Several software systems are built upon stream processing architectures to process large
amounts of data in near real-time. Today's distributed stream processing systems (DSPSs) …

This chapter provides students, industry experts, and researchers a high-level and
comprehensive overview of the end-to-end architectures of big data stream processing …

One of the most important issues in distributed data stream processing systems is using
operator migration to handle highly variable workloads cost-efficiently and adapt to the …

The article describes how digitalization and the wide diffusion of knowledge technologies
such as the Internet of (battlefield) things, big data, and artificial intelligence, are …

Nowadays, several software systems rely on stream processing architectures to deliver
scalable performance and handle large volumes of data in near real-time. Stream …

Latency or throughput is often critical performance metrics in stream processing.
Applications' performance can fluctuate depending on the input stream. This unpredictability …

Maximising the benefits of auto-scaling is difficult due to challenges associated with
precisely estimating resource usage in the face of significant variability in client workload …

Self-adaptation is a potential alternative to provide a higher level of autonomic abstractions
and run-time responsiveness in parallel executions. However, the recurrent problem is that …

GrPPI library aims to simplify the burdening task of parallel programming. It provides a
unified, abstract, and generic layer while promising minimal overhead on performance …

To maintain a stable Quality of Service (QoS), these systems require a sufficient allocation of
resources. At the same time, over-provisioning can result in wasted energy and high …