We propose a novel two-level hierarchical auction model for 5G RAN network slicing that enables financially-aware resource allocation among Mobile Network Operators (MNOs), Mobile Virtual Network Operators (MVNOs), and end users. Compared to prior models that primarily emphasize technical resource utilization, our approach integrates a realistic financial cost framework, including electricity consumption and resale bidding fees into the optimization process. Each level solves a Winner Determination Problem (WDP) using Integer Linear Programming (ILP) and scalable heuristic algorithms. Vickrey–Clarke–Groves (VCG)-based pricing is adopted to ensure incentive compatibility and procedural fairness among bidders. The optimization objective is to maximize the social welfare, defined in this work as the total valuation of accepted requests minus the electricity cost incurred by CU–DU placement. Power consumption is therefore internalized as a cost component in the objective function rather than treated as an independent optimization target. Through extensive simulations over different network topologies and dynamic traffic conditions, we show that the proposed approach achieves higher social welfare compared to baseline methods, while maintaining scalability and satisfying the latency and capacity constraints of heterogeneous network slices.

In the context of the ever-evolving 5G landscape, where network management and control are paramount, a new Radio Access Network (RAN) as emerged. This innovative RAN offers a revolutionary approach by enabling the flexible distribution of baseband functions across various nodes, all tailored to meet the ever-shifting demands of both system requirements and user traffic patterns. As users move within the network, the need to anticipate and strategically position these baseband functions becomes crucial for seamless network operation. Traditionally, this challenge has been tackled through a two-step process: first, forecasting traffic patterns, and then optimizing resource allocation accordingly. However, this approach falls short in guaranteeing an efficient placement when actual traffic demands surge onto the network. It often leads to resource overbooking, constraint violations, and excessive power consumption, putting strain on the network’s capabilities. In this paper, we introduce a novel framework based on a black-box optimization approach. This tool empowers prediction algorithms not just with historical traffic data but also with insights from optimization outcomes. The goal is to minimize a loss function related to power consumption and constraint violation: this ensures a predicted placement that is feasible and whose power is close to optimal. This approach ensures that the predicted placement is both feasible and power-efficient, bridging the gap between theoretical prediction and practical implementation. Remarkably, our proposed method, while potentially sacrificing some degree of traffic prediction accuracy, outperforms the conventional two-step approach by delivering a more efficient baseband function placement.

Network slicing is an important characteristic of 5G/6G networks that increases flexibility and enables different applications over a single infrastructure. The physical resources are partitioned to create virtualized networks, each dedicated to services with specific requirements. Several entities participate in network slicing, including Mobile Network Operators (MNOs), Mobile Virtual Network Operators (MVNOs), and users. An MNO owns the physical network infrastructure and the resources. MVNOs lease resources from the MNO and operate as service providers towards their subscribers. The goal of this work is to optimize the end-to-end network slicing process to provide services to users with a fair sharing of resources. We model this problem as a hierarchical combinatorial auction with a modified Vickrey-Clarke-Groves pricing mechanism. In the upper-level auction, an MNO is the seller supplying Network Slice to several MVNOs, who act as the bidders. In the lower-level auction, each MVNO holds an auction as a seller delivering services to their subscribed end-users, who play the role of bidders. We formulate and solve the Winner Determination Problem using mathematical programming and heuristic algorithms. The simulations show that the model can achieve fair sharing of resources, and it enables improving the MNO and MVNO revenue.

The increased demand for high-quality Internet connectivity resulting from the growing number of connected devices and advanced services has put significant strain on telecommunication networks. In response, cutting-edge technologies such as Network Function Virtualization (NFV) and Software Defined Networking (SDN) have been introduced to transform network infrastructure. These innovative solutions offer dynamic, efficient, and easily manageable networks that surpass traditional approaches. To fully realize the benefits of NFV and maintain the performance level of specialized equipment, it is critical to assess the behavior of Virtual Network Functions (VNFs) and the impact of virtualization overhead. This paper delves into understanding how various factors such as resource allocation, consumption, and traffic load impact the performance of VNFs. We aim to provide a detailed analysis of these factors and develop analytical functions to accurately describe their impact. By testing VNFs on different testbeds, we identify the key parameters and trends, and develop models to generalize VNF behavior. Our results highlight the negative impact of resource saturation on performance and identify the CPU as the main bottleneck. We also propose a VNF profiling procedure as a solution to model the observed trends and test more complex VNFs deployment scenarios to evaluate the impact of interconnection, co-location, and NFV infrastructure on performance.

Today, more and more enterprises are embarking on a digital transformation where most of their applications are hosted in the Cloud. As a result, a reliable Wide Area Network (WAN) has become a primary need to interconnect their distributed branch offices and data centers that accommodate those applications. Software-Defined Wide Area Network (SD-WAN) represents the most promising technology solution for next-generation enterprise networks, being able to increase network agility and reduce costs. In this paper, we present an experimental SD-WAN solution capable of running and optimizing delay-sensitive high-priority services, such as real-time video streaming, while minimizing downtime caused by network failures. This solution comprises a monitoring and a traffic engineering system for SD-WAN. The first consists of a Transport-layer Passive Monitoring (TPM) system based on extended Berkeley Packet Filter (eBPF) technology with the goal of monitoring TCP flows; the second consists of an application, running inside the SD-WAN controller, with the goal of orchestrating the network traffic in consideration of the monitoring measurements by ensuring rapid recovery and resilience in case of unexpected congestion events. We validate our solution over two SD-WAN testbeds: the first is hosted in our laboratory at Politecnico di Milano, while the second is deployed in a municipal network of an Italian city. Results show that our SD-WAN solution can increase the overall service availability while meeting the stringent QoS requirements of delay-sensitive services.s

The 5G Radio Access Network (RAN) virtualization aims to improve network quality and lower the operator’s costs. One of its main features is the functional split, i.e., dividing the instantiation of RAN baseband functions into different units over metro-network nodes. However, its optimal placement is non-trivial: it depends on the application requirements and on the expected traffic volume, whose daily variation highly impacts the total power consumption. Current optimization solutions fail to provide a placement solution capable of handling traffic fluctuations. In fact, the standard machine learning algorithms used in the literature for planning the network resources in advance result in an allocation that is inadequate to carry the actual traffic at all the time-slots. Hence, we must reserve an artificial buffer capacity in the nodes to ensure feasibility. Instead, our proposed method exploits a fine-grained two-step multi-task algorithm that predicts the mean and quantile traffic, making the artificial capacity no longer necessary. The subsequent placement uses mixed-integer linear programming and a heuristic. The former considers the expected traffic in the objective function (to estimate costs) and the quantile in the constraints (to enforce capacity limits). The heuristic combines the mean and quantile results to minimize the power and comply with the requirements. While using sufficiently large artificial buffers guarantees robustness with a mild power increase compared to the oracle, the fine-grained multi-task model improves the results, reducing the power consumption compared to the mean and meets all constraints. The heuristic enables significant computational time reduction.

Widespread deployments of Network Function Virtualization (NFV) technology will replace many physical appliances in telecommunication networks with software executed on cloud platforms. Setting compute servers continuously to high-performance operating modes is a common NFV approach for achieving predictable operations. However, this has the effect that large amounts of energy are consumed even when little traffic needs to be forwarded. The Dynamic Voltage-Frequency Scaling (DVFS) technology available in Intel processors is a known option for adapting the power consumption to the workload, but it is not optimized for network traffic processing workloads. We developed a novel control method for DVFS, based observing the ongoing traffic and online predictions using machine learning. Our results show that we can save up to 27% compared to commodity DVFS, even when including the computational overhead of machine learning.