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Managing parallelism for stream processing in the cloud
| Content Provider | ACM Digital Library |
|---|---|
| Author | Backman, Nathan Fonseca, Rodrigo Çetintemel, Uǧur |
| Abstract | Stream processing applications run continuously and have varying load. Cloud infrastructures present an attractive option to meet these fluctuating computational demands. Coordinating such resources to meet end-to-end latency objectives efficiently is important in preventing the frivolous use of cloud resources. We present a framework that parallelizes and schedules workflows of stream operators, in real-time, to meet latency objectives. It supports data- and task-parallel processing of all workflow operators, by all computing nodes, while maintaining the ordering properties of sorted data streams. We show that a latency-oriented operator scheduling policy coupled with the diversification of computing node responsibilities encourages parallelism models that achieve end-to-end latency-minimization goals. We demonstrate the effectiveness of our framework with preliminary experimental results using a variety of real-world applications on heterogeneous clusters. |
| Starting Page | 1 |
| Ending Page | 5 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781450311625 |
| DOI | 10.1145/2169090.2169091 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-04-10 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Parallelism management Heterogeneous clusters Stream processing |
| Content Type | Text |
| Resource Type | Article |
