Abstract:
Complex Event Processing (CEP) is a well-known technology in real-time Big Data processing systems. Performance of CEP engines is expected to scale with ever-increasing data rates and complex use cases. CEP operators like stream join and event patterns involve high computational complexity; hence, have a considerable impact on the overall query processing performance. Distributed event processing and CPU-level
parallel event processing algorithms are common approaches for improving the performance. We explore how commodity massively parallel architectures like modern Graphics Processing Units (GPUs) can be utilized to improve the performance of frequently used CEP operators. We demonstrate how CEP operators such as event filter, event window, and stream join can be redesigned and implemented on GPUs to gain an
order of magnitude improvement in throughput compared to a CPU-based mplementation. This work is demonstrated using NVIDIA CUDA based implementation of CEP operators for Siddhi CEP engine on low-end GPUs. Moreover, this approach reduces event queuing at the incoming event queue, even with a large number of event streams, high arrival rates, and several complex queries. Consequently, the average latency experienced by incoming events is also reduced.
