Publications

2026

1. USENIX NSDI

   

   Observability Is Eating Your Cores: Fine-Grained Analysis of Microservice Metrics with IPU-Hosted Sketches

   Alessandro Cornacchia, Theophilus A Benson, Muhammad Bilal, and Marco Canini

   In USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2026

   Abs Bib PDF Code

   Observability has become mission-critical for troubleshooting cloud-native technology. However, today’s observability fails to meet the demands of cloud-native environments, either resulting in crippling complexity and high costs for collecting and storing huge data volumes, or sacrificing events coverage by sampling at coarse time granularity. We present µView, which stands out from conventional cloud monitors by incorporating a lightweight observability data-plane on Infrastructure Processing Units (IPUs). Our novel architecture leverages the proximity of IPUs to the monitored services to tackle observability bloat. Crucially, µView’s data-plane applies streaming data sketching techniques to continuously process and analyze microservice’s metrics at fine time resolution, without hurting application performance. We show for several use cases that by anticipating SLO violations µView can help (i) narrow the focus on informative observability data, and (ii) trigger useful signals about service performance, thus enabling timely proactive actions.

   @inproceedings{cornacchiaobservability,
     title = {Observability Is Eating Your Cores: Fine-Grained Analysis of Microservice Metrics with IPU-Hosted Sketches},
     author = {Cornacchia, Alessandro and Benson, Theophilus A and Bilal, Muhammad and Canini, Marco},
     year = {2026},
     booktitle = {USENIX Symposium on Networked Systems Design and Implementation (NSDI)},
   }

2. arXiv

   MAESTRO: Multi-Agent Evaluation Suite for Testing, Reliability, and Observability

   Tie Ma, Yixi Chen, Vaastav Anand, Alessandro Cornacchia, Amândio R Faustino, Guanheng Liu, Shan Zhang, Hongbin Luo, Suhaib A Fahmy, Zafar A Qazi, and 1 more author

   arXiv preprint arXiv:2601.00481, 2026

   Bib

   @article{ma2026maestro,
     title = {MAESTRO: Multi-Agent Evaluation Suite for Testing, Reliability, and Observability},
     author = {Ma, Tie and Chen, Yixi and Anand, Vaastav and Cornacchia, Alessandro and Faustino, Am{\a}ndio R and Liu, Guanheng and Zhang, Shan and Luo, Hongbin and Fahmy, Suhaib A and Qazi, Zafar A and others},
     journal = {arXiv preprint arXiv:2601.00481},
     year = {2026},
   }

2025

1. ACM NGNO

   Towards a Playground to Democratize Experimentation and Benchmarking of AI Agents for Network Troubleshooting

   Zhihao Wang, Alessandro Cornacchia, Franco Galante, Carlo Centofanti, Alessio Sacco, and Dingde Jiang

   In Proceedings of the 1st Workshop on Next-Generation Network Observability, 2025

   Bib

   @inproceedings{wang2025towards,
     title = {Towards a Playground to Democratize Experimentation and Benchmarking of AI Agents for Network Troubleshooting},
     author = {Wang, Zhihao and Cornacchia, Alessandro and Galante, Franco and Centofanti, Carlo and Sacco, Alessio and Jiang, Dingde},
     booktitle = {Proceedings of the 1st Workshop on Next-Generation Network Observability},
     pages = {1--3},
     year = {2025},
   }

2. ACM SIGIR

   

   Information Retrieval in the Age of Generative AI: The RGB Model

   Michele Garetto, Alessandro Cornacchia, Franco Galante, Emilio Leonardi, Alessandro Nordio, and Alberto Tarable

   In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2025

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   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.

   @inproceedings{garetto2025information,
     title = {Information Retrieval in the Age of Generative AI: The RGB Model},
     author = {Garetto, Michele and Cornacchia, Alessandro and Galante, Franco and Leonardi, Emilio and Nordio, Alessandro and Tarable, Alberto},
     booktitle = {Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval},
     pages = {602--612},
     year = {2025},
   }

3. arXiv

   ChamaleoNet: Programmable Passive Probe for Enhanced Visibility on Erroneous Traffic

   Zhihao Wang, Alessandro Cornacchia, Andrea Bianco, Idilio Drago, Paolo Giaccone, Dingde Jiang, and Marco Mellia

   arXiv preprint arXiv:2508.12496, 2025

   Bib

   @article{wang2025chamaleonet,
     title = {ChamaleoNet: Programmable Passive Probe for Enhanced Visibility on Erroneous Traffic},
     author = {Wang, Zhihao and Cornacchia, Alessandro and Bianco, Andrea and Drago, Idilio and Giaccone, Paolo and Jiang, Dingde and Mellia, Marco},
     journal = {arXiv preprint arXiv:2508.12496},
     year = {2025},
   }

