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DTSTAMP:20210402T160551Z
LOCATION:Track 2
DTSTART;TZID=America/New_York:20201118T150000
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UID:submissions.supercomputing.org_SC20_sess146@linklings.com
SUMMARY:Containers and Serverless Computing
DESCRIPTION:Paper\n\nBATCH:  Machine Learning Inference Serving on Serverl
 ess Platforms with Adaptive Batching\n\nAli, Pinciroli, Yan, Smirni\n\nSer
 verless computing is a new pay-per-use cloud service paradigm that automat
 es resource scaling for stateless functions and can potentially facilitate
  bursty machine learning serving. Batching is critical for latency perform
 ance and cost-effectiveness of machine learning inference, but unfortunate
 l...\n\n---------------------\nWaiting Game: Optimally Provisioning Fixed 
 Resources for Cloud-Enabled Schedulers\n\nAmbati, Bashir, Irwin, Shenoy\n\
 nWhile cloud platforms enable users to rent computing resources on demand 
 to execute their jobs, buying fixed resources is still much cheaper than r
 enting if utilization is high. Optimizing cloud costs requires users to de
 termine how many fixed resources to buy versus rent based on their workloa
 d. In...\n\n---------------------\nMetis: Learning to Schedule Long-Runnin
 g Applications in Shared Container Clusters at Scale\n\nWang, Weng, Wang, 
 Chen, Li\n\nOnline cloud services are deployed as long-running application
 s (LRAs) in containers. Scheduling LRA containers is known to be difficult
  as they often have sophisticated resource interferences and I/O dependenc
 ies. Existing schedulers rely on placement constraints and thus fall short
  in performance....\n\n\nTag: Cloud and Distributed Computing, Containers,
  Machine Learning, Deep Learning and Artificial Intelligence, Resource Man
 agement and Scheduling\n\nRegistration Category: Tech Program Reg Pass
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