Tesi etd-10262020-152558 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
SILEO, CHRISTIAN
URN
etd-10262020-152558
Titolo
SDN-aided Companion Fog Computing: towards seamless container migration in a Fog Computing platform with the ONOS controller
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
COMPUTER ENGINEERING
Relatori
relatore Prof. Mingozzi, Enzo
relatore Prof. Virdis, Antonio
relatore Prof. Puliafito, Carlo
relatore Prof. Virdis, Antonio
relatore Prof. Puliafito, Carlo
Parole chiave
- fog-computing
- mobility-support
- onos
- sdn
- seamless-container-migration
- software-defined-networking
Data inizio appello
20/11/2020
Consultabilità
Completa
Riassunto
Mobile Internet of Things (MIoT) devices, because of their limited resources, are not able to offer complex and intensive services by only exploiting their own local capabilities. Fog Computing represents a valuable solution to overcome this problem by extending the Cloud towards the network edge, distributing services close to the end devices. In order to maintain the advantages deriving from this approach, it is necessary to support the mobility of the devices, keeping the services provided by the Fog layer constantly close to them. For this purpose, the container deployed in the Fog, who hosts the service behaving as the “companion” of the correspondent application running on the mobile node, may need to be migrated appropriately. The aim of this Thesis is to investigate how a Companion Fog Computing platform can exploit the flexibility of the Software Defined Networking (SDN) paradigm. Specifically, the ONOS (Open Network Operating System) SDN controller has been integrated in the system architecture to enable a seamless container migration, simplifiying the internal network infrastructure management. Specific policies and functional components of the platform have been designed and then implemented and validated through an operational prototype, with the objective of pursuing service continuity and container migration transparency.
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Thesis.pdf | 4.30 Mb |
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