The 5G network is vastly becoming available in many countries, including Italy, and is emerging as the new reference architecture for the global mobile and fixed telecommunication network. 5G is not only an evolution of 4G in terms of performance, but it also creates a breaking point with respect to previous generations: 5G will support a number of diversified vertical sectors, targeting different types of users and services. This thesis work is based on the reference architecture of the Base5G project for techno-economic analysis. Its aim is to experiment how 5G can help value IoT applications, while exploring the new technology called “Network slicing”. It then presents Base5G mathematical models for identifying different trade-offs in the deployment of MEC in a real operator network. Mobile Edge Computing (MEC) is a network architecture concept that enables the execution of the various network services at the edge of the cellular network allowing the mobile users to benefit from a lower latency and network congestion since the services are delivered from a geographical area that is very close to the user location. To be effective, MEC requires that mobile operators open their Radio Access Network (RAN) to authorized service operators and content providers. The task of this thesis work is to analyse and determine the appropriate positions to activate MEC for optimal coverage of several base station sites. We have data from a mobile operator about the network latency in the backhauling and in the core part of the network and want to evaluate the cost in terms of the number of MEC-enabled sites in order to provide different real-time applications. By leveraging delay measurements collected on field, the Base5G models make it possible for an operator to obtain a rough estimate of the cost necessary to enable Ultra Reliable Low Latency (URLL) services for its customers. These models also make it possible to identify the optimal locations of the mobile radio sites that should be upgraded to support MEC in order to maximize the speed of deployment of URLL services. Numerical results applied to measurements collected show that, without MEC, URLL services can be deployed only to a small part of the network subscribers. On the other hand, if MEC could be enabled in all the candidate sites, then URLL services could be offered to almost all the network subscribers.
La rete 5G sta diventando ampiamente disponibile in molti paesi, Italia compresa, e si sta affermando come la nuova architettura di riferimento per la rete globale di telecomunicazioni mobili e fisse. Il 5G non è solo un'evoluzione del 4G in termini di prestazioni, ma crea anche un punto di rottura rispetto alle generazioni precedenti: il 5G supporterà una serie di settori verticali diversificati, rivolgendosi a diverse tipologie di utenti e servizi. Questo lavoro di tesi si basa sull'architettura di riferimento del progetto Base5G per l'analisi tecnico-economica. Il suo obiettivo è sperimentare come il 5G può aiutare a valorizzare le applicazioni IoT, esplorando la nuova tecnologia chiamata "Network slicing". Presenta quindi modelli matematici Base5G per identificare diversi compromessi nell'implementazione di MEC in una rete di operatori reali. Mobile Edge Computing (MEC) è un concetto di architettura di rete che consente l'esecuzione dei vari servizi di rete ai margini della rete cellulare consentendo agli utenti mobili di beneficiare di una minore latenza e congestione della rete poiché i servizi sono forniti da un'area geografica che è molto vicino alla posizione dell'utente. Per essere efficace, MEC richiede che gli operatori mobili aprano la loro rete di accesso radio (RAN) agli operatori di servizi autorizzati e ai fornitori di contenuti. Il compito di questo lavoro di tesi è analizzare e determinare le posizioni appropriate per attivare MEC per una copertura ottimale di diversi siti di stazioni base. Abbiamo dati da un operatore mobile sulla latenza della rete nel backhauling e nella parte centrale della rete e vogliamo valutare il costo in termini di numero di siti abilitati MEC per fornire diverse applicazioni in tempo reale. Sfruttando le misurazioni del ritardo raccolte sul campo, i modelli Base5G consentono a un operatore di ottenere una stima approssimativa del costo necessario per abilitare i servizi URLL (Ultra Reliable Low Latency) per i propri clienti. Questi modelli consentono inoltre di identificare le posizioni ottimali dei siti radio mobili che dovrebbero essere aggiornati per supportare MEC al fine di massimizzare la velocità di implementazione dei servizi URLL. I risultati numerici applicati alle misurazioni raccolte mostrano che, senza MEC, i servizi URLL possono essere distribuiti solo a una piccola parte degli abbonati alla rete. Se invece MEC fosse abilitato in tutti i siti candidati, i servizi URLL potrebbero essere offerti a quasi tutti gli abbonati alla rete.
Techno-economic analysis of mobile edge computing for base 5G project
AHANONU, CHINWENDU JANE
2020/2021
Abstract
The 5G network is vastly becoming available in many countries, including Italy, and is emerging as the new reference architecture for the global mobile and fixed telecommunication network. 5G is not only an evolution of 4G in terms of performance, but it also creates a breaking point with respect to previous generations: 5G will support a number of diversified vertical sectors, targeting different types of users and services. This thesis work is based on the reference architecture of the Base5G project for techno-economic analysis. Its aim is to experiment how 5G can help value IoT applications, while exploring the new technology called “Network slicing”. It then presents Base5G mathematical models for identifying different trade-offs in the deployment of MEC in a real operator network. Mobile Edge Computing (MEC) is a network architecture concept that enables the execution of the various network services at the edge of the cellular network allowing the mobile users to benefit from a lower latency and network congestion since the services are delivered from a geographical area that is very close to the user location. To be effective, MEC requires that mobile operators open their Radio Access Network (RAN) to authorized service operators and content providers. The task of this thesis work is to analyse and determine the appropriate positions to activate MEC for optimal coverage of several base station sites. We have data from a mobile operator about the network latency in the backhauling and in the core part of the network and want to evaluate the cost in terms of the number of MEC-enabled sites in order to provide different real-time applications. By leveraging delay measurements collected on field, the Base5G models make it possible for an operator to obtain a rough estimate of the cost necessary to enable Ultra Reliable Low Latency (URLL) services for its customers. These models also make it possible to identify the optimal locations of the mobile radio sites that should be upgraded to support MEC in order to maximize the speed of deployment of URLL services. Numerical results applied to measurements collected show that, without MEC, URLL services can be deployed only to a small part of the network subscribers. On the other hand, if MEC could be enabled in all the candidate sites, then URLL services could be offered to almost all the network subscribers.| File | Dimensione | Formato | |
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Descrizione: Techno-Economic Analysis of Mobile Edge Computing for the Base 5G Project
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https://hdl.handle.net/10589/178033