Recently, the concept of cloud computing has been extended towards the network edge.
Devices near the network edge, called fog nodes, offer computing capabilities with low …

The proliferation of Internet-of-Things (IoT) devices is rapidly increasing the demands for
efficient processing of low latency stream data generated close to the edge of the network …

The continual increase in the amount of generated data by social media, IoT devices, and
monitoring systems have motivated the use of Distributed Data Stream Processing (DSP) …

Federated Learning (FL) is a very promising approach for improving decentralized Machine
Learning (ML) models by exchanging knowledge between participating clients without …

A key feature of distributed stream processing (DSP) systems is the scheduling of operators
on clustered computers. In scheduling, the assignment plan of operators to nodes of the …

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 Internet of Things has enabled many application scenarios where a large number of
connected devices generate unbounded streams of data, often processed by data stream …

From a bird's eye point of view, large-scale data analytics workflows, eg, those executed in
popular tools, such as Apache Spark and Flink, are typically represented by directed acyclic …

Our work is motivated by the fact that there is an increasing need to perform complex
analytics jobs over streaming data as close to the edge devices as possible and, in parallel …

Rapid detection and mitigation of issues that impact performance and reliability are
paramount for large-scale online services. For real-time detection of such issues, datacenter …