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LOCATION:601
DTSTART;TZID=America/Denver:20191118T103000
DTEND;TZID=America/Denver:20191118T105500
UID:submissions.supercomputing.org_SC19_sess121_ws_pdsw102@linklings.com
SUMMARY:In Search of a Fast and Efficient Serverless DAG Engine
DESCRIPTION:Workshop\n\nIn Search of a Fast and Efficient Serverless DAG E
 ngine\n\nCarver, Zhang, Wang, Cheng\n\nPython-written data analytics appli
 cations can be modeled as and compiled into a directed acyclic graph (DAG)
  based workflow, where the nodes are fine-grained tasks and the edges are 
 task dependencies.  Such analytics workflow jobs are increasingly characte
 rized by short, fine-grained tasks with large fan-outs. These characterist
 ics make them well-suited for a new cloud computing model called serverles
 s computing or Function-as-a-Service (FaaS), which has become prevalent in
  recent years. The auto-scaling property of serverless computing platforms
  accommodates short tasks and bursty workloads, while the pay-per-use bill
 ing model of serverless computing providers keeps the cost of short tasks 
 low. \n\nIn this paper, we thoroughly investigate the problem space of DAG
  scheduling in serverless computing. We identify and evaluate a set of tec
 hniques to make DAG schedulers serverless-aware. These techniques have bee
 n implemented in WUKONG, a serverless, DAG scheduler attuned to AWS Lambda
 . WUKONG provides decentralized scheduling through a combination of static
  and dynamic scheduling. We present the results of an empirical study in w
 hich WUKONG is applied to a range of microbenchmark and real-world DAG app
 lications. Results demonstrate the efficacy of WUKONG in minimizing the pe
 rformance overhead introduced by AWS Lambda — WUKONG achieves competitive 
 performance compared to a serverful DAG scheduler, while improving the per
 formance of real-world DAG jobs by as much as 3.1× at larger scale.\n\nTag
 : Workshop Reg Pass, Big Data, Data Analytics, Data Management, Storage\n\
 nRegistration Category: Workshop Reg Pass, Big Data, Data Analytics, Data 
 Management, Storage
URL:https://sc19.supercomputing.org/presentation/?id=ws_pdsw102&sess=sess1
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