Traditional parallel computing for power management systems has prime challenges such
as execution time, computational complexity, and efficiency like process time and delays in …

Stream processing is an emerging paradigm to handle data streams upon arrival, powering
latency-critical application such as fraud detection, algorithmic trading, and health …

In the wake of the development of mobile devices, how to provide low-latency mobile
services with the limited battery power is attracting more and more attention. A novel …

The high volumes of data are continuously generated from Internet of Things (IoT) sensors in
an industrial landscape. Especially, the data-intensive workflows from IoT systems require to …

Edge computing is more and more popular due to its low latency and bandwidth-efficient
services. Edge computing is mainly applied to the latency-critical and computation-intensive …

The cloud computing networks used in the IoT, and other themes of network architectures,
can be investigated and improved by cheminformatics, which is a combination of chemistry …

With the proliferation of smartphones and tablets, large amounts of data are generated at the
edge of the network. Edge computing becomes a promising paradigm for the processing of …

Scheduling streaming applications in Data Stream Management Systems (DSMS) has been
investigated for years. However, there lacks an intelligent system that is capable of …

Like other emerging fields, Stream Processing Engines (SPEs) pose several challenges to
the researchers such as resource awareness, dynamic configurations, heterogeneous …

In this study, we investigated the problem of scheduling streaming applications on a
heterogeneous cluster environment and, based on our previous work, developed the …