Referente: Gianluca Reali (Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.) - The ongoing refactoring of traditional network function in their virtualized software-based counterparts holds the promise to dramatically enhance service delivery and deployment agility. Indeed, business cycles shrink and network function virtualization (NFV) technologies will allow network stakeholders to be able to move quicker than ever, change offerings, promptly add new services, and get better insight, consistency, troubleshooting and visibility into the network status. Computing platforms and virtualization layers have, therefore, a fundamental role in the evolution of network functions technologies and application services. The introduction of lightweight virtualization approaches is paving the way for new, better infrastructures for deploying network functions in terms of resource exploitation. In particular, serverless and function-as-a-service (FaaS) technologies are gaining popularity, given their capability of scaling computing and storage resources according to the user demand. However, nothing comes free and the major drawbacks of these techniques consists of a potential increase in service latency.
This research proposal considers a class of applications that are candidate to be deployed in edge computing platforms, e.g. for latency and data protection. Some IoT-based and vehicle-based applications fall in this category: for these applications, both latency and resource usage efficiency may be important key performance indicators (KPIs). The goal is to investigate the suitability of these new serverless and FaaS virtualization solutions and to pursue an innovative orchestration strategy driven by artificial intelligence for deploying container-based virtualized network middleboxes and critical service components. In fact, AI will play a critical role in designing and optimizing future architectures, protocols, and operations, including forthcoming services fostered by 6G. To go further, the stakeholders using this approach will not deal with AI algorithms, but will simply define their intents, and it will be an AI-empowered decision and orchestration engine to translate these intents into detailed and operative network configurations.