Network administrators have long relied on passive measurement to detect malicious activity, diagnose misconfigurations, and ensure network health. Under the assumption that threats and issues originate externally, prior studies [1] have predominantly focused on the analysis of inbound traffic — i.e., traffic initiated by external hosts and targeting internal destinations. Conversely, outbound traffic — i.e., originating from internal hosts toward external destinations — has received comparatively less attention, despite carrying strong indicators of security-relevant anomalies [2].

Federated testbeds enable collaborative research by providing access to diverse resources, including computing power, storage, and specialized hardware like GPUs, programmable switches and smart Network Interface Cards (NICs). Efficiently sharing these resources across federated institutions is challenging, particularly when resources are scarce and costly. GPUs are crucial for AI and machine learning research, but their high demand and expense make efficient management essential. Similarly, advanced experimentation on programmable data plane requires very expensive programmable switches (e.g., based on P4) and smart NICs. This paper introduces SHARY (SHaring Any Resource made easY), a dynamic reservation system that simplifies resource booking and management in federated environments. We show that SHARY can be adopted for heterogenous resources, thanks to an adaptation layer tailored for the specific resource considered. Indeed, it can be integrated with FIGO (Federated Infrastructure for GPU Orchestration), which enhances GPU availability through a demand-driven sharing model. By enabling real-time resource sharing and a flexible booking system, FIGO improves access to GPUs, reduces costs, and accelerates research progress. SHARY can be also integrated with SUP4RNET platform to reserve the access of P4 switches.

The advent of Large Language Models (LLMs) and generative AI is fundamentally transforming information retrieval and processing on the Internet, bringing both great potential and significant concerns regarding content authenticity and reliability.This paper presents a novel quantitative approach to shed light on the complex information dynamics arising from the growing use of generative AI tools.Despite their significant impact on the digital ecosystem, these dynamics remain largely uncharted and poorly understood.We propose a stochastic model to characterize the generation, indexing, and dissemination of information in response to new topics.This scenario particularly challenges current LLMs, which often rely on real-time Retrieval-Augmented Generation (RAG) techniques to overcome their static knowledge limitations.Our findings suggest that the rapid pace of generative AI adoption, combined with increasing user reliance, can outpace human verification, escalating the risk of inaccurate information proliferation across digital resources.An in-depth analysis of Stack Exchange data confirms that high-quality answers inevitably require substantial time and human effort to emerge.This underscores the considerable risks associated with generating persuasive text in response to new questions and highlights the critical need for responsible development and deployment of future generative AI tools.

Data center networks undergo the coexistence of latency-sensitive mice flows and bandwidth-intensive elephant flows. Jointly optimizing the performance of both traffic classes poses complex challenges. Existing flow schedulers either rely on detailed flow size information or require numerous physical priority queues (PQs) within network switches, thus facing practical challenges.In this work, we propose a novel flow scheduling algorithm, namely Multi-Path Multi-Level Feedback Queueing (MPMLFQ), to overcome these limitations. MP-MLFQ leverages the spatial diversity and regularity of DCNs to realize a scheduler with numerous logical priority levels while occupying as few as 2 physical PQs at each switch port. We designed MP-MLFQ to run atop modern programmable networks, and highlighted how to implement it without modifications at the end-hosts’ stacks. Our simulation results show that MP-MLFQ outperforms existing flow size-agnostic solutions in minimizing the flow completion time, when only two PQs are available.

Probabilistic approaches allow designing very efficient data structures and algorithms aimed at computing the number of flows within a given observation window. The practical applications are many, ranging from security to network monitoring and control. We focus our investigation on approaches tailored for sliding windows, that enable continous-time measurements independently from the observation window. In particular, we show how to extend standard approaches, such as Probabilistic Counting with Stochastic Averaging (PCSA), to count over an observation window. The main idea is to modify the data structure to store a compact representation of the timestamp in the registers and to modify coherently the related algorithms. We propose a timestamp-augmented version of PCSA, denoted as TS-PCSA, and compare it with state-of-the-art solutions based on Hyper-LogLog (HLL) counters that evaluate the cardinality over a sliding window, but without storing the timestamps. We will show that TS-PCSA with a limited memory footprint is achieving a different tradeoff between memory and accuracy with respect to HLL-based solutions.