Gandalf: an intelligent, end-to-end analytics service for safe deployment in cloud-scale infrastructure, Li et al., NSDI'20 Modern software systems at scale are incredibly complex ever changing environments. Despite all the pre-deployment testing you might employ, this makes it really tough to change them with confidence. Thus it's common to use some form of phased rollout, … Continue reading Gandalf: an intelligent, end-to-end analytics service for safe deployment in cloud-scale infrastructure
Month: February 2020
Meaningful availability
Meaningful availability, Hauer et al., NSDI'20 With thanks to Damien Mathieu for the recommendation. This very clearly written paper describes the Google G Suite team's search for a meaningful availability metric: one that accurately reflected what their end users experienced, and that could be used by engineers to pinpoint issues and guide improvements. > A … Continue reading Meaningful availability
AnyLog: a grand unification of the Internet of things
AnyLog: a grand unification of the Internet of Things, Abadi et al., CIDR'20 The Web provides decentralised publishing and direct access to unstructured data (searching / querying that data has turned out to be a pretty centralised affair in practice though). AnyLog wants to do for structured (relational) data what the Web has done for … Continue reading AnyLog: a grand unification of the Internet of things
Extending relational query processing with ML inference
Extending relational query processing with ML inference, Karanasos, CIDR'10 This paper provides a little more detail on the concrete work that Microsoft is doing to embed machine learning inference inside an RDBMS, as part of their vision for Enterprise Grade Machine Learning. The motivation is not that inference will perform better inside the database, but … Continue reading Extending relational query processing with ML inference
Cloudy with a high chance of DBMS: a 10-year prediction for enterprise-grade ML
Cloudy with a high chance of DBMS: a 10-year prediction for enterprise-grade ML, Agrawal et al., CIDR'20 "Cloudy with a high chance of DBMS" is a fascinating vision paper from a group of experts at Microsoft, looking at the transition of machine learning from being primarily the domain of large-scale, high-volume consumer applications to being … Continue reading Cloudy with a high chance of DBMS: a 10-year prediction for enterprise-grade ML
Migrating a privacy-safe information extraction system to a Software 2.0 design
Migrating a privacy-safe information extraction system to a software 2.0 design, Sheng, CIDR'20 This is a comparatively short (7 pages) but very interesting paper detailing the migration of a software system to a 'Software 2.0' design. Software 2.0, in case you missed it, is a term coined by Andrej Karpathy to describe software in which … Continue reading Migrating a privacy-safe information extraction system to a Software 2.0 design
Programs, life cycles, and laws of software evolution
Programs, life cycles, and laws of software evolution, Lehman, Proc. IEEE, 1980 Today's paper came highly recommended by Kevlin Henney and Nat Pryce in a Twitter thread last week, thank you both! The footnotes show that the manuscript for this paper was submitted almost exactly 40 years ago - on the 27th February 1980. The … Continue reading Programs, life cycles, and laws of software evolution
Let’s Encrypt: an automated certificate authority to encrypt the entire web
Let's encrypt: an automated certificate authority to encrypt the entire web, Aas et al., CCS'19 This paper tells the story of Let's Encrypt, from it's early beginnings in 2012/13 all the way to becoming the world's largest HTTPS Certificate Authority (CA) today - accounting for more currently valid certificates than all other browser-trusted CAs combined. … Continue reading Let’s Encrypt: an automated certificate authority to encrypt the entire web
Watching you watch: the tracking system of over-the-top TV streaming devices
Watching you watch: the tracking ecosystem of over-the-top TV streaming devices, Moghaddam et al., CCS'19 The results from this paper are all too predictable: channels on Over-The-Top (OTT) streaming devices are insecure and riddled with privacy leaks. The authors quantify the scale of the problem, and note that users have even less viable defence mechanisms … Continue reading Watching you watch: the tracking system of over-the-top TV streaming devices
Cloudburst: stateful functions-as-a-service
Cloudburst: stateful functions-as-a-service, Sreekanti et al., arXiv 2020 Today's paper choice is a fresh-from-the-arXivs take on serverless computing from the RISELab at Berkeley, addressing some of the limitations outlined in last year's 'Berkeley view on serverless computing.' Stateless is fine until you need state, at which point the coarse-grained solutions offered by current platforms limit … Continue reading Cloudburst: stateful functions-as-a-service
the morning paper 