# Minimizing Communication Overhead in Window-Based Parallel Complex Event Processing

## TL;DR

This paper proposes a model-based batch scheduling approach to optimize the trade-off between communication overhead and processing load in distributed complex event processing, significantly reducing bandwidth while maintaining latency bounds.

## Contribution

It introduces a novel prediction-based scheduling controller that effectively balances load and communication in window-based parallel processing.

## Key findings

- Significant bandwidth savings achieved
- Maintains user-defined latency bounds
- Effective load balancing in parallel operators

## Abstract

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.