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UID:submissions.supercomputing.org_SC21_sess148_pap533@linklings.com
SUMMARY:Understanding, Predicting and Scheduling Serverless Workloads Unde
 r Partial Interference
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 computing 
 that structures applications into small-sized, short-lived functions furth
 er exacerbate partial interference. We characterize the features of partia
 l interference in serverless as exhibiting high volatility, spatial-tempor
 al variation, and propagation. Given these observations, we propose an inc
 remental learning predictor, named Gsight, which can achieve high precisio
 n by harnessing the spatial-temporal overlap codes and profiles of functio
 ns via an end-to-end call path. Experimental results show that Gsight can 
 achieve an average error of 1.71%. Its convergence speed is at least 3x fa
 ster than that in a serverful system. A scheduling case study shows that t
 he proposed method can improve function density by >18.79% while guarantee
 ing the quality of service (QoS).\n\nTag: Reproducibility Badge, Resource 
 Management and Scheduling\n\nRegistration Category: Tech Program Reg Pass\
 n\nAward Finalist: Best Paper Finalist\n\nReproducibility Badges: Artifact
  Available
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