This article is co-authored by Mishal Shah. As part of the evaluation stage of our bachelor thesis, we set up a testbed for running forwarding applications.
_This article is co-authored by [Mishal Shah_](https://mishal23.github.io/)
As part of the evaluation stage of our bachelor thesis, we set up a testbed for running forwarding applications in DPDK and with Pktgen-DPDK as the traffic generator. In this blog, we aim to cover
We did not find a lot of resources for setting up these tests and with this blog, we aim to bridge the gap.
The Data Plane Development Kit is an open-source software project managed by the Linux Foundation. It provides a set of data plane libraries and network interface controller polling-mode drivers running in userspace. This way, the NIC is directly accessible by the DPDK application. The advantage of using DPDK is that you can utilize the link speed completely whereas, in the standard packet processing, the link goes underutilized. Thus, high performance can be achieved with DPDK.
Pktgen-DPDK is a software-based traffic generator powered by DPDK. Traffic generators are often used to simulate various situations and test the performance of the application.
Neural networks, as their name implies, are computer algorithms modeled after networks of neurons in the human brain. Learn more about neural networks from Algorithmia.
Recurrent neural networks, also known as RNNs, are a class of neural networks that allow previous outputs to be used as inputs while having hidden states.
The purpose of this project is to build and evaluate Recurrent Neural Networks(RNNs) for sentence-level classification tasks. Let's understand about recurrent neural networks for multilabel text classification tasks.
Artificial neural networks (ANNs), usually simply called neural networks (NNs), are computing systems vaguely inspired by the biological neural networks that constitute animal brains.
Convolutional Neural Network: How is it different from the other networks? What’s so unique about CNNs and what does convolution really do? This is a math-free introduction to the wonders of CNNs.