Leveraging deep reinforcement learning for traffic engineering: A survey
After decades of unprecedented development, modern networks have evolved far beyond
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …
Convergence of blockchain and edge computing for secure and scalable IIoT critical infrastructures in industry 4.0
Critical infrastructure systems are vital to underpin the functioning of a society and economy.
Due to the ever-increasing number of Internet-connected Internet-of-Things (IoT)/Industrial …
Due to the ever-increasing number of Internet-connected Internet-of-Things (IoT)/Industrial …
Security and privacy in 5g-iiot smart factories: Novel approaches, trends, and challenges
To implement various artificial intelligence and automation applications in smart factories,
edge computing and industrial Internet of Things (IIoT) devices must be widely deployed, so …
edge computing and industrial Internet of Things (IIoT) devices must be widely deployed, so …
An incremental learning framework for human-like redundancy optimization of anthropomorphic manipulators
Recently, the human-like behavior on the anthropomorphic robot manipulator is increasingly
accomplished by the kinematic model establishing the relationship of an anthropomorphic …
accomplished by the kinematic model establishing the relationship of an anthropomorphic …
DRL-PLink: Deep reinforcement learning with private link approach for mix-flow scheduling in software-defined data-center networks
WX Liu, J Lu, J Cai, Y Zhu, S Ling… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In datacenter networks, bandwidth-demanding elephant flows without deadline and delay-
sensitive mice flows with strict deadline coexist. They compete with each other for limited …
sensitive mice flows with strict deadline coexist. They compete with each other for limited …
Fine-grained flow classification using deep learning for software defined data center networks
WX Liu, J Cai, Y Wang, QC Chen, JQ Zeng - Journal of Network and …, 2020 - Elsevier
Abstract in a data center network, accurately classifying flow is the key to optimal schedule
flow. However, the existing classification methods cannot meet the demand of real networks …
flow. However, the existing classification methods cannot meet the demand of real networks …
Next-generation data center network enabled by machine learning: Review, challenges, and opportunities
Data center network (DCN) is the backbone of many emerging applications from smart
connected homes to smart traffic control and is continuously evolving to meet the diverse …
connected homes to smart traffic control and is continuously evolving to meet the diverse …
RSCAT: Towards zero touch congestion control based on actor–critic reinforcement learning and software-defined networking
Network congestion is a phenomenon present in contemporaneous data centers (DCs)
independently of scale and underlying technologies. The small-scale presence of …
independently of scale and underlying technologies. The small-scale presence of …
Data classification and reinforcement learning to avoid congestion on SDN-based data centers
A contemporaneous data center (DC) hosts multiple competitive network data flows from
different applications, sharing the intermediate switches capacities. In this context …
different applications, sharing the intermediate switches capacities. In this context …
Review of path selection algorithms with link quality and critical switch aware for heterogeneous traffic in SDN
Sažetak Software Defined Networking (SDN) introduced network management flexibility that
eludes traditional network architecture. Nevertheless, the pervasive demand for various …
eludes traditional network architecture. Nevertheless, the pervasive demand for various …