A two-stage Multi-Criteria Optimization method for service placement in decentralized edge micro-clouds

Community networks are becoming increasingly popular due to the growing demand for network connectivity in both rural and urban areas. Community networks are owned and managed at the edge by volunteers. Their irregular topology, the heterogeneity of resources and their unreliable behavior claim for...

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Bibliographic Details
Published in:Future generation computer systems Vol. 121; pp. 90 - 105
Main Authors: Panadero, Javier, Selimi, Mennan, Calvet, Laura, Marquès, Joan Manuel, Freitag, Felix
Format: Journal Article
Language:English
Published: Elsevier B.V 01.08.2021
Subjects:
Community networks
Distributed systems
Micro-clouds
Multi-objective optimization algorithms
Service placement
Micro-clouds
Distributed systems
Service placement
Multi-objective optimization algorithms
Community networks
ISSN:0167-739X, 1872-7115
Online Access:Get full text
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Summary:Community networks are becoming increasingly popular due to the growing demand for network connectivity in both rural and urban areas. Community networks are owned and managed at the edge by volunteers. Their irregular topology, the heterogeneity of resources and their unreliable behavior claim for advanced optimization methods to place services in the network. In particular, an efficient service placement method is key for the performance of these systems. This work presents the Multi-Criteria Optimal Placement method, a novel and fast two-stage multi-objective method to place services in decentralized community network edge micro-clouds. A comprehensive set of computational experiments is carried out using real traces of Guifi.net, which is the largest production community network worldwide. According to the results, the proposed method outperforms both the random placement method used currently in Guifi.net and the Bandwidth-aware Service Placement method, which provides the best known solutions in the literature, by a mean gap in bandwidth gain of about 53% and 10%, respectively, while it also reduces the number of resources used. •A methodology aware of the irregular topology and the heterogeneity of CN’s is key.•Our method outperforms the placement method used currently in Guifi.net.•Our method improves the best known solutions in the literature.
ISSN:0167-739X
1872-7115
DOI:10.1016/j.future.2021.03.013