Station Assignment with Reallocation

03/04/2018
by   Austin Halper, et al.
0

We study a dynamic allocation problem that arises in various scenarios where mobile clients joining and leaving the system have to communicate with static stations via radio transmissions. Restrictions are a maximum delay, or laxity, between consecutive client transmissions and a maximum bandwidth that a station can share among its clients. We study the problem of assigning clients to stations so that every client transmits to some station, satisfying those restrictions. We consider reallocation algorithms, where clients are revealed at its arrival time, the departure time is unknown until they leave, and clients may be reallocated to another station, but at a cost proportional to the reciprocal of the client laxity. We present negative results for previous related protocols that motivate the study; we introduce new protocols that expound trade-offs between station usage and reallocation cost; we determine experimentally a classification of the clients attempting to balance those opposite goals; we prove theoretically bounds on our performance metrics; and we show through simulations that, for realistic scenarios, our protocols behave much better than our theoretical guarantees.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/25/2021

Measuring Financial Advice: aligning client elicited and revealed risk

Financial advisors use questionnaires and discussions with clients to de...
research
05/18/2020

Joint Index Coding and Incentive Design for Selfish Clients

The index coding problem includes a server, a group of clients, and a se...
research
03/07/2022

Dynamic Pricing for Client Recruitment in Federated Learning

Though federated learning (FL) well preserves clients' data privacy, man...
research
06/29/2018

Fixed-parameter algorithms for facility location under matroid constraints

We introduce a new uncapacitated discrete facility location problem, whe...
research
02/27/2023

Communication Trade-offs in Federated Learning of Spiking Neural Networks

Spiking Neural Networks (SNNs) are biologically inspired alternatives to...
research
09/21/2018

Privacy in Index Coding: k-Limited-Access Schemes

In the traditional index coding problem, a server employs coding to send...
research
04/11/2020

Submodular Clustering in Low Dimensions

We study a clustering problem where the goal is to maximize the coverage...

Please sign up or login with your details

Forgot password? Click here to reset