Designing far memory data structures: think outside the box

Designing far memory data structures: think outside the box Aguilera et al., HotOS'19 Last time out we looked at some of the trade-offs between RInKs and LInKs, and the advantages of local in-memory data structures. There’s another emerging option that we didn’t talk about there: the use of far-memory, memory attached to the network that … Continue reading Designing far memory data structures: think outside the box

Fast key-value stores: an idea whose time has come and gone

Fast key-value stores: an idea whose time has come and gone Adya et al., HotOS'19 No controversy here! Adya et al. would like you to stop using Memcached and Redis, and start building 11-factor apps. Factor VI in the 12-factor app manifesto, "Execute the app as one or more stateless processes," to be dropped and … Continue reading Fast key-value stores: an idea whose time has come and gone

Nines are not enough: meaningful metrics for clouds

Nines are not enough: meaningful metrics for clouds Mogul & Wilkes, HotOS'19 It’s hard to define good SLOs, especially when outcomes aren’t fully under the control of any single party. The authors of today’s paper should know a thing or two about that: Jeffrey Mogul and John Wilkes at Google1! John Wilkes was also one … Continue reading Nines are not enough: meaningful metrics for clouds

A case for managed and model-less inference serving

A case for managed and model-less inference serving Yadwadkar et al., HotOS'19 HotOS’19 is presenting me with something of a problem as there are so many interesting looking papers in the proceedings this year it’s going to be hard to cover them all! As a transition from the SysML papers we’ve been looking at recently, … Continue reading A case for managed and model-less inference serving

Beyond data and model parallelism for deep neural networks

Beyond data and model parallelism for deep neural networks Jia et al., SysML'2019 I’m guessing the authors of this paper were spared some of the XML excesses of the late nineties and early noughties, since they have no qualms putting SOAP at the core of their work! To me that means the "simple" object access … Continue reading Beyond data and model parallelism for deep neural networks