Stefano Salsano

Giuseppe Tomei, Andrea Mayer, Giuseppe Alcini et al.

The accelerating expansion of AI workloads is colliding with an energy landscape increasingly dominated by intermittent renewable generation. While vast quantities of zero-carbon energy are routinely curtailed, today's centralized datacenter architectures remain poorly matched to this reality in both energy proportionality and geographic flexibility. This work envisions a shift toward a distributed fabric of renewable-powered micro-datacenters that dynamically follow the availability of surplus green energy through live workload migration. At the core of this vision lies a formal feasibility-domain model that delineates when migratory AI computation is practically achievable. By explicitly linking checkpoint size, wide-area bandwidth, and renewable-window duration, the model reveals that migration is almost always energetically justified, and that time-not energy-is the dominant constraint shaping feasibility. This insight enables the design of a feasibility-aware orchestration framework that transforms migration from a best-effort heuristic into a principled control mechanism. Trace-driven evaluation shows that such orchestration can simultaneously reduce non-renewable energy use and improve performance stability, overcoming the tradeoffs of purely energy-driven strategies. Beyond the immediate feasibility analysis, the extended version explores the architectural horizon of renewable-aware AI infrastructures. It examines the role of emerging ultra-efficient GPU-enabled edge platforms, anticipates integration with grid-level control and demand-response ecosystems, and outlines paths toward supporting partially migratable and distributed workloads. The work positions feasibility-aware migration as a foundational building block for a future computing paradigm in which AI execution becomes fluid, geographically adaptive, and aligned with renewable energy availability.

Paolo Lungaroni, Andrea Mayer, Stefano Salsano et al.

Virtualized environments offer a flexible and scalable platform for evaluating network performance, but they can introduce significant variability that complicates accurate measurement. This paper presents PASTRAMI, a methodology designed to assess the stability of software routers, which is critical to accurately evaluate performance metrics such as the Partial Drop Rate at 0.5% (PDR@0.5%). While PDR@0.5% is a key metric to assess packet processing capabilities of a software router, its reliable evaluation depends on consistent router performance with minimal measurement variability. Our research reveals that different Linux versions exhibit distinct behaviors, with some demonstrating non-negligible packet loss even at low loads and high variability in loss measurements, rendering them unsuitable for accurate performance assessments. This paper proposes a systematic approach to differentiate between stable and unstable environments, offering practical guidance on selecting suitable configurations for robust networking performance evaluations in virtualized environments.

Pier Luigi Ventre, Alberto Caponi, Giuseppe Siracusano et al.

Many reasons make NFV an attractive paradigm for IT security: lowers costs, agile operations and better isolation as well as fast security updates, improved incident responses and better level of automation. On the other side, the network threats tend to be increasingly complex and distributed, implying huge traffic scale to be monitored and increasingly strict mitigation delay requirements. Considering the current trend of the net- working and the requirements to counteract to the evolution of cyber-threats, it is expected that also network monitoring will move towards NFV based solutions. In this paper, we present D- StreaMon an NFV-capable distributed framework for network monitoring realized to face the above described challenges. It relies on the StreaMon platform, a solution for network monitoring originally designed for traditional middleboxes. An evolution path which migrates StreaMon from middleboxes to Virtual Network Functions (VNFs) has been realized.