David Morrison, Yelp
Apache Mesos is a powerful tool for running workloads across a distributed cluster of machines. It has powered Yelp’s production infrastructure since 2014 and runs an increasing variety of workloads, from traditional stateless services to batch and machine learning workloads. In this talk we describe how we use data and analytics to push Mesos to the limits, while providing a better experience for our users and our developers.
David Morrison, Yelp
David R. Morrison is a software engineer working in scheduling and optimization on the Distributed Systems team at Yelp, where he has developed autoscaling code for Yelp’s most expensive compute clusters. Previously, David worked in research and development at Inverse Limit, where he received federal funding from DARPA and Google’s ATAP program. David received his PhD in computer science from the University of Illinois, Urbana-Champaign under the supervision of Dr. Sheldon Jacobson. David has spoken at the INFORMS Business Analytics conference in 2017, at AWS re:Invent 2016, and given multiple presentations at the INFORMS Annual Meetings and other venues.
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author = {David Morrison},
title = {Getting the Most Out of Your Mesos},
year = {2018},
address = {Nashville, TN},
publisher = {USENIX Association},
month = oct
}