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DTSTAMP:20181221T160731Z
LOCATION:C141/143/149
DTSTART;TZID=America/Chicago:20181114T140000
DTEND;TZID=America/Chicago:20181114T143000
UID:submissions.supercomputing.org_SC18_sess177_pap356@linklings.com
SUMMARY:Dynamically Negotiating Capacity Between On-Demand and Batch Clust
 ers
DESCRIPTION:Paper\nClouds and Distributed Computing, Resource Management, 
 Scheduling, Tech Program Reg Pass\n\nDynamically Negotiating Capacity Betw
 een On-Demand and Batch Clusters\n\nLiu, Keahey, Riteau, Weissman\n\nIn th
 e era of rapid experimental expansion data analysis needs are rapidly outp
 acing the capabilities of small institutional clusters and looking to inte
 grate HPC resources into their workflow. We propose one way of reconciling
  on-demand needs of experimental analytics with the batch managed HPC reso
 urces within a system that dynamically moves nodes between an on-demand cl
 uster configured with cloud technology (OpenStack) and a traditional HPC c
 luster managed by a batch scheduler (Torque). We evaluate this system expe
 rimentally both in the context of real-life traces representing two years 
 of a specific institutional need, and via experiments in the context of sy
 nthetic traces that capture generalized characteristics of potential batch
  and on-demand workloads. Our results for the real-life scenario show that
  our approach could reduce the current investment in on-demand infrastruct
 ure by 82% while at the same time improving the mean batch wait time almos
 t by an order of magnitude (8x).
URL:https://sc18.supercomputing.org/presentation/?id=pap356&sess=sess177
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