Enabling emergency flow prioritization in SDN networks

Emergency services must be able to transfer data with high priority over different networks. With 5G, slicing concepts at mobile network connections are introduced, allowing operators to divide portions of their network for specific use cases. In addition, Software-Defined Networking (SDN) principles allow to assign different Quality-of-Service (QoS) levels to different network slices.This paper proposes an SDN-based solution, executable both offline and online, that guarantees the required bandwidth for the emergency flows and maximizes the best-effort flows over the remaining bandwidth based on their priority. The offline model allows to optimize the problem for a batch of flow requests, but is computationally expensive, especially the variant where flows can be split up over parallel paths. For practical, dynamic situations, an online approach is proposed that periodically recalculates the optimal solution for all requested flows, while using shortest path routing and a greedy heuristic for bandwidth allocation for the intermediate flows.Afterwards, the offline approaches are evaluated through simulations while the online approach is validated through physical experiments with SDN switches, both in a scenario with 500 best-effort and 50 emergency flows. The results show that the offline algorithm is able to guarantee the resource allocation for the emergency flows while optimizing the best-effort flows with a sub-second execution time. As a proof-of-concept, a physical setup with Zodiac switches effectively validates the feasibility of the online approach in a realistic setup

Towards distributed emergency flow prioritization in software-defined networks

Emergency services must be able to transfer data with high priority over different networks. With 5G, slicing concepts at mobile network connections are introduced, allowing operators to divide portions of their network for specific use cases. In addition, Software-Defined Networking (SDN) principles allow to assign different Quality-of-Service (QoS) levels to different network slices. This paper proposes a microservices-based framework, able to run both centralized and distributed, that guarantees the required bandwidth for the emergency flows and maximizes the best-effort flows over the remaining bandwidth based on their priority. The proposed framework consists of an offline linear model, allowing to optimize the problem for a batch of flow requests. For dynamic situations, an online approach is also required in the framework to handle new incoming flows by calculating the path with a shortest path algorithm and utilizing a greedy approach in assigning bandwidth to the intermediate flows. In this article, the linear model is evaluated through simulation, the distributed architecture is evaluated through emulation while the online approach is validated through physical experiments with SDN switches. The results show that the linear model is able to guarantee the resource allocation for the emergency flows while optimizing the best-effort flows with a sub-second execution time. The distributed architecture is able to split up the managed network into different parts, allowing division of work between controllers. As a proof-of-concept, a prototype with Zodiac switches validates the feasibility of the centralized framework

Prioritized deployment of dynamic service function chains

Service Function Chaining and Network Function Virtualization are enabling technologies that provide dynamic network services with diverse QoS requirements. Regarding the limited infrastructure resources, service providers need to prioritize service requests and even reject some of low-priority requests to satisfy the requirements of high-priority services. In this paper, we study the problem of deployment and reconfiguration of a set of chains with different priorities with the objective of maximizing the service provider's profit; wherein, we also consider management concerns including the ability to control the migration of virtual functions. We show the problem is more practical and comprehensive than the previous studies, and propose an MILP formulation of it along with two solving algorithms. The first algorithm is a fast polynomial-time heuristic that calculates an initial feasible solution to the problem. The second algorithm is an exact method that utilizes the initial feasible solution to achieve the optimal solution quickly. Using extensive simulations, we evaluate the algorithms and show the proposed heuristic can find a feasible solution in at least 83% of the simulation runs in less than 7 seconds, and the exact algorithm can achieve 25% more profit 8 times faster than the state-of-the-art MILP solving methods