The rapid development of 5G brought advanced capabilities to support low-latency and bandwidth-hungry applications, including edge computing capabilities. However, service orchestration on the network edge is characterized by the scarcity of available resources in the edge infrastructure, emphasizing the need for optimal resource allocation. In this work, we explore a multi-objective optimization approach to resource allocation in 5G edge networks that focuses on the deployment of edge applications and User Plane Functions (UPFs) to maximize operator’s profit with minimum energy consumption. Due to the computationally expensive nature of the optimization formulation, we develop a solution that relies on the use of genetic algorithms, specifically a non-dominated sorting genetic algorithm (NSGA-II)-based multi-objective approach. We compare the performance obtained with our approach against the optimal solution, where the latter can be obtained only for small instances, and another benchmark heuristic. We do this for three different topologies under varying demand levels. Results show that our approach yields solid performance, outperforming the benchmark by at least 25%.

As public and private cloud-native deployments of the 5G Core (5GC) networks are rolling-out on a wide scale, attention is shifting towards efficient state management. While stateful deployments were the default method in the previous generations of mobile networks, they lack the necessary flexibility that cloud-native orchestration demands. Yet, traditional approaches taken to enable stateless deployments require the operators to sacrifice on performance due to the frequent state transactions. To overcome this issue, in this paper we propose a Piggyback-based and a Proactive-Push approach which allow for procedure-aware stateless 5GC systems. Our evaluations highlight the advantages of the Piggyback approach for two synchronous control procedures, reducing their completion time by ~44% and ~70% compared to the baseline. For asynchronous procedures, the Proactive-Push approach outperforms the baseline with ~13% and ~22%. More importantly, these mechanisms do not pose additional overhead on CPU and bandwidth utilization.

The introduction of the Service-Based Architecture (SBA) for the 5G Core Networks has drastically changed the way these networks are designed and operated. Aiming for higher flexibility and agility, the adoption of SBA is the first step towards cloud-native deployments of 5G Core. However, the high degree of functional decomposition in SBA has implications in terms of increased inter-NF signaling traffic during the execution of control plane procedures, as well as an increased complexity in orchestrating a system with tight inter-NF dependencies. In this work, we introduce PP5GS as a stateless 5G Core architecture that implements a procedure-based functional decomposition of the 5G Core NFs. We develop Per-Procedure NFs for four different control plane procedures and perform extensive evaluations in a private cloud environment orchestrated with Kubernetes. The results show that PP5GS requires up to 34% and 55% less computing resources compared to the baseline stateful and stateless systems, respectively, while generating at least 40% less signaling traffic. Moreover, complex control plane procedures can complete up to 50% faster. Lastly, the results show that PP5GS is a more feasible architecture in leveraging edge-offloading of 5G Core NFs.

Languages for programming data planes like P4 sparked a plethora of new applications in the data plane. The dynamic, evolving environment makes it challenging to understand what performance can be expected when running a program in a specific data plane target. However, knowing this is crucial for network operators when upgrading their networks.

The increasing popularity of cloud-native approaches has led to their wide adoption in the telecommunications industry. 5G Core Networks (5GCN) are developed to take advantage of cloud-native design principles, with a high degree of functional decomposition and distributed deployment. This results in implications in inter-Network Function (NF) dependencies that need to be studied. This work focuses on investigating the effect that these dependencies have in how the resources are utilized from the 5GCN NFs. We consider a private cloud environment where a reference 5G Core implementation, namely Free5GC, is deployed and orchestrated with Kubernetes. In addition, a gNB & UE Emulator is developed to allow for the execution of different control plane procedures. Our evaluations highlight the importance of catering for the inter-NF dependencies in achieving efficient resource utilization as well as avoiding deployments where a single NF can bottleneck the entire 5GCN.

Network programmability also sneaked into the mobile world leading to the emergence of Software-Defined Radio Access Network (SD-RAN) architectures. Interestingly, while only a small number of prototype architectures exist for SD-RAN, their performance evaluations are unfortunately also limited. Recent evaluations are carried out for small network dimensions of up to 50 devices, while emerging 5G/6G networks envision numbers of devices beyond 5000. Although 5G/6G applications are more stringent with respect to latency guarantees, performance evaluations of such low scale remain questionable. To fill this void, this paper presentsMARC: a novel benchmarking tool for SD-RAN architectures and their controllers. We useMARCto measure, analyze and identify performance implications for two state-of-the-art open source SD-RAN solutions:FlexRANand5G-EmPOWER. We perceive results for monitoring application scenarios considering fully centralized control. For this setting, our findings show that the proposed architectures with a single SD-RAN controller are not scalable and can even lead to unpredictable network operations. Using our tool and based on our insights, we provide and implement design guidelines for the internal working behavior of the existing controllers.

Byzantine Fault Tolerance (BFT) enables correct operation of distributed, i.e., replicated applications in the face of malicious take-over and faulty/buggy individual instances. Recently, BFT designs have gained traction in the context of Software Defined Networking (SDN). In SDN, controller replicas are distributed and their state replicated for high availability purposes. Malicious controller replicas, however, may destabilize the control plane and manipulate the data plane, thus motivating the BFT requirement. Nonetheless, deploying BFT in practice comes at a disadvantage of increased traffic load stemming from replicated controllers, as well as a requirement for proprietary switch functionalities, thus putting strain on switches' control plane where particular BFT actions must be executed in software. P4BFT leverages an optimal strategy to decrease the total amount of messages transmitted to switches that are the configuration targets of SDN controllers. It does so by means of message comparison and deduction of correct messages in the determined optimal locations in the data plane. In terms of the incurred control plane load, our P4-based data plane extensions outperform the existing solutions by ~33.2% and ~40.2% on average, in random 128-switch and Fat-Tree/Internet2 topologies, respectively. To validate the correctness and performance gains of P4BFT, we deploy bmv2 and Netronome Agilio SmartNIC-based topologies. The advantages of P4BFT can thus be reproduced both with software switches and "commodity" P4-enabled hardware. A hardware- accelerated controller packet comparison procedure results in an average 96.4% decrease in processing delay per request compared to existing software approaches.