Firecracker: lightweight virtualization for serverless applications

Firecracker: lightweight virtualisation for serverless applications, Agache et al., NSDI'20 Finally the NSDI'20 papers have opened up to the public (as of last week), and what a great looking crop of papers it is. We looked at a couple of papers that had pre-prints available last week, today we'll be looking at one of the … Continue reading Firecracker: lightweight virtualization for serverless applications

Gandalf: an intelligent, end-to-end analytics service for safe deployment in cloud-scale infrastructure

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

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