Combining edge processing (at data capture site) with analysis carried out while data is enroute from the capture site to a data center offers a variety of different processing models. Such in-transit nodes include network data centers that have generally been used to support content distribution (providing support for data multicast and caching), but have recently started to offer user-defined programmability, through Software Defined Networks (SDN) capability, e.g., OpenFlow and Network Function Visualization (NFV). We demonstrate how this multi-site computational capability can be aggregated to support video analytics, with Quality of Service and cost constraints (e.g., latency-bound analysis). The use of SDN technology enables separatio...
Internet video requires large-scale content-centric multicast and is highly latency-sensitive. The r...
Edge cloud computing seems to be a key enabler of 5G networks which essentially brings the servers a...
Edge video analytics based on deep learning has become an important building block for many modern i...
Combining edge processing (at data capture site) with analysis carried out while data is enroute fro...
Combining edge processing (at data capture site) with analysis carried out while data is enroute fro...
This paper proposes the introduction of SDN-enabled containers to support the deployment of SDN/NFV ...
Software-defined networks (SDNs) have proven to be an efficacious tool for undertaking complex data ...
Powered by deep learning, video analytic applications process millions of camera feeds in real-time ...
In this paper, we study how to design resource allocation algorithms for data analytics services tha...
Distributed execution of real-time data analytics such as event stream processing is the key to scal...
Prediction systems present some challenges on two fronts: the relation between video quality and obs...
International audienceLive video analytics have become a key technology to support surveillance, sec...
Several technologies are being presented with 5G networks’ evolution, technologies such as Network ...
This thesis contains four interrelated research areas. Before presenting the four research areas, th...
Internet video requires large-scale content-centric multicast and is highly latency-sensitive. The r...
Edge cloud computing seems to be a key enabler of 5G networks which essentially brings the servers a...
Edge video analytics based on deep learning has become an important building block for many modern i...
Combining edge processing (at data capture site) with analysis carried out while data is enroute fro...
Combining edge processing (at data capture site) with analysis carried out while data is enroute fro...
This paper proposes the introduction of SDN-enabled containers to support the deployment of SDN/NFV ...
Software-defined networks (SDNs) have proven to be an efficacious tool for undertaking complex data ...
Powered by deep learning, video analytic applications process millions of camera feeds in real-time ...
In this paper, we study how to design resource allocation algorithms for data analytics services tha...
Distributed execution of real-time data analytics such as event stream processing is the key to scal...
Prediction systems present some challenges on two fronts: the relation between video quality and obs...
International audienceLive video analytics have become a key technology to support surveillance, sec...
Several technologies are being presented with 5G networks’ evolution, technologies such as Network ...
This thesis contains four interrelated research areas. Before presenting the four research areas, th...
Internet video requires large-scale content-centric multicast and is highly latency-sensitive. The r...
Edge cloud computing seems to be a key enabler of 5G networks which essentially brings the servers a...
Edge video analytics based on deep learning has become an important building block for many modern i...