The 5G network is widely becoming available in many countries across the globe (including Italy), and is evolving as the new reference architecture for the global mobile and fixed telecommunications network. The Core Upgrade in future 5G cellular network is network slicing, which provides a flexible and efficient frame work for network organization and resource management. Mobile edge cloud (MEC) is a model for enabling on-demand interaction with a shared pool of re-configurable computing resources such as servers, storage, peer devices, applications, and execution of the various network services at the edge of the cellular network to allow the mobile subscribers benefit from a lower latency and network congestion. It overcomes some obstacles of traditional central clouds by offering wireless network information, low latency and bandwidth conservation. This paper presents a survey of MEC systems, including the concept, architectures, and technical enablers. First, the MEC applications are explored and classified based on different criteria ,then the service models and deployment are reviewed to categorize the factors influencing the MEC system design .Finally, the new approach are presented after analysing the technical issues of the architectures and designs of MEC systems from the existing solutions.Additionally the challenges and future developments of MEC are further discussed. The MEC additionally enables the compute intensive applications execution in the resource constraint devices with the collaborative computing that involves the cloud servers. This paper discuss the architectural description of the MEC platform along with the functionalities to enable the above discussed features. The relevant state-of-the-art research efforts are then surveyed. We have data from a mobile operator about the network latency in the back-hauling and in the core part of the network and we want to evaluate the optimal location where the Radio sites could be deployed to support for Mobile Edge Computing and determine optimal cost MEC to reduce cost for the Network Providers which enable them provide different real-time applications to the network subscribers. The task of this thesis work is to develop the cost effective model by equipping a new MEC node comprises a fixed part, equal for all the gateways, and a variable part proportional to the traffic that flows through that gateway,the Base5G models make it possible for an operator to obtain a rough estimate of the cost necessary to enable them provide Ultra Reliable Low Latency (URLL) services to its Subscribers.
La rete 5G sta diventando ampiamente disponibile in molti paesi del mondo (Italia compresa) e si sta evolvendo come nuova architettura di riferimento per la rete globale di telecomunicazioni mobili e fisse. Il principale aggiornamento nella futura rete cellulare 5G è lo slicing della rete, che fornisce un quadro flessibile ed efficiente per l’organizzazione della rete e la gestione delle risorse. Mobile edge cloud (MEC) è un modello per abilitare l’interazione on-demand con un pool condiviso di risorse informatiche riconfigurabili come server, storage, dispositivi peer, applicazioni ed eseguire vari servizi di rete all’edge della rete cellulare per consentire agli abbonati di essere mobili telefoni per beneficiare di una minore latenza e congestione della rete. Supera alcuni ostacoli dei cloud core tradizionali offrendo informazioni dettagliate sulla rete wireless, bassa latenza e conservazione della larghezza di banda. Questo documento presenta un’indagine sui sistemi MEC, inclusi concept, architetture e abilitanti tecnici. In primo luogo, le applicazioni MEC vengono esplorate e classificate secondo diversi criteri, quindi vengono esaminati i modelli di servizio e l’implementazione per classificare i fattori che influenzano la progettazione del sistema MEC. Infine, il nuovo approccio viene presentato dopo aver analizzato i problemi tecnici delle architetture MEC Systems e dei progetti dalle soluzioni esistenti. Inoltre, vengono ulteriormente discusse le sfide e gli sviluppi futuri del MEC. MEC consente inoltre l’esecuzione di applicazioni ad alta intensità di calcolo su dispositivi con risorse limitate con elaborazione collaborativa che coinvolge server cloud. Questo documento discute la descrizione dell’architettura della piattaforma MEC insieme alle funzionalità per abilitare le funzionalità discusse sopra. Vengono quindi esaminati i pertinenti sforzi di ricerca all’avanguardia. Disponiamo di dati di un operatore mobile sulla latenza di rete nel back-hauling e nel core della rete e vogliamo valutare la posizione ottimale in cui i siti radio potrebbero essere implementati per supportare il Mobile Edge Computing e determinare il costo MEC ottimale per ridurre i costi per provider di rete consentendo loro di fornire diverse applicazioni in tempo reale agli abbonati della rete. Il compito di questo lavoro di tesi è sviluppare il modello redditizio attrezzando un nuovo nodo MEC composto da una parte fissa, uguale per tutti i gateway, e da una parte variabile proporzionale al traffico che scorre attraverso quel gateway, i modelli Base5G lo rendono possibile. per un operatore di ottenere una stima approssimativa del costo per consentire loro di fornire servizi URLL (Ultra Reliable Low Latency) ai propri abbonati.
Cost effective model of mobile edge computing for the base 5G project
SAKTHIVEL, MANISH
2021/2022
Abstract
The 5G network is widely becoming available in many countries across the globe (including Italy), and is evolving as the new reference architecture for the global mobile and fixed telecommunications network. The Core Upgrade in future 5G cellular network is network slicing, which provides a flexible and efficient frame work for network organization and resource management. Mobile edge cloud (MEC) is a model for enabling on-demand interaction with a shared pool of re-configurable computing resources such as servers, storage, peer devices, applications, and execution of the various network services at the edge of the cellular network to allow the mobile subscribers benefit from a lower latency and network congestion. It overcomes some obstacles of traditional central clouds by offering wireless network information, low latency and bandwidth conservation. This paper presents a survey of MEC systems, including the concept, architectures, and technical enablers. First, the MEC applications are explored and classified based on different criteria ,then the service models and deployment are reviewed to categorize the factors influencing the MEC system design .Finally, the new approach are presented after analysing the technical issues of the architectures and designs of MEC systems from the existing solutions.Additionally the challenges and future developments of MEC are further discussed. The MEC additionally enables the compute intensive applications execution in the resource constraint devices with the collaborative computing that involves the cloud servers. This paper discuss the architectural description of the MEC platform along with the functionalities to enable the above discussed features. The relevant state-of-the-art research efforts are then surveyed. We have data from a mobile operator about the network latency in the back-hauling and in the core part of the network and we want to evaluate the optimal location where the Radio sites could be deployed to support for Mobile Edge Computing and determine optimal cost MEC to reduce cost for the Network Providers which enable them provide different real-time applications to the network subscribers. The task of this thesis work is to develop the cost effective model by equipping a new MEC node comprises a fixed part, equal for all the gateways, and a variable part proportional to the traffic that flows through that gateway,the Base5G models make it possible for an operator to obtain a rough estimate of the cost necessary to enable them provide Ultra Reliable Low Latency (URLL) services to its Subscribers.| File | Dimensione | Formato | |
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Descrizione: The File Describes the Cost Effective Model of Mobile Edge Computing for a Base 5G Project
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https://hdl.handle.net/10589/186478