  Distributed Complex Event Processing has emerged as a well-established
paradigm to detect situations of interest from basic sensor streams, building
an operator graph between sensors and applications. In order to detect event
patterns that correspond to situations of interest, each operator correlates
events on its incoming streams according to a sliding window mechanism. To
increase the throughput of an operator, different windows can be assigned to
different operator instances---i.e., identical operator copies---which process
them in parallel. This implies that events that are part of multiple
overlapping windows are replicated to different operator instances. The
communication overhead of replicating the events can be reduced by assigning
overlapping windows to the same operator instance. However, this imposes a
higher processing load on the single operator instance, possibly overloading
it. In this paper, we address the trade-off between processing load and
communication overhead when assigning overlapping windows to a single operator
instance. Controlling the trade-off is challenging and cannot be solved with
traditional reactive methods. To this end, we propose a model-based batch
scheduling controller building on prediction. Evaluations show that our
approach is able to significantly save bandwidth, while keeping a user-defined
latency bound in the operator instances.
