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DTSTART;TZID=America/Denver:20231112T145500
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UID:submissions.supercomputing.org_SC23_sess435_ws_pdswwip107@linklings.co
 m
SUMMARY:Toward a Peer-to-Peer Data Distribution Layer for Efficient and Co
 llaborative Resource Optimization of Distributed Dataflow Applications
DESCRIPTION:Dominik Scheinert, Soeren Becker, Jonathan Will, and Luis Engl
 aender (Technical University of Berlin) and Lauritz Thamsen (University of
  Glasgow)\n\nOptimizing the underlying cluster configurations of distribut
 ed data processing frameworks can be complex and often requires performanc
 e modeling techniques due to the multitude of performance-affecting factor
 s.  While these approaches may not always be applicable due to the need fo
 r substantial training data, at the same time, data analytics jobs oftenti
 mes share common characteristics, such as algorithm implementations, which
  suggest the potential for collaborative performance modeling.  Current co
 llaborative approaches, however, mainly assume a centralized storage infra
 structure, which comes with its own potential drawbacks, i.e., with regard
  to data privacy, storage costs, or system maintenance.  We envision a pee
 r-to-peer-based data distribution layer, facilitating data sovereignty, fa
 ilure resilience, and means of ad-hoc collaboration, thereby fostering cro
 ss-context resource optimization approaches for big data analytics.\n\nTag
 : Data Analysis, Visualization, and Storage, Data Movement and Memory\n\nR
 egistration Category: Workshop Reg Pass\n\nSession Chairs: Suren Byna (The
  Ohio State University, Lawrence Berkeley National Laboratory (LBNL)); Jay
  Lofstead (Sandia National Laboratories, University of New Mexico); Bing X
 ie (Microsoft Corporation); and Amelie Chi Zhou (Hong Kong Baptist Univers
 ity)\n\n
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