|Bibliography||Mayer, Ruben; Koldehofe, Boris; Rothermel, Kurt: Meeting Predictable Buffer Limits in the Parallel Execution of Event Processing Operators. |
In: Proceedings of the 2014 IEEE International Conference on Big Data, BigData '14.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology.
pp. 402-411, english.
IEEE, October 27, 2014.
Article in Proceedings (Conference Paper).
|CR-Schema||C.2.4 (Distributed Systems)|
C.4 (Performance of Systems)
|Keywords||Complex Event Processing, Stream Processing, Data Parallelization, Self-Adaptation, Quality of Service|
Complex Event Processing (CEP) systems enable applications to react to live-situations by detecting event patterns (complex events) in data streams. With the increasing number of data sources and the increasing volume at which data is produced, parallelization of event detection is becoming of tremendous importance to limit the time events need to be buffered before they actually can be processed by an event detector—named event processing operator. In this paper, we propose a pattern-sensitive partitioning model for data streams that is capable of achieving a high degree of parallelism for event patterns which formerly could only be consistently detected in a sequential manner or at a low parallelization degree. Moreover, we propose methods to dynamically adapt the parallelization degree to limit the buffering imposed on event detection in the presence of dynamic changes to the workload. Extensive evaluations of the system behavior show that the proposed partitioning model allows for a high degree of parallelism and that the proposed adaptation methods are able to meet the buffering level for event detection under high and dynamic workloads.
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|Copyright||© 2014 IEEE |
|Department(s)||University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems|
|Entry date||October 1, 2014|