4. ACM APSys

   

   Between Promise and Pain: The Reality of Automating Failure Analysis in Microservices with LLMs

   Alessandro Cornacchia, Iliyas Alabdulaal, Ibraheem Saghier, Albaraa Mirdad, Omar Fayoumi, and Marco Canini

   In Proceedings of the 16th ACM SIGOPS Asia-Pacific Workshop on Systems, 2025

   Abs Bib PDF Slides

   Large Language Models (LLMs) are increasingly explored as general-purpose assistants for infrastructure operations, helping automate tasks like querying data, analyzing logs, and suggesting fixes. In this paper, we consider the more general and ambitious problem of fully automating root cause analysis (RCA) in microservice systems, where LLMs must collect information, reason about it, and interact with the environment to detect, localize and resolve issues. Anecdotal evidence offers useful insights and partial solutions, but the broader challenge remains unresolved. We systematically evaluate multiple LLM agent architectures across a range of incident scenarios. We study how different tool-augmented agents perform, and shed light on common failure modes, including hallucinated reasoning paths and inefficient use of context. Our findings reveal both the promise and the limitations of current approaches, and point to concrete directions for more robust and effective use of LLMs in this domain.

   @inproceedings{cornacchia2025between,
     title = {Between Promise and Pain: The Reality of Automating Failure Analysis in Microservices with LLMs},
     author = {Cornacchia, Alessandro and Alabdulaal, Iliyas and Saghier, Ibraheem and Mirdad, Albaraa and Fayoumi, Omar and Canini, Marco},
     booktitle = {Proceedings of the 16th ACM SIGOPS Asia-Pacific Workshop on Systems},
     pages = {155--167},
     year = {2025},
   }

5. ACM IMC

   Poster: The Potential of Erroneous Outbound Traffic Analysis to Unveil Silent Internal Anomalies

   Andrea Sordello, Zhihao Wang, Kai Huang, Alessandro Cornacchia, and Marco Mellia

   In Proceedings of the 2025 ACM Internet Measurement Conference, 2025

   Bib

   @inproceedings{sordello2025poster,
     title = {Poster: The Potential of Erroneous Outbound Traffic Analysis to Unveil Silent Internal Anomalies},
     author = {Sordello, Andrea and Wang, Zhihao and Huang, Kai and Cornacchia, Alessandro and Mellia, Marco},
     booktitle = {Proceedings of the 2025 ACM Internet Measurement Conference},
     pages = {1078--1079},
     year = {2025},
   }

6. arXiv

   DMAS-Forge: A Framework for Transparent Deployment of AI Applications as Distributed Systems

   Alessandro Cornacchia, Vaastav Anand, Muhammad Bilal, Zafar Qazi, and Marco Canini

   arXiv preprint arXiv:2510.11872, 2025

   Bib

   @article{cornacchia2025dmas,
     title = {DMAS-Forge: A Framework for Transparent Deployment of AI Applications as Distributed Systems},
     author = {Cornacchia, Alessandro and Anand, Vaastav and Bilal, Muhammad and Qazi, Zafar and Canini, Marco},
     journal = {arXiv preprint arXiv:2510.11872},
     year = {2025},
   }

7. IEEE NOMS

   Sharing GPUs and Programmable Switches in a Federated Testbed with SHARY

   Stefano Salsano, Andrea Mayer, Paolo Lungaroni, Pierpaolo Loreti, Lorenzo Bracciale, Andrea Detti, Marco Orazi, Paolo Giaccone, Fulvio Risso, Alessandro Cornacchia, and 1 more author

   In IEEE Network Operations and Management Symposium, 2025

   Bib

   @inproceedings{salsano2025sharing,
     title = {Sharing GPUs and Programmable Switches in a Federated Testbed with SHARY},
     author = {Salsano, Stefano and Mayer, Andrea and Lungaroni, Paolo and Loreti, Pierpaolo and Bracciale, Lorenzo and Detti, Andrea and Orazi, Marco and Giaccone, Paolo and Risso, Fulvio and Cornacchia, Alessandro and Chiasserini, Carla Fabiana},
     booktitle = {IEEE Network Operations and Management Symposium},
     pages = {1--5},
     year = {2025},
   }

8. arXiv

   

   A Network Arena for Benchmarking AI Agents on Network Troubleshooting

   Zhihao Wang, Alessandro Cornacchia, Alessio Sacco, Franco Galante, Marco Canini, and Dingde Jiang

   arXiv preprint arXiv:2512.16381, 2025

   Abs Bib PDF Code

   Agentic systems, powered by Large Language Models (LLMs), assist network engineers with network configuration synthesis and network troubleshooting tasks. For network troubleshooting, progress is hindered by the absence of standardized and accessible benchmarks for evaluating LLM agents in dynamic network settings at low operational effort. We present NIKA, the largest public benchmark to date for LLM-driven network incident diagnosis and troubleshooting. NIKA targets both domain experts and especially AI researchers alike, providing zero-effort replay of real-world network scenarios, and establishing well-defined agent-network interfaces for quick agent prototyping. NIKA comprises hundreds of curated network incidents, spanning five network scenarios, from data centers to ISP networks, and covers 54 representative network issues. Lastly, NIKA is modular and extensible by design, offering APIs to facilitate the integration of new network scenarios and failure cases. We evaluate state-of-the-art LLM agents on NIKA and find that while larger models succeed more often in detecting network issues, they still struggle to localize faults and identify root causes.

