Adaptive Fault-Tolerance for Dynamic Resource Provisioning in Distributed Stream Processing Systems

A growing number of applications require continuous processing of high-throughput data streams, e.g., financial analysis, network traffic monitoring, or Big Data analytics for smart cities. Stream processing applications typically require specific quality-of-service levels to achieve their goals; yet, due to the high time-variability of stream characteristics, it is often inefficient to statically allocate the resources needed to guarantee application Service Level Agreements (SLAs). In this paper, we present LAAR, a novel method for adaptive replication that trades fault tolerance for increased capacity during load spikes. We have implemented and validated LAAR as a middleware layer on top of IBM InfoSphere Streams. We have performed a wide set of experiments on an industrial-quality 60-core cluster deployment and we show that, under the assumption of only statistical knowledge of streams load distribution, LAAR can reduce resource consumption while guaranteeing an upper-bound on information loss in case of failures.