Automated Management of Network Slices with Service Guarantees

Abstract

Future mobile networks are expected to support a diverse set of applications including high-throughput video streaming, delay-sensitive augmented reality applications, and critical control traffic for autonomous driving. Unfortunately, existing networks do not have the required management mechanisms to handle this complex mix of traffic efficiently. At the same time, however, there is a significant effort from both industry and academia to make networks more open and programmable, leading to the emergence of software-defined networking, network function virtualization, and packet-forwarding programming languages. Moreover, several organisations such as the Open Networking Foundation were founded to facilitate innovation and lower the entry barriers in the mobile networking industry. In this setting, the concept of network slicing emerged which involves the partitioning of the mobile network into virtual networks that are tailored for specific applications. Each network slice needs to provide premium service to its users as specified in a service level agreement between the mobile network operator and the customer. The deployment of network slices has been largely realized thanks to network function virtualization. However, little progress has been made on mechanisms to efficiently share the network resources among them. In this dissertation, we develop such mechanisms for the licensed spectrum at the base station, a scarce resource that operators obtain through competitive auctions. We propose a system architecture composed of two new network functions; the bandwidth demand estimator and the network slice multiplexer. The bandwidth demand estimator monitors the traffic of the network slice and outputs the amount of bandwidth currently needed to deliver the desired quality of service. The network slice multiplexer decides which bandwidth demands to accept when the available bandwidth does not suffice for all the network slices. A key feature of this architecture is the separation of the demand estimation task from the contention resolution task. This separation makes the architecture scalable for a large number of network slices. It also allows the mobile network operator to charge fairly each customer based on their bandwidth demands. In contrast, the most common approach in the literature is to learn online how to split the available resources among the slices to maximize a total network utility. However, this approach is neither scalable nor suitable for service level agreements. The dissertation contributes several algorithms to realize the proposed architecture and provisioning methods to guarantee the fulfillment of the service level agreements. To satisfypacket delay requirements, we develop a bandwidth demand estimator based on queueing theory and online learning. To share resources efficiently even in the presence of traffic anomalies, we develop a network slice multiplexer based on the Max-Weight algorithm and hypothesis testing. We implement and test the proposed algorithms on network simulators and 5G testbeds to showcase their efficiency in realistic settings. Overall, we present a scalable architecture that is robust to traffic anomalies and reduces the bandwidth needed to serve multiple network slices.