Data stream processing (DSP) has emerged over the years as the reference paradigm for
the analysis of continuous and fast information flows, which often have to be processed with …

Cloud resource provisioning is a challenging job that may be compromised due to
unavailability of the expected resources. Quality of Service (QoS) requirements of workloads …

Distributed stream processing frameworks have gained widespread adoption in the last
decade because they abstract away the complexity of parallel processing. One of their key …

In this tutorial we present the results of recent research about the cloud enablement of data
streaming systems. We illustrate, based on both industrial as well as academic prototypes …

Cloud computing evolved from the concept of utility computing, which is defined as the
provision of computational and storage resources as a metered service. Another key …

Fault-tolerance techniques for stream processing engines can be categorized into passive
and active approaches. A typical passive approach periodically checkpoints a processing …

Abstract Rise of Big Data techniques has led to the requirement for low latency analysis of
high-velocity continuous data streams in real time. Several solutions, including Stream …

Abstract Distributed Stream Processing Engine (DSPE) is designed for processing
continuous streams so as to achieve the real-time performance with low latency guaranteed …

Fault-tolerance techniques for stream processing engines can be categorized into passive
and active approaches. However, both approaches have their own inadequacies in …

Stream processing has emerged as a useful technology for applications which require
continuous and low latency computation on infinite streaming data. Since stream processing …