Grand Challenge: Scalable Stateful Stream Processing for Smart Grids

We describe a solution to the ACM DEBS Grand Challenge 2014, which evaluates event-based systems for smart grid analytics. Our solution follows the paradigm of stateful data stream processing and is implemented on top of the SEEP stream processing platform. It achieves high scalability by massive data-parallel processing and the option of performing semantic load-shedding. In addition, our solu-tion is fault-tolerant—the large processing state of stream operators is not lost after failure. Our experimental results show that our solution processes 1 month worth of data for 40 houses in 4 hours. When we scale out the system, the time reduces linearly to 30 minutes before the system bottle-necks at the data source. We then apply semantic load-shedding, maintaining a low median prediction error and reducing the time fur-ther to 17 minutes. The system achieves these results with median latencies below 30 ms and a 90th percentile below 50 ms. 1