Pinned Repositories
batchy
Batch-scheduler framework for controlling execution in a packet-processing pipeline based on strict service-level objectives
BFlows
BFlows is a SDN-based traffic schduling application.
Burst-Balancer
ccp_copa
ECMP
Fattree network ECMP routing application.
Hedera
Implementing Hedera with Ryu controller.
learn-sdn-with-ryu
Learn SDN with RYU Controller
PureSDN
PureSDN routing application for FatTree network.
ryu
Ryu component-based software defined networking framework
tutorials
P4 language tutorials
aymeniq's Repositories
aymeniq/BFlows
BFlows is a SDN-based traffic schduling application.
aymeniq/Hedera
Implementing Hedera with Ryu controller.
aymeniq/learn-sdn-with-ryu
Learn SDN with RYU Controller
aymeniq/PureSDN
PureSDN routing application for FatTree network.
aymeniq/ryu
Ryu component-based software defined networking framework
aymeniq/tutorials
P4 language tutorials
aymeniq/batchy
Batch-scheduler framework for controlling execution in a packet-processing pipeline based on strict service-level objectives
aymeniq/Burst-Balancer
aymeniq/ccp_copa
aymeniq/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.
aymeniq/coflowsim
Flow-level simulator for coflow scheduling used in Varys and Aalo
aymeniq/DeepTraffic
Deep Learning models for network traffic classification
aymeniq/delay_monitor_sdn
A delay monitoring module for SDN in Ryu
aymeniq/exp_BFlows
exp_BFlows is an experiment to compare the performance of BFlows with ECMP, PureSDN, Hedera and NonBlocking network.
aymeniq/exp_BFlows2
exp_BFlows is an experiment to compare the performance of BFlows with ECMP, PureSDN, Hedera and NonBlocking network.
aymeniq/exp_EFattree
exp_EFattree is an experiment to compare the performance of EFattree with ECMP, PureSDN and Hedera.
aymeniq/flow-models
A framework for analysis and modeling of IP network flows
aymeniq/flowmanager
The FlowManager is an SDN application that gives a network administrator the ability to control flows in an OpenFlow network.
aymeniq/HMMLB
This is the code repo for the HMMLB Project
aymeniq/INT_DETECT
aymeniq/Learning-SDN
SDN 學習及實作範例。
aymeniq/mininet
Emulator for rapid prototyping of Software Defined Networks
aymeniq/MovingTargetDefenseProject
aymeniq/multipath
Multipath routing with Ryu and Pyretic SDN Controllers
aymeniq/Oddlab
Oddlab DCN traffic engineering and fault-tolerant method repo.
aymeniq/P4TE
aymeniq/SDN-Smart-Routing
SDN proactive fault handling
aymeniq/Sieve
aymeniq/Smoothie
aymeniq/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.