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deep-sdn

End-of-Year Project, Intelligent SDN traffic classification using deep learning : classifying SDN network traffic to differentiate between normal and abnormal packets by detecting attacks using deep learning. Mininet - RYU - Tensorflow - Neural Networks - CNN

deep-sdn-1

Intelligent SDN traffic classification using deep learning : Generating and classifying SDN network traffic to differentiate between normal and abnormal packets using deep learning.

deeplearning-network-traffic

Network Traffic Identification with Convolutional Neural Networks

deeptraffic

Deep Learning models for network traffic classification

dimsum

Implementation and evaluation code for the IMSum and DIMSum algorithms for optimal elephant flow detection.

dledge

This project accelerates deep learning models on the edge with the support of running ensemble learning for performance improvement. The current version accelerates YOLO object detectors with Intel Neural Compute Stick 2 using parallel programming.

dnac-samples-aradford

doat

DPDK Optimisation & Analysis Tool

documentations

Repository for WIKI

documentations-1

Some documents about ONOS and XOS deployment

dpdk

dpdk开发文档翻译

dpdk-1

dpdk学习及相关项目

dpdk-2

From http://fast.dpdk.org/rel/dpdk-17.08.1.tar.xz

dpdk-ans

ANS(Accelerated Network Stack) on DPDK, DPDK native TCP/IP stack.

dpdk-dump

DPDK-Dump is able to store on disk network traffic at high speed using DPDK library.

dpdk-go

Go bindings for dpdk.

dpdk-hyperscan

hyperscan using dpdk

dpdk-iperf

Fork from official iperf-3.1.3, and run on the dpdk user space TCP/IP stack(ANS).

dpdk-tcp-udp_protocol_stack

Simple protocol stack based on dpdk（使用dpdk搭建协议栈）

dpdk-telemetry-exporter

A small DPDK telemetry exporter

dpdksim

DPDKSim - A simulator for DPDK-based Virtual Switches

dperf

dperf is a DPDK based 100Gbps network performance and load testing software.

dr-elephant

Dr. Elephant is a job and flow-level performance monitoring and tuning tool for Apache Hadoop and Apache Spark

droo

Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

dsd-datahub-sdk-embedded-c

An embedded C edition of DSD IoT Datahub SDK for connecting Dasudian IIoT and AI platform with MQTT, which facilitates various devices to be intelligent as an Edge Computing node dedicated for industrial smart applications. (嵌入式C版本的DSD IoT Datahub SDK，专注于让设备安全、高效地连接大数点工业物联网与人工智能平台，帮助设备快速成为智能的边缘计算节点，创建工业智能应用。)

dsv

A dpdk based storage virtualization

dynamic-link-failure-recovery-sdn

To enable fast recovery upon link failure in OpenFlow networks by computing an alternative backup path for a particular link. When the link failure happens, the computed backup path for that particular link is used to redirect the traffic.

e15-4yp-microservice-based-edge-computing-architecture

edge-intelligence

随着移动云计算和边缘计算的快速发展，以及人工智能的广泛应用，产生了边缘智能(Edge Intelligence)的概念。深度神经网络(例如CNN)已被广泛应用于移动智能应用程序中，但是移动设备有限的存储和计算资源无法满足深度神经网络计算的需求。神经网络压缩与加速技术可以加速神经网络的计算，例如剪枝、量化、卷积核分解等。但是这些技术在实际应用非常复杂，并且可能导致模型精度的下降。在移动云计算或边缘计算中，任务卸载技术可以突破移动终端的资源限制，减轻移动设备的计算负载并提高任务处理效率。通过任务卸载技术优化深度神经网络成为边缘智能研究中的新方向。Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge这篇文章提出了协同推断的**，将深度神经网络进行分区，一部分层在移动端计算，而另一部分在云端计算。根据硬件平台、无线网络以及服务器负载等因素实现动态分区，降低时延以及能耗。本项目给出了边缘智能方面的相关论文，并且给出了一个Python语言实现的卷积神经网络协同推断实验平台。关键词：边缘智能(Edge Intelligence)，计算卸载(Computing Offloading)，CNN模型分区(CNN Partition)，协同推断(Collaborative Inference)，移动云计算(Mobile Cloud Computing)

edgecomputing

Edge Based SDN Solution for Handling Increasing IoT Data