Tools and strategies for debugging distributed stream processing applications
Software—Practice & Experience
Design principles for developing stream processing applications
Software—Practice & Experience - Focus on Selected PhD Literature Reviews in the Practical Aspects of Software Technology
Odessa: enabling interactive perception applications on mobile devices
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
Elastic complex event processing
Proceedings of the 8th Middleware Doctoral Symposium
Virtualizing stream processing
Middleware'11 Proceedings of the 12th ACM/IFIP/USENIX international conference on Middleware
Adaptive task duplication using on-line bottleneck detection for streaming applications
Proceedings of the 9th conference on Computing Frontiers
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
Auto-parallelizing stateful distributed streaming applications
Proceedings of the 21st international conference on Parallel architectures and compilation techniques
Virtualizing stream processing
Proceedings of the 12th International Middleware Conference
Scheduling linear chain streaming applications on heterogeneous systems with failures
Future Generation Computer Systems
Integrating scale out and fault tolerance in stream processing using operator state management
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Adaptive input admission and management for parallel stream processing
Proceedings of the 7th ACM international conference on Distributed event-based systems
Enhancing throughput for streaming applications running on cluster systems
Journal of Parallel and Distributed Computing
DANBI: dynamic scheduling of irregular stream programs for many-core systems
PACT '13 Proceedings of the 22nd international conference on Parallel architectures and compilation techniques
A catalog of stream processing optimizations
ACM Computing Surveys (CSUR)
Semantic-based QoS management in cloud systems: Current status and future challenges
Future Generation Computer Systems
Active workflow system for near real-time extreme-scale science
Proceedings of the first workshop on Parallel programming for analytics applications
| Hi-index | 0.01 |
We describe an approach to elastically scale the performance of a data analytics operator that is part of a streaming application. Our techniques focus on dynamically adjusting the amount of computation an operator can carry out in response to changes in incoming workload and the availability of processing cycles. We show that our elastic approach is beneficial in light of the dynamic aspects of streaming workloads and stream processing environments. Addressing another recent trend, we show the importance of our approach as a means to providing computational elasticity in multicore processor-based environments such that operators can automatically find their best operating point. Finally, we present experiments driven by synthetic workloads, showing the space where the optimizing efforts are most beneficial and a radioastronomy imaging application, where we observe substantial improvements in its performance-critical section.