Network slicing enables the coexistence of multiple virtual networks over shared infrastructure, each designed to meet distinct service-level requirements. Ensuring SLA compliance in such environments demands efficient and fine-grained monitoring, particularly within the transport network (TN). Existing In-band Network Telemetry (INT) solutions, however, face challenges related to scalability and overhead. We propose SliceMon, a slice-aware SLA monitoring framework for P4enabled TNs. SliceMon deploys lightweight machine learning models in the data plane to locally infer potential SLA violations, while the controller aggregates these inferences using ensemble methods and activates detailed telemetry collection only when necessary. We evaluate SliceMon under delay-based SLAs using datasets gathered via emulation with Mininet and BMv2. Results demonstrate that SliceMon maintains high detection accuracy while significantly reducing monitoring overhead and improving responsiveness compared to traditional periodic telemetry approaches.

HTTP is frequently used by smartphones and IoT devices to access information and Web services. Nowadays, HTTP is used in three major versions, each introducing significant changes with respect to the previous one. We evaluated the energy consumption of the major versions of the HTTP protocol when used in the communication between energy-constrained devices and cloud-based or edge-based services. Experimental results show that in a machine-to-machine communication scenario, for the considered client devices – a smartphone and a Single Board Computer – and for a number of cloud/edge services and facilities, HTTP/3 frequently requires more energy than the previous versions of the protocol. The focus of our analysis is on machine-to-machine communication, but to obtain a broader view we also considered a client–server interaction pattern that is more browsing-like. In this case, HTTP/3 can be more energy efficient than the other versions.

Given the fundamental role of the Internet in our lives, a better understanding of its operational status during war times is crucial. In this paper, we analyze the Ukrainian Internet during the first months of war after the Russian invasion occurred in February 2022. The analysis is carried out from two points of view: routing and latency. In terms of routing, there is a substantial increase in BGP announcements and withdrawals which can be due to both the physical unavailability of facilities and cyberattacks. Latency also increased significantly compared to the pre-conflict period, especially when considering paths going from Ukraine to Russia. The increase in latency appears to be due to a partial shift from peering to transit.

Edge computing is an emerging architecture in 5G networks where computing power is provided at the edge of the fixed network, to be as close as possible to the end users. Computation offloading, better communication latency, and reduction of traffic in the core network are just some of the possible benefits. However, the Quality of Experience (QoE) depends significantly on the network performance of the user device towards the edge server vs. cloud server, which is not known a priori and may generally change very fast, especially in heterogeneous, dense, and mobile deployments. Building on the emergence of standard interfaces for the installation and operation of thirdparty edge applications in a mobile network, such as the MultiAccess Edge Computing (MEC) under standardization at the European Telecommunications Standards Institute (ETSI), we propose MECPerf, a tool for user-driven network performance measurements. Bandwidth and latency on different network segments are measured and stored in a central repository, from where they can be analyzed, e.g., by application and service providers without access to the underlying network management services, for run-time resource optimization.

Crowdsourcing enables the fine-grained characterization and performance evaluation of today׳s large-scale networks using the power of the masses and distributed intelligence. This paper presents SmartProbe, a system that assesses the bottleneck capacity of Internet paths using smartphones, from a mobile crowdsourcing perspective. With SmartProbe measurement activities are more bandwidth efficient compared to similar systems, and a larger number of users can be supported. An application based on SmartProbe is also presented: georeferenced measurements are mapped and used to compare the performance of mobile broadband operators in wide areas. Results from one year of operation are included.

Several works over the past few years have shown that the Internet AS-level topology is partially hidden from the current Internet measurement infrastructures. Most have focused on the incompleteness of the connectivity extracted from BGP data. A few have analysed the connectivity collected by traceroute measurement infrastructures showing the amount of connections introduced by traceroute campaigns. None, however, have investigated in detail the underlying rationale, i.e. the economic nature of the Internet. In this paper we fill this gap by analysing five traceroute infrastructures, found to be active in October 2013, with the p2c-distance metric, which is specifically designed to capture the complex economic dynamics that rule the Internet. We found that the traceroute infrastructures that currently run topology discovery measurements (Ark, DIMES and Portolan), together with BGP route collectors, are able to reveal the full connectivity of 23.50% of the Internet core ASes. This is a considerable improvement given that the BGP infrastructure alone is able to cover only 15.90% of the Internet core. This percentage could be increased up to 48.48% if the remaining two infrastructures (Dasu/Ono and RIPE Atlas) performed topology discovery campaigns. We also found that the placement of traceroute probes is not optimal from a topology discovery perspective, as it causes several probes to provide only redundant connectivity information. We show that the same number of traceroute probes optimally deployed, would be able to completely reveal the full AS connectivity of the Internet core.

Portolan is a crowdsourcing system, aimed at monitoring and measuring large-scale networks, that uses smartphones as mobile observation elements. Currently, Portolan is able to collect information about both wired and wireless networks, in particular it is used to obtain the graph of the Internet with unprecedented resolution and to associate performance indexes (received signal strength, maximum throughput) of cellular networks to geographic locations.