Utilizing TabNet Deep Learning for Elephant Flow Detection by Analyzing Information in First Packet Headers.

Rapid and precise detection of significant data streams within a network is crucial for efficient traffic management. This study leverages the TabNet deep learning architecture to identify large-scale flows, known as elephant flows, by analyzing the information in the 5-tuple fields of the initial packet header. The results demonstrate that employing a TabNet model can accurately identify elephant flows right at the start of the flow and makes it possible to reduce the number of flow table entries by up to 20 times while still effectively managing 80% of the network traffic through individual flow entries.

The no-wait cyclic scheduling of delivery traffic in the grid distribution network.

This paper presents a method for the fast prototyping of no-wait cyclic schedules for periodic material handling systems with a Grid-like Material Transportation Network (GMTN). A distribution network is modeled as a grid-like system of cyclic processes performing regular pick-up and delivery operations between workstations in separate grid modules. The considered problem boils down to a job shop cyclic scheduling problem with no-buffer and no-wait constraints, which is NP-hard.