   @article{wang2025network,
     title = {A Network Arena for Benchmarking AI Agents on Network Troubleshooting},
     author = {Wang, Zhihao and Cornacchia, Alessandro and Sacco, Alessio and Galante, Franco and Canini, Marco and Jiang, Dingde},
     journal = {arXiv preprint arXiv:2512.16381},
     year = {2025},
   }

2024

1. IEEE HPSR

   A “Big-Spine” Abstraction: Flow Prioritization With Spatial Diversity in The Data Center Network

   Alessandro Cornacchia, Andrea Bianco, Paolo Giaccone, and German Sviridov

   In 2024 IEEE 25th International Conference on High Performance Switching and Routing (HPSR), 2024

   Bib

   @inproceedings{cornacchia2024big,
     title = {A “Big-Spine” Abstraction: Flow Prioritization With Spatial Diversity in The Data Center Network},
     author = {Cornacchia, Alessandro and Bianco, Andrea and Giaccone, Paolo and Sviridov, German},
     booktitle = {2024 IEEE 25th International Conference on High Performance Switching and Routing (HPSR)},
     pages = {43--48},
     year = {2024},
     organization = {IEEE},
   }

2023

1. ACM CoNEXT-SW

   MicroView: Cloud-Native Observability with Temporal Precision

   Alessandro Cornacchia, Theophilus A Benson, Muhammad Bilal, and Marco Canini

   In Proceedings of the on CoNEXT Student Workshop, 2023

   Bib

   @inproceedings{cornacchia2023microview,
     title = {MicroView: Cloud-Native Observability with Temporal Precision},
     author = {Cornacchia, Alessandro and Benson, Theophilus A and Bilal, Muhammad and Canini, Marco},
     booktitle = {Proceedings of the on CoNEXT Student Workshop},
     pages = {7--8},
     year = {2023},
   }

2022

1. ComCom

   Staggered HLL: Near-continuous-time cardinality estimation with no overhead

   Alessandro Cornacchia, Giuseppe Bianchi, Andrea Bianco, and Paolo Giaccone

   Computer Communications, 2022

   Bib

   @article{cornacchia2022staggered,
     title = {Staggered HLL: Near-continuous-time cardinality estimation with no overhead},
     author = {Cornacchia, Alessandro and Bianchi, Giuseppe and Bianco, Andrea and Giaccone, Paolo},
     journal = {Computer Communications},
     volume = {193},
     pages = {168--175},
     year = {2022},
     publisher = {Elsevier},
   }

2. IEEE PEMWN

   Designing Probabilistic Flow Counting over Sliding Windows

   Alessandro Cornacchia, Giuseppe Bianchi, Andrea Bianco, and Paolo Giaccone

   In 2022 IEEE 11th IFIP International Conference on Performance Evaluation and Modeling in Wireless and Wired Networks (PEMWN), 2022

   Bib

   @inproceedings{cornacchia2022designing,
     title = {Designing Probabilistic Flow Counting over Sliding Windows},
     author = {Cornacchia, Alessandro and Bianchi, Giuseppe and Bianco, Andrea and Giaccone, Paolo},
     booktitle = {2022 IEEE 11th IFIP International Conference on Performance Evaluation and Modeling in Wireless and Wired Networks (PEMWN)},
     pages = {1--6},
     year = {2022},
     organization = {IEEE},
   }

2021

1. IEEE MedComNet

   A traffic-aware perspective on network disaggregated sketches

   Alessandro Cornacchia, German Sviridov, Paolo Giaccone, and Andrea Bianco

   In 2021 19th Mediterranean Communication and Computer Networking Conference (MedComNet), 2021

   Bib

   @inproceedings{cornacchia2021traffic,
     title = {A traffic-aware perspective on network disaggregated sketches},
     author = {Cornacchia, Alessandro and Sviridov, German and Giaccone, Paolo and Bianco, Andrea},
     booktitle = {2021 19th Mediterranean Communication and Computer Networking Conference (MedComNet)},
     pages = {1--4},
     year = {2021},
     organization = {IEEE},
   }