A survey of low-latency transmission strategies in software defined networking
B Yan, Q Liu, JL Shen, D Liang, B Zhao… - Computer Science …, 2021 - Elsevier
Abstract Software-defined networking (SDN), as a revolutionary networking paradigm,
provides a new solution for future network development and equipment manufacturing by …
provides a new solution for future network development and equipment manufacturing by …
Deep Q-learning for routing schemes in SDN-based data center networks
Q Fu, E Sun, K Meng, M Li, Y Zhang - IEEE Access, 2020 - ieeexplore.ieee.org
In order to adapt to the rapid development of cloud computing, big data, and other
technologies, the combination of data center networks and SDN is proposed to make …
technologies, the combination of data center networks and SDN is proposed to make …
Intelligently modeling, detecting, and scheduling elephant flows in software defined energy cloud: A survey
Elephant flows (elephants) refer to the sequences of packets that contribute only 10% of the
total volume but consume over 90% of the network bandwidth. They often cause network …
total volume but consume over 90% of the network bandwidth. They often cause network …
Nelly: Flow detection using incremental learning at the server side of sdn-based data centers
F Estrada-Solano, OM Caicedo… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
The processing of big data generated by the Industrial Internet of Things (IIoT) calls for the
support of processing at the edge of the network, as well as at the cloud data centers. The …
support of processing at the edge of the network, as well as at the cloud data centers. The …
A framework for elephant flow detection for SDNS based on the classification
Recently, there has been considerable interest in the classification of flows among existing
applications in SDN, such as traffic engineering, service quality, and network management …
applications in SDN, such as traffic engineering, service quality, and network management …
An approach based on knowledge-defined networking for identifying heavy-hitter flows in data center networks
A Duque-Torres, F Amezquita-Suárez… - Applied Sciences, 2019 - mdpi.com
Heavy-Hitters (HHs) are large-volume flows that consume considerably more network
resources than other flows combined. In SDN-based DCNs (SDDCNs), HHs cause non …
resources than other flows combined. In SDN-based DCNs (SDDCNs), HHs cause non …
Machine learning empowered intelligent data center networking: A survey
To support the needs of ever-growing cloud-based services, the number of servers and
network devices in data centers is increasing exponentially, which in turn results in high …
network devices in data centers is increasing exponentially, which in turn results in high …
Towards threshold‐agnostic heavy‐hitter classification
A heavy‐hitter (HH) network traffic flow consumes considerably more network resources
than other flows combined. The classification of HHs is critical to provide, among others, the …
than other flows combined. The classification of HHs is critical to provide, among others, the …
An Adaptable and Agnostic Flow Scheduling Approach for Data Center Networks
Cloud applications have reshaped the model of services and infrastructure of the Internet.
Search engines, social networks, content delivery and retail and e-commerce sites belong to …
Search engines, social networks, content delivery and retail and e-commerce sites belong to …
Machine learning empowered intelligent data center networking
Abstract Machine learning has been widely studied and practiced in data center networks,
and a large number of achievements have been made. In this chapter, we will review …
and a large number of achievements have been made. In this chapter, we will review …