NDLI: "All roads lead to Rome": optimistic recovery for distributed iterative data processing

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"All roads lead to Rome": optimistic recovery for distributed iterative data processing

Content Provider	ACM Digital Library
Author	Tzoumas, Kostas Ewen, Stephan Schelter, Sebastian Markl, Volker
Abstract	Executing data-parallel iterative algorithms on large datasets is crucial for many advanced analytical applications in the fields of data mining and machine learning. Current systems for executing iterative tasks in large clusters typically achieve fault tolerance through rollback recovery. The principle behind this pessimistic approach is to periodically checkpoint the algorithm state. Upon failure, the system restores a consistent state from a previously written checkpoint and resumes execution from that point. We propose an optimistic recovery mechanism using algorithmic compensations. Our method leverages the robust, self-correcting nature of a large class of fixpoint algorithms used in data mining and machine learning, which converge to the correct solution from various intermediate consistent states. In the case of a failure, we apply a user-defined compensate function that algorithmically creates such a consistent state, instead of rolling back to a previous checkpointed state. Our optimistic recovery does not checkpoint any state and hence achieves optimal failure-free performance with respect to the overhead necessary for guaranteeing fault tolerance. We illustrate the applicability of this approach for three wide classes of problems. Furthermore, we show how to implement the proposed optimistic recovery mechanism in a data flow system. Similar to the Combine operator in MapReduce, our proposed functionality is optional and can be applied to increase performance without changing the semantics of programs. In an experimental evaluation on large datasets, we show that our proposed approach provides optimal failure-free performance. In the absence of failures our optimistic scheme is able to outperform a pessimistic approach by a factor of two to five. In presence of failures, our approach provides fast recovery and outperforms pessimistic approaches in the majority of cases.
Starting Page	1919
Ending Page	1928
Page Count	10
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505753
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Iterative algorithms Optimistic recovery Fault-tolerance
Content Type	Text
Resource Type	Article

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