Many" big data" applications must act on data in real time. Running these applications at
ever-larger scales requires parallel platforms that automatically handle faults and stragglers …

Stream processors are emerging in industry as an apparatus that drives analytical but also
mission critical services handling the core of persistent application logic. Thus, apart from …

The past few years have seen a major change in computing systems, as growing data
volumes and stalling processor speeds require more and more applications to scale out to …

The concept of state and its applications vary widely across big data processing systems.
This is evident in both the research literature and existing systems, such as Apache Flink …

We introduce ChronoStream, a distributed system specifically designed for elastic stateful
stream computation in the cloud. ChronoStream treats internal state as a first-class citizen …

In an edge deployment model, Internet-of-Things (IoT) applications, eg for building
automation or video surveillance, must process data locally on IoT devices without relying …

State-of-the-art distributed stream processing systems such as Apache Flink and Storm have
recently included checkpointing to provide fault-tolerance for stateful applications. This is a …

Guaranteed tuple processing has become critically important for many streaming
applications. This paper describes how we enabled IBM Streams, an enterprise-grade …

Fault tolerance is critical for distributed stream processing systems, yet achieving error-free
fault tolerance often incurs substantial performance overhead. We present AF-Stream, a …

This paper presents a low hardware overhead test pattern generator (TPG) for scan-based
BIST that can reduce switching activity in CUTs during BIST and also achieve very high fault …