Aymen Hasan AlAwadi's repositories
learn-sdn-with-ryu
Learn SDN with RYU Controller
batchy
Batch-scheduler framework for controlling execution in a packet-processing pipeline based on strict service-level objectives
cloudsimsdn
CloudSimSDN is an SDN extension of CloudSim project to simulate Networking, SDN and SFC features in the context of edge and cloud data centers.
coflowsim
Flow-level simulator for coflow scheduling used in Varys and Aalo
DeepTraffic
Deep Learning models for network traffic classification
delay_monitor_sdn
A delay monitoring module for SDN in Ryu
exp_BFlows
exp_BFlows is an experiment to compare the performance of BFlows with ECMP, PureSDN, Hedera and NonBlocking network.
exp_BFlows2
exp_BFlows is an experiment to compare the performance of BFlows with ECMP, PureSDN, Hedera and NonBlocking network.
exp_EFattree
exp_EFattree is an experiment to compare the performance of EFattree with ECMP, PureSDN and Hedera.
flow-models
A framework for analysis and modeling of IP network flows
flowmanager
The FlowManager is an SDN application that gives a network administrator the ability to control flows in an OpenFlow network.
HMMLB
This is the code repo for the HMMLB Project
Learning-SDN
SDN 學習及實作範例。
mininet
Emulator for rapid prototyping of Software Defined Networks
multipath
Multipath routing with Ryu and Pyretic SDN Controllers
Oddlab
Oddlab DCN traffic engineering and fault-tolerant method repo.
SDN-Smart-Routing
SDN proactive fault handling
Understanding-the-Modeling-of-Network-Delays-using-NN
Recent trends in networking are proposing the use of Machine Learning (ML) techniques for the control and operation of the network. In this context, ML can be used as a computer network modeling technique to build models that estimate the network performance. Indeed, network modeling is a central technique to many networking functions, for instance in the field of optimization, in which the model is used to search a configuration that satisfies the target policy. In this paper, we aim to provide an answer to the following question: Can neural networks accurately model the delay of a computer network as a function of the input traffic? For this, we assume the network as a black-box that has as input a traffic matrix and as output delays. Then we train different neural networks models and evaluate its accuracy under different fundamental network characteristics: topology, size, traffic intensity and routing. With this, we aim to have a better understanding of computer network modeling with neural nets and ultimately provide practical guidelines on how such models need to be trained.