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Fault-Tolerance for Distributed Iterative Dataflows in Action" Accepted for Publication in PVLDB and for Demonstration at VLDB 2018

We are pleased to announce that the demonstration proposal "Fault-Tolerance for Distributed Iterative Dataflows in Action," by Chen Xu, Rudi Poepsel Lemaitre, Juan Soto, and Volker Markl has been accepted for publication in Issue 12 of PVLDB and for demonstration at the VLDB 2018 Conference, which will be held in Rio de Janeiro, Brazil, from August 27 through August 31, 2018.

Abstract : Distributed dataflow systems (DDS) are widely employed in graph processing and machine learning (ML), where many of these algorithms are iterative in nature. Typically, DDS achieve fault-tolerance using checkpointing mechanisms or they exploit algorithmic properties to enable fault-tolerance without the need for checkpoints. Recently, for graph processing, we proposed utilizing unblocking checkpointing, to parallelize the execution pipeline and checkpoint writing, as well as confined recovery, to enable fast recovery upon partial node failures. Furthermore, for ML algorithms implemented using broadcast variables, we proposed utilizing replica recovery, to leverage broadcast variable replicas and facilitate failure recovery checkpointing-free. In this demonstration, we showcase these fault-tolerance techniques using Apache Flink. Attendees will be able to: (i) run representative iterative algorithms including PageRank, Connected Components, and K-Means, (ii) explore the internal behavior of DDS under the influence of unblocking checkpointing, and (iii) trigger failures, to observe the effects of confined recovery and replica recovery.


For more information about the conference visit: VLDB 2018 Website.