Gossip based asynchronous and randomized distributed task assignment with guaranteed performance on heterogeneous networks

The main contribution of this paper is a novel distributed algorithm based on asynchronous and randomized local interactions, i.e., gossip based, for task assignment on heterogeneous networks. We consider a set of tasks with heterogeneous cost to be assigned to a set of nodes with heterogeneous exec...

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Bibliographic Details
Published in:Nonlinear analysis. Hybrid systems Vol. 26; pp. 292 - 306
Main Authors: Franceschelli, Mauro, Giua, Alessandro, Seatzu, Carla
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.11.2017
Elsevier
Subjects:
Distributed optimization
Distributed task assignment
Engineering Sciences
Gossip algorithms
Multi-agent systems
Quantized consensus
Distributed optimization
Quantized consensus
Gossip algorithms
Distributed task assignment
Multi-agent systems
ISSN:1751-570X
Online Access:Get full text
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Summary:The main contribution of this paper is a novel distributed algorithm based on asynchronous and randomized local interactions, i.e., gossip based, for task assignment on heterogeneous networks. We consider a set of tasks with heterogeneous cost to be assigned to a set of nodes with heterogeneous execution speed and interconnected by a network with unknown topology represented by an undirected graph. Our objective is to minimize the execution time of the set of tasks by the networked system. We propose a local interaction rule which allows the nodes of a network to cooperatively assign tasks among themselves with a guaranteed performance with respect to the optimal assignment exploiting a gossip based randomized interaction scheme. We first characterize the convergence properties of the proposed approach, then we propose an edge selection process and a distributed embedded stop criterion to terminate communications, not only task exchanges, while keeping the performance guarantee. Numerical simulations are finally presented to corroborate the theoretical results.
ISSN:1751-570X
DOI:10.1016/j.nahs.2017.06.008