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SUMMARY:Massive Main Memory for the Masses
DTSTART:20180514T160000
DTSTAMP:20260408T101706Z
UID:8db91e22b82991056c65869ae99282c24a9074975ff86d7758f663ea
CATEGORIES:Conferences - Seminars
DESCRIPTION:Trends in DRAM scaling and new kernel-bypass-based networking 
 have given rise to fast disaggregated storage that can process millions of
  requests per second per server with just a few microseconds of delay. The
 se systems are fast in large part because they are dumb. Their stripped do
 wn data models make them easy to implement and optimize\, but several thin
 gs still limit the practicality of these systems. Simple data models force
  applications to "pay back" much of the performance they gain\, since appl
 ications have to make many round-trips to perform simple operations and si
 nce they must perform all processing client-side. Furthermore\, these syst
 ems are expensive\; typically\, smaller scale applications can't afford th
 eir own deployments of in-memory storage.  In this talk\, I will cover on
 going work from my lab that tackles these weakness to make low-latency in-
 memory storage more practical. Our efforts focus on resource provisioning 
 under massive multi-tenancy to make these types of systems amenable to clo
 ud-scale consolidation\, and programmability/extensibility that preserves 
 performance gains by minimizing isolation costs for tenant-provided in-sto
 rage functions.\n\nRyan Stutsman is an Assistant Professor at the Universi
 ty of Utah.  He received his Ph.D. from Stanford in 2013 working on the R
 AMCloud low-latency in-memory data center storage system. After that\, he 
 was a Postdoctoral Researcher in the Databases group at Microsoft Research
  where he worked on Deuteronomy\, a project to support highly scalable clo
 ud storage transactions.  Ryan's current work is funded by a recent NSF C
 AREER award and targets new inter-tenant isolation techniques for fast\, k
 ernel-bypass-based storage systems.
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420
STATUS:CONFIRMED
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