Name
Abstract | ||
|---|---|---|
Fault tolerance is a property which needs deeper consideration when dealing with streaming jobs requiring high levels of availability and low-latency processing even in case of failures where Quality-of-Service constraints must be adhered to. Typically, systems achieve fault tolerance and the ability to recover automatically from partial failures by implementing Checkpoint and Rollback Recovery. However, this is an expensive operation which impacts negatively on the overall performance of the system and manually optimizing fault tolerance for specific jobs is a difficult and time consuming task.In this paper we introduce Chiron, an approach for automatically optimizing the frequency with which checkpoints are performed in streaming jobs. For any chosen job, parallel profiling runs are performed, each containing a variant of the configurations, with the resulting metrics used to model the impact of checkpoint-based fault tolerance on performance and availability. Understanding these relationships is key to minimizing performance objectives and meeting strict Quality-of-Service constraints. We implemented Chiron prototypically together with Apache Flink and demonstrate its usefulness experimentally. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/BigData50022.2020.9378474 | 2020 IEEE International Conference on Big Data (Big Data) |
Keywords | DocType | ISSN |
Distributed Stream Processing,Fault Tolerance,Profiling,Performance Modeling,Quality of Service | Conference | 2639-1589 |
ISBN | Citations | PageRank |
978-1-7281-6252-2 | 2 | 0.38 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Morgan Geldenhuys | 1 | 2 | 0.38 |
| Lauritz Thamsen | 2 | 43 | 9.26 |
| Odej Kao | 3 | 1066 | 96.19 |