Prescriptive Analytics for MEC Orchestration

Abstract : Orchestrating network and computing resources in Mobile Edge Computing (MEC) is an important item in the networking research agenda. In this paper, we propose a novel algorithmic approach to solve the problem of dynamically assigning base stations to MEC facilities, while taking into consideration multiple time-periods, and computing load switching and access latency costs. In particular, leveraging on an existing state of the art on mobile data analytics, we propose a methodology to integrate arbitrary time-period aggregation methods into a network optimization framework. We notably apply simple consecutive time period aggregation and agglomerative hierarchical clustering. Even if the aggregation and optimization methods represent techniques which are different in nature, and whose aim is partially overlapping, we show that they can be integrated in an efficient way. By simulation on real mobile cellular datasets, we show that, thanks to the clustering, we can scale with the number of time-periods considered, that our approach largely outperforms the case without time-period aggregations in terms of MEC access latency, and at which extent the use of clustering and time aggregation affects computing time and solution quality.
Type de document :
Communication dans un congrès
IFIP Networking 2018, May 2018, Zurich, Switzerland
Liste complète des métadonnées

Littérature citée [37 références]  Voir  Masquer  Télécharger
Contributeur : Stefano Secci <>
Soumis le : jeudi 22 mars 2018 - 13:59:03
Dernière modification le : mardi 17 avril 2018 - 16:03:23


Fichiers produits par l'(les) auteur(s)


  • HAL Id : hal-01740816, version 1



Alberto Ceselli, Marco Fiore, Angelo Furno, Marco Premoli, Stefano Secci, et al.. Prescriptive Analytics for MEC Orchestration. IFIP Networking 2018, May 2018, Zurich, Switzerland. 〈hal-01740816〉



Consultations de la notice


Téléchargements de fichiers