Francesco Monaco
Enabling Seamless Autoscaling of Service Function Chains in Kubernetes.
Rel. Fulvio Giovanni Ottavio Risso. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2022
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Abstract
Service Function Chains (SFCs) are typical components of the edge networks of telecommunications operators. They are composed by chained network functions which were originally deployed as physical appliances and subsequently as Virtual Network Functions (VNFs). Recently a new paradigm shift towards Cloud Native Network Functions (CNFs) is taking place. Kubernetes is the reference platform for running cloud native workloads, however it is not natively oriented to network functions. This thesis presents the prototype of a system to integrate SFCs in Kubernetes, with support for seamless autoscaling of chain elements. The proposed solution enables the creation of arbitrary chains starting from a logical description and a set of network functions deployments.
In order to dynamically adapt to the horizontal scaling of network functions, a controller that watches and reacts to the events concerning scaling is included in the system
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