Self-Adjusting Linear Networks with Ladder Demand Graph

07/08/2022
by   Anton Paramonov, et al.
0

This paper revisits the problem of designing online algorithms for self-adjusting linear networks which dynamically adapt to the traffic pattern they serve. We refer to the graph formed by the pairwise communication requests as the demand graph. Even though the line is a fundamental network topology, existing results only study linear demand graphs. In this work, we take a first step toward studying more general demand graphs. We present a self-adjusting algorithm that responds to the traffic pattern drawn from the n-ladder graph resulting algorithm appears to have an optimal competitive ratio. As two additional side results, we get a generic algorithm for an arbitrary demand graph and an optimal algorithm for a cycle demand graph.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/05/2019

ReNets: Toward Statically Optimal Self-Adjusting Networks

This paper studies the design of self-adjusting networks whose topology ...
research
05/07/2019

Self-Adjusting Linear Networks

Emerging networked systems become increasingly flexible and reconfigurab...
research
12/26/2020

A second-order self-adjusting steepness based remapping method for arbitrary quadrilateral meshes

In this paper, based on the idea of self-adjusting steepness based schem...
research
06/19/2020

An Online Matching Model for Self-Adjusting ToR-to-ToR Networks

This is a short note that formally presents the matching model for the t...
research
09/30/2021

Self-Adjusting Packet Classification

This paper is motivated by the vision of more efficient packet classific...
research
04/09/2021

Detecting outlying demand in multi-leg bookings for transportation networks

Network effects complicate demand forecasting in general, and outlier de...
research
04/20/2023

Polylog-Competitive Algorithms for Dynamic Balanced Graph Partitioning for Ring Demands

The performance of many large-scale and data-intensive distributed syste...

Please sign up or login with your details

Forgot password? Click here to reset