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DTSTART;TZID=America/Chicago:20211116T133000
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UID:submissions.supercomputing.org_SC21_sess148@linklings.com
SUMMARY:Cloud and Edge Computing
DESCRIPTION:Paper\n\nUnderstanding, Predicting and Scheduling Serverless W
 orkloads Under Partial Interference\n\nZhao, Yang, Li, Zhou, Li\n\nInterfe
 rence among distributed cloud applications can be classified into three ty
 pes:  full, partial and zero. While prior research merely focused on full 
 interference, the partial interference that occurs at parts of application
 s is far more common yet still lacks in-depth study. Serverless computi...
 \n\n---------------------\nThe Hidden Cost of the Edge: A Performance Comp
 arison of Edge and Cloud Latencies\n\nAli-Eldin, Wang, Shenoy\n\nEdge comp
 uting has emerged as a popular paradigm due to its ability to offer lower 
 network latencies to end-users. In this paper, we argue that despite its l
 ower network latency, the resource-constrained nature of the edge can resu
 lt in higher end-to-end latency, especially at higher utilizations, c...\n
 \n---------------------\nRibbon: Cost-Effective and QoS-Aware Deep Learnin
 g Model Inference Using a Diverse Pool of Cloud Computing Instances\n\nLi,
  Roy, Patel, Gadepally, Gettings...\n\nDeep learning model inference is a 
 key service in many businesses and scientific discovery processes. This pa
 per introduces Ribbon, a novel deep learning inference serving system that
  meets two competing objectives: quality-of-service (QoS) target and cost-
 effectiveness. The key idea behind Ribbon i...\n\n\nTag: Resource Manageme
 nt and Scheduling\n\nRegistration Category: Tech Program Reg Pass
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