With the increasing stringent QoS constraints (eg, latency, bandwidth, jitter) imposed by
novel applications (eg, e-Health, autonomous vehicles, smart cities, etc.), as well as the …

Nowadays, many communities are emerging towards smart environments, requiring the
communication and collaboration of diverse Internet-of-Things (IoT) devices. A smart …

Abstract Existing Data Stream Processing (DSP) systems perform poorly while encountering
heavy workloads, particularly on clustered set of (heterogeneous) computers. Elasticity and …

Stream processing has been gaining extensive attention in the past few years. Apache Flink
is a new generation of distributed stream processing engines that can process a great deal …

As one of the most computationally intensive operations in stream processing applications,
join operation can cause severe load imbalance problem when dealing with skewed data …

Apache Flink has become one of the highly regarded streaming computing frameworks with
its excellent advantages of high throughput, low latency, and high reliability. However, the …

The complexity and variability of streaming data have brought a great challenge to the
elasticity of the data processing systems. Streaming systems, such as Flink and Storm, need …

IoT-Edge-Fog Computing presents a trio-logical model for decentralized computing in a time-
sensitive manner. However, to address the rising need for real-time information processing …

Stream processing systems commonly work with auto-scaling to ensure resource efficiency
and quality of service (QoS). Existing auto-scaling solutions lack accuracy in resource …

Stream processing is a new memory computing paradigm that deals with dynamic data
streams efficiently. Storm is one of the stream processing frameworks, but the default stream …