Introduction of new, more advanced services to the networking paradigm has led to an increased heterogeneity of media types and network traffic. Although several transport protocols have been developed over the years to cater to the Quality-of-Service requirements of these network services, the dynamic nature of the network condition is a variable that creates a hindrance in the mapping of an efficient transport protocol to a network service.

Dynamic Protocol Selection can serve as a potential solution for this by applying innovative techniques to adaptively select among pre-existing transport protocols during run-time, thus catering to these requirements dynamically. Although the concept has been proven to be beneficial, there are certain research gaps that are yet to be addressed. In this thesis, we attempt to take a step forward to address a few of these research gaps. As a result, we designed a standardized conceptual framework (DPS framework) for the Dynamic Selection of various TCP congestion control algorithms (as the pre-existing transport protocols). To enable this framework to function autonomously, we developed an online learning strategy implemented in the framework design. Also, to ensure efficient deployability of the framework in a real-network, we further proposed a fairness framework to manage multiple DPS framework-enabled application flows to co-exist sustainably.

Through our experiments, we demonstrated the trade-off between application flow performance and learning time for the proposed online learning strategy and the ways it can be tuned to benefit certain types of application flows. We further presented results that showcased around $15\%$ improvement in the fairness performance between multiple application flows and stability in the average flow performance due to the implementation of the proposed fairness framework.

As recently emerged network concepts such as Network Function Virtualization (NFV) and Software-Defined Networking (SDN) promise to bring more flexibility to existing networks they also pave the road for the development of a new type of service. These novel services are known as mission-critical services which generally have tight latency and jitter restrictions. Some examples of this type of service are could gaming, cloud-connected virtual reality, or remote surgery. To avoid exceeding the tight tolerances set by these services, Network Functions (NFs) that are necessary for the network connection (e.g. firewalls), should benefit from both hardware acceleration for increased performance and scaling to reduce the hardware footprint and introduce more network flexibility. In this thesis, a novel, horizontal scaling solution for Virtualized Network Functions (VNFs) was designed and implemented in P4. In particular, we propose a novel elastic scaling solution for hardware-accelerated switches. Our solution consists of three parts: flow migration, monitoring, and decision-making. Flow migration, the part where flows are migrated to another switch, is performed in three phases. First, the migration source migrates the state to the migration destination. Then, the migration source updates the NF states as they change on the migration source. When the controller completes its tasks, flow packets are forwarded to the migration destination and the controller can divert the traffic to the migration destination directly. Our decision-making algorithm uses VNF, switch, and individual flow usage statistics to decide where to scale to and if applicable where to place a new NF instance. To reduce the monitoring overhead, only usage statistics for high-rate flows are gathered and overloads are detected within the switch. Our decision-making algorithm is able to spawn new NF instances when an overload occurs or redistribute load among already existing NF instances to optimize NF resource usage. Results show that our algorithm reduces migration times by 0.52s on average while the average latency observed by the flow is reduced by approximately 5ms on average when compared to current state-of-the-art. Furthermore, our solution does not overflow the controller as NF states are communicated directly between switches which are equipped with links that are optimized for high data volumes.