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 …

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) …

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 …

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 …

Parallel computing is very important to accelerate the performance of software systems.
Additionally, considering that a recurring challenge is to process high data volumes …

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 …

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

Distributed stream processing systems experience highly variable workloads. This presents
a challenge when provisioning compute to meet the needs of these workloads. Rightsizing …

Within window-based streaming applications, a longer window size results in the
accumulation of a greater number of states, and this has been observed to affect the …