Abstract
Using imperative programming to process event streams, such as
those generated by multi-touch devices and 3D cameras, has significant
engineering drawbacks. Declarative approaches solve common problems but
so far, they have not been able to scale on multicore systems while providing
guaranteed response times.
We propose PARTE, a parallel scalable complex event processing engine
that allows for a declarative definition of event patterns and provides soft
real-time guarantees for their recognition. The proposed approach extends
the classical
Rete algorithm and maps event matching onto a graph of actor nodes.
Using a tiered event matching model, PARTE provides upper bounds on the
detection latency by relying on a combination of non-blocking
message passing between Rete nodes and safe memory management techniques.
The performance evaluation shows the scalability of our approach on up to
64 cores. Moreover, it indicates that PARTE's design choices lead to
more predictable performance compared to a PARTE variant without soft
real-time guarantees. Finally, the evaluation indicates further that gesture
recognition can benefit from the exposed parallelism with superlinear
speedups.
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