22,090 research outputs found
NeuRoute: Predictive Dynamic Routing for Software-Defined Networks
This paper introduces NeuRoute, a dynamic routing framework for Software
Defined Networks (SDN) entirely based on machine learning, specifically, Neural
Networks. Current SDN/OpenFlow controllers use a default routing based on
Dijkstra algorithm for shortest paths, and provide APIs to develop custom
routing applications. NeuRoute is a controller-agnostic dynamic routing
framework that (i) predicts traffic matrix in real time, (ii) uses a neural
network to learn traffic characteristics and (iii) generates forwarding rules
accordingly to optimize the network throughput. NeuRoute achieves the same
results as the most efficient dynamic routing heuristic but in much less
execution time.Comment: Accepted for CNSM 201
VideoCapsuleNet: A Simplified Network for Action Detection
The recent advances in Deep Convolutional Neural Networks (DCNNs) have shown
extremely good results for video human action classification, however, action
detection is still a challenging problem. The current action detection
approaches follow a complex pipeline which involves multiple tasks such as tube
proposals, optical flow, and tube classification. In this work, we present a
more elegant solution for action detection based on the recently developed
capsule network. We propose a 3D capsule network for videos, called
VideoCapsuleNet: a unified network for action detection which can jointly
perform pixel-wise action segmentation along with action classification. The
proposed network is a generalization of capsule network from 2D to 3D, which
takes a sequence of video frames as input. The 3D generalization drastically
increases the number of capsules in the network, making capsule routing
computationally expensive. We introduce capsule-pooling in the convolutional
capsule layer to address this issue which makes the voting algorithm tractable.
The routing-by-agreement in the network inherently models the action
representations and various action characteristics are captured by the
predicted capsules. This inspired us to utilize the capsules for action
localization and the class-specific capsules predicted by the network are used
to determine a pixel-wise localization of actions. The localization is further
improved by parameterized skip connections with the convolutional capsule
layers and the network is trained end-to-end with a classification as well as
localization loss. The proposed network achieves sate-of-the-art performance on
multiple action detection datasets including UCF-Sports, J-HMDB, and UCF-101
(24 classes) with an impressive ~20% improvement on UCF-101 and ~15%
improvement on J-HMDB in terms of v-mAP scores
Impact of community structure on information transfer
The observation that real complex networks have internal structure has important implication for dynamic processes occurring on such topologies. Here we investigate the impact of community structure on a model of information transfer able to deal with both search and congestion simultaneously. We show that networks with fuzzy community structure are more efficient in terms of packet delivery than those with pronounced community structure. We also propose an alternative packet routing algorithm which takes advantage of the knowledge of communities to improve information transfer and show that in the context of the model an intermediate level of community structure is optimal. Finally, we show that in a hierarchical network setting, providing knowledge of communities at the level of highest modularity will improve network capacity by the largest amount
Multitask Learning for Network Traffic Classification
Traffic classification has various applications in today's Internet, from
resource allocation, billing and QoS purposes in ISPs to firewall and malware
detection in clients. Classical machine learning algorithms and deep learning
models have been widely used to solve the traffic classification task. However,
training such models requires a large amount of labeled data. Labeling data is
often the most difficult and time-consuming process in building a classifier.
To solve this challenge, we reformulate the traffic classification into a
multi-task learning framework where bandwidth requirement and duration of a
flow are predicted along with the traffic class. The motivation of this
approach is twofold: First, bandwidth requirement and duration are useful in
many applications, including routing, resource allocation, and QoS
provisioning. Second, these two values can be obtained from each flow easily
without the need for human labeling or capturing flows in a controlled and
isolated environment. We show that with a large amount of easily obtainable
data samples for bandwidth and duration prediction tasks, and only a few data
samples for the traffic classification task, one can achieve high accuracy. We
conduct two experiment with ISCX and QUIC public datasets and show the efficacy
of our approach
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