ABSTRACT
Task migration happens when distributed data processing systems scale in real-time. To handle the task migration process more gracefully, we propose three task migration methods: (i) worker level migration, (ii) executor level migration, and (iii) executor level migration with reliable messaging. We implement our migration methods on Apache Storm. Our experiments show that, compared with Storm's original migration implementation, our methods significantly reduce the performance degradation and the number of task failures during each migration.
- Apache storm. http://storm.apache.org.Google Scholar
- R. Castro Fernandez, M. Migliavacca, E. Kalyvianaki, and P. Pietzuch. Integrating scale out and fault tolerance in stream processing using operator state management. In SIGMOD 2013, pages 725--736. ACM, 2013. Google ScholarDigital Library
- T. Heinze, Z. Jerzak, G. Hackenbroich, and C. Fetzer. Latency-aware elastic scaling for distributed data stream processing systems. In DEBS 2014, pages 13--22. ACM, 2014. Google ScholarDigital Library
- S. Schneider, H. Andrade, B. Gedik, A. Biem, and K.-L. Wu. Elastic scaling of data parallel operators in stream processing. In IPDPS 2009, pages 1--12, May 2009. Google ScholarDigital Library
Index Terms
- Smooth Task Migration in Apache Storm
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