Simone Ferlin

Simon Sundberg, Anna Brunstrom, Simone Ferlin-Reiter et al.

With networking moving into the sub-millisecond latency domain, latency in the end host itself can become a significant barrier to achieving consistently low application latency. Both the physical interconnect between the network card and the CPU, the kernel network stack, and the scheduling of applications themselves can be considerable sources of latency. Previous work has studied host latency at various levels, yet there remains a lack of methods and tools to continuously monitor host latency in production. To remedy this, we present netstacklat, a monitoring tool that captures latency at several points in the host network, from the early parts of the Linux kernel network stack all the way until the application reads the data. We evaluate netstacklat in a testbed, demonstrating its ability to capture host latency across 144 variations of HTTP workloads for Nginx and Apache, while also showing how the low monitoring overhead does not inflate tail latency by more than 6%, where previous monitoring solutions increase it by over 100%. Furthermore, we share our initial findings from deploying netstacklat in Cloudflare's global CDN network.

Daniele Cipollone, Changjie Wang, Mariano Scazzariello et al.

In today's digital landscape, the importance of timely and accurate vulnerability detection has significantly increased. This paper presents a novel approach that leverages transformer-based models and machine learning techniques to automate the identification of software vulnerabilities by analyzing GitHub issues. We introduce a new dataset specifically designed for classifying GitHub issues relevant to vulnerability detection. We then examine various classification techniques to determine their effectiveness. The results demonstrate the potential of this approach for real-world application in early vulnerability detection, which could substantially reduce the window of exploitation for software vulnerabilities. This research makes a key contribution to the field by providing a scalable and computationally efficient framework for automated detection, enabling the prevention of compromised software usage before official notifications. This work has the potential to enhance the security of open-source software ecosystems.

Armin Catovic, Carolyn Cartwright, Yasmin Tesfaldet Gebreyesus et al.

Logs are a common way to record detailed run-time information in software. As modern software systems evolve in scale and complexity, logs have become indispensable to understanding the internal states of the system. At the same time however, manually inspecting logs has become impractical. In recent times, there has been more emphasis on statistical and machine learning (ML) based methods for analyzing logs. While the results have shown promise, most of the literature focuses on algorithms and state-of-the-art (SOTA), while largely ignoring the practical aspects. In this paper we demonstrate our end-to-end log classification pipeline, Linnaeus. Besides showing the more traditional ML flow, we also demonstrate our solutions for adaptability and re-use, integration towards large scale software development processes, and how we cope with lack of labelled data. We hope Linnaeus can serve as a blueprint for, and inspire the integration of, various ML based solutions in other large scale industrial settings.