The large bulk of packets/flows in future core networks will require a highly efficient header processing in the switching elements. Simplifying lookup in core network switching elements is capital to transport data at high rates and with low latency. Flexible network hardware combined with agile network control is also an essential property for future software-defined networking. We argue that only further decoupling between the control and data planes will unlock the flexibility and agility in SDN for the design of new network solutions for core networks. This article proposes a new approach named KeyFlow to build a flexible network-fabricbased model. It replaces the table lookup in the forwarding engine by elementary operations relying on a residue number system. This provides us tools to design a stateless core network by still using OpenFlow centralized control. A proof of concept prototype is validated using the Mininet emulation environment and OpenFlow 1.0. The results indicate RTT reduction above 50 percent, especially for networks with densely populated flow tables. KeyFlow achieves above 30 percent reduction in keeping active flow state in the network.

Self-driving networks guided by machine-learning (ML) algorithms are the driving force for building networks of the future. ML is effective at making inferences about data that is too complex or too unpredictable for humans. The network softwarization enabled by a deep programmability approach opens up new opportunities to deploy ML at the programmable data plane. In this paper, we introduce the MAP4 as a framework that explores the feasibility of mapping ML models in programmable network devices. To achieve this, we rely on the P4 language to deploy a pre-trained model into a programmable switch, utilizing the ML model to accurately classify flows at line rate. Our approach demonstrates that ML models working as classifiers can better fit the data by using the new levels of network programmability from the P4 language. The results showed that with few packets, most of the flows are properly classified. In some use cases, with two packets in the flow, 97% of traffic can be correctly classified, and all classes are properly labeled with a maximum of four packets.

5G relies on its pervasive and convergent cloud-based architecture to accomplish its futuristic challenge of being the next-generation communication platform. However, the new perspectives opened by 5G networks do not go unnoticed. Regardless of their motivation or objectives, cyberattackers find in the new 5G ecosystem, including its tenancy-driven control plane, an attractive greenfield to create new types of denial of services attacks. In this paper, we leverage on the virtualised environment of 5G to propose REPEL – an intelligent resource scaling strategy to mitigate DDoS signalling attacks preserving legitimate traffic. Our prevention-based approach uses games theory to build up a defence front line, able to keep services availability and discourage the attacker. To demonstrate the effectiveness and feasibility of our approach, we feed a queuing model with parameters obtained from a testbed, where simulated subscribers connect to a virtualised evolved packet core prototype. The final results show a dramatic signalling losses reduction, which can ensure the appropriate control plane availability under a DDoS attack.

Innovative traffic engineering functions and services require disrupting routing and forwarding mechanisms to be performed with low overhead over complex network topologies. Source routing (SR) is a prominent alternative to table-based routing for providing the needed expressiveness and agility by reducing the number of network states. This work proposes the M-PolKA, a topology-agnostic multipath source routing scheme and orchestration architecture for reliable communications, which explores special properties from the Residue Number System (RNS) polynomial arithmetic. A P4-based proof-of-concept is experimentally demonstrated using emulated and hardware prototypes. Also, use cases for revealing M-PolKA’s functionalities are tested in different scenarios in order to address problems, such as communication reliability improvement, agile path migration and fast failure reaction. Finally, low overhead for extra functionalities is observed when RNS-based SR is compared to traditional routing approaches.

At the verge of the launch of the first commercial fifth generation (5G) system, trends in wireless and optical networks are proceeding toward increasingly dense deployments, supporting resilient interconnection for applications that carry higher and higher capacity and tighter latency requirements. These developments put increasing pressure on network backhaul and drive the need for a re-examination of traditional backhaul topologies. Challenges of impending networks cannot be tackled by star and ring approaches due to their lack of intrinsic survivability and resilience properties, respectively. In support of this re-examination, we propose a backhaul topology design method that formulates the topology optimization as a graph optimization problem by capturing both the objective and constraints of optimization in graph invariants. Our graph theoretic approach leverages well studied mathematical techniques to provide a more systematic alternative to traditional approaches to backhaul design. Specifically, herein, we optimize over some known graph invariants, such as maximum node degree, topology diameter, average distance, and edge betweenness, as well as over a new invariant called node Wiener impact, to achieve baseline backhaul topologies that match the needs for resilient future wireless and optical networks.

This paper presents a scalable and efficient solution for secure network design that involves the selection and verification of network paths. The proposal addresses the challenges related to compliance policies by introducing a Proof-of-Transit (PoT) feasible implementation for path-aware programmable networks. Our approach relies on i) a source routing mechanism based on a fixed routeID representing a unique identifier per path, which serves as a key for PoT lookup tables; ii) the "in situ" that allows to collect telemetry information in the packet while the packet traverses a path. The former enables path selection with policy at the edge, while the later allows to perform path verification without extra probe-traffic. A P4 programmable language prototype demonstrates the effectiveness of this approach to protect against deviation attacks with low overhead. The results show its scalability considering the protocol overhead as the path length increases; a significant reduction in network’s forwarding state for fat-tree topologies depending on the workload per path (flows/path). Finally, experimental results show a RTT comparison evaluation, the impact of PoT computation, protection to path deviation and seamless path migration keeping flow protection.