Snap: a microkernel approach to host networking

Snap: a microkernel approach to host networking Marty et al., SOSP'19 This paper describes the networking stack, Snap, that has been running in production at Google for the last three years+. It’s been clear for a while that software designed explicitly for the data center environment will increasingly want/need to make different design trade-offs to … Continue reading Snap: a microkernel approach to host networking

The inflection point hypothesis: a principled approach to finding the root cause of a failure

The inflection point hypothesis: a principled debugging approach for locating the root cause of a failure Zhang et al., SOSP'19 It’s been a while since we looked a debugging and troubleshooting on The Morning Paper (here’s a sample of earlier posts on the topic). Today’s paper introduces a root cause of failure detector for those … Continue reading The inflection point hypothesis: a principled approach to finding the root cause of a failure

File systems unfit as distributed storage backends: lessons from ten years of Ceph evolution

File systems unfit as distributed storage backends: lessons from 10 years of Ceph evolution Aghayev et al., SOSP'19 Ten years of hard-won lessons packed into just 17 pages (13 if you don’t count the references!) makes this paper extremely good value for your time. It’s also a fabulous example of recognising and challenging implicit assumptions. … Continue reading File systems unfit as distributed storage backends: lessons from ten years of Ceph evolution

An analysis of performance evolution of Linux’s core operations

An analysis of performance evolution of Linux’s core operations Ren et al., SOSP'19 I was drawn in by the headline results here: This paper presents an analysis of how Linux’s performance has evolved over the past seven years... To our surprise, the study shows that the performance of many core operations has worsened or fluctuated … Continue reading An analysis of performance evolution of Linux’s core operations

Learning certifiably optimal rule lists for categorical data

Learning certifiably optimal rule lists for categorical data Angelino et al., JMLR 2018 Today we’re taking a closer look at CORELS, the Certifiably Optimal RulE ListS algorithm that we encountered in Rudin’s arguments for interpretable models earlier this week. We’ve been able to create rule lists (decision trees) for a long time, e.g. using CART, … Continue reading Learning certifiably optimal rule lists for categorical data

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead Rudin et al., arXiv 2019 With thanks to Glyn Normington for pointing out this paper to me. It’s pretty clear from the title alone what Cynthia Rudin would like us to do! The paper is a mix of technical … Continue reading Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead