Tolerating Transient Late-Timing Faults in Cloud-Based Real-Time Stream Processing

Real-time stream processing is a frequently deployed application within Cloud datacenters that is required to provision high levels of performance and reliability. Numerous fault-tolerant approaches have been proposed to effectively achieve this objective in the presence of crash failures. However, such systems struggle with transient late-timing faults - a fault classification challenging to effectively tolerate - that manifests increasingly within large-scale distributed systems. Such faults represent a significant threat towards minimizing soft real-time execution of streaming applications in the presence of failures. This work proposes a fault-tolerant approach for QoS-aware data prediction to tolerate transient late-timing faults. The approach is capable of determining the most effective data prediction algorithm for imposed QoS constraints on a failed stream processor at run-time. We integrated our approach into Apache Storm with experiment results showing its ability to minimize stream processor end-to-end execution time by 61% compared to other fault-tolerant approaches. The approach incurs 12% additional CPU utilization while reducing network usage by 44%.