortest Paths ◽  
Coarse Grained ◽  
Decay Parameter ◽  
Electron Hole ◽  
Web Based ◽  
Aromatic Amino Acid Residue ◽  
Pathways Model ◽  
Visualization Tools ◽  
Effective Decay

<p>eMap is a web-based platform for identifying and visualizing electron or hole transfer pathways in proteins based on their crystal structures. The underlying model can be viewed as a coarse-grained version of the Pathways model, where each tunneling step between hopping sites represented by electron transfer active (ETA) moieties is described with one effective decay parameter that describes protein-mediated tunneling. ETA moieties include aromatic amino acid residue side chains and aromatic fragments of cofactors that are automatically detected, and, in addition, electron/hole residing sites that can be specified by the users. The software searches for the shortest paths connecting the user-specified electron/hole source to either all surface-exposed ETA residues or to the user-specified target. The identified pathways are ranked based on their length. The pathways are visualized in 2D as a graph, in which each node represents an ETA site, and in 3D using available protein visualization tools. Here, we present the capability and user interface of eMap 1.0, which is available at https://emap.bu.edu.</p>

From Assortative to Dissortative Networks: The Role of Capacity Constraints

2010 ◽  
Author(s):  
Michael Koenig ◽  
Claudio J. Tessone ◽  
Yves Zenou
Keyword(s):  
Capacity Constraints

Computer algorithms

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Data Structures ◽  
Analysis Of Algorithms ◽  
Shortest Paths ◽  
Minimum Cut ◽  
Network Data ◽  
Computer Algorithms ◽  
Breadth First Search ◽  
Augmenting Path ◽  
Basic Concepts ◽  
Maximum Flows

This chapter introduces some of the fundamental concepts of numerical network calculations. The chapter starts with a discussion of basic concepts of computational complexity and data structures for storing network data, then progresses to the description and analysis of algorithms for a range of network calculations: breadth-first search and its use for calculating shortest paths, shortest distances, components, closeness, and betweenness; Dijkstra's algorithm for shortest paths and distances on weighted networks; and the augmenting path algorithm for calculating maximum flows, minimum cut sets, and independent paths in networks.

scholarly journals Lower bounds for computing geometric spanners and approximate shortest paths

2001 ◽  
Vol 110 (2-3) ◽  
pp. 151-167 ◽  
Author(s):  
Danny Z. Chen ◽  
Gautam Das ◽  
Michiel Smid
Keyword(s):  
Lower Bounds ◽  
Shortest Paths ◽  
Geometric Spanners
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