We first formulate an optimization problem for the placement of stream processing operators, which is tailored to fog computing environments.

The results show that our approach can minimize the end-to-end latency by at least 38% by pushing part of the IoT application to the edge. Meanwhile, the edge- ...

We first formulate an optimization problem for the placement of stream processing operators, which is tailored to fog computing environments.

A plugin is built which solves the optimization problem for the placement of stream processing operators periodically in order to support the dynamic ...

Abstract—Elastic data stream processing enables applications to query and analyze streams of real time data. This is commonly facilitated by processing the ...

For avoiding this delay, we leverage on the fog computing paradigm which extends the cloud to the edge of the network. In order to design a stream processing ...

Elastic data stream processing enables applications to query and analyze streams of real time data. This is commonly facilitated by processing the flow of ...

Bibliographic details on Optimal Placement of Stream Processing Operators in the Fog.

... Operator Placement for Stream-Processing Systems [14] present a decentralized optimization of placements that is performed in a stream-based overlay network ...

Optimal Operator Replication and Placement for Distributed Stream Processing Systems · Placement of distributed stream processing over heterogeneous ...