Abstract
Correlated failures that usually involve a number of nodes failing simultaneously have significant effect on systems' availability, especially for streaming applications that require real-Time analysis. Most state-of-The-Art distributed stream processing engines focus on recovering individual operator failure. By analyzing the existing recovery techniques, we identify the challenges and propose a fault-Tolerance framework that can tolerate both individual and correlated failures with minimum overhead during the system's normal execution. Our progressive and query-centric recovery paradigm carefully schedules the recovery of failed operators based on the current availability of resources, such that the outputs of queries can be recovered as early as possible. We also formulate the new problem of recovery scheduling under correlated failures and design algorithms to optimize the recovery latency with a performance guarantee.
| Original language | English |
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| Journal | Proceedings - International Conference on Data Engineering |
| Volume | 2018 |
| Pages (from-to) | 1280-1283 |
| Number of pages | 4 |
| ISSN | 1084-4627 |
| DOIs | |
| Publication status | Published - 24 Oct 2018 |
| Event | 34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, France Duration: 16 Apr 2018 → 19 Apr 2018 |
Conference
| Conference | 34th IEEE International Conference on Data Engineering, ICDE 2018 |
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| Country/Territory | France |
| City | Paris |
| Period | 16/04/2018 → 19/04/2018 |
Keywords
- Correlated Failure
- Distributed Stream Processing
- Fault Tolerance