Stream processing systems must handle stream data coming from real-time, high-throughput applications, for example in financial trading. Timely processing of streams is important and requires sufficient available resources to achieve high throughput and deliver accurate results. However, static allocation of stream processing resources in terms of machines is inefficient when input streams have significant rate variations— machines remain underutilised for long periods of average load.

We present a combined stream processing system that, as the input stream rate varies, adaptively balances workload between a dedicated local stream processor and a cloud stream processor. This approach only utilises cloud machines when the local stream processor becomes overloaded. We evaluate a prototype system with financial trading data. Our results show that it can adapt effectively to workload variations, while only discarding a small percentage of input data.