We propose, in this paper, an efficient rules placement algorithm based on Deep Reinforcement Learning (DRL) and traffic prediction.
The obtained results using ONOS controller and OpenvSwitch revealed the efficiency of the proposed approach in decreasing both network latency and packet loss, ...
Abstract—The centralization of network intelligence enabled by Software Defined Networking (SDN), and the recent break- throughs of Machine Learning (ML), ...
A Knowledge plane, on top of control and management planes, should allow for: – automation & optimization. – prediction. • Machine Learning can take ...
This paper studies controller placement and optimal edge selection in SDN-based multi-access edge computing environments.
In this work, we propose the evolution of an existing Edge-IoT architecture to a new improved version in which SDN/NFV are used over the Edge-IoT capabilities.
Oct 28, 2024 · The integration of Deep Reinforcement Learning (DRL) allows for dynamic network resource balancing, minimizing communication latency and ...
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Oct 28, 2024 · The simulation results show that the performance of DFRDRL is better than the equivalent algorithms in terms of latency and throughput. Also, ...
In this paper, we take a further step towards the goal of an efficient and intelligent routing scheme in SDN by intro- ducing a novel approach, called Deep ...
Oct 20, 2020 · In this paper, we design a novel routing optimization mechanism based on deep reinforcement learning. This mechanism is capable of reducing the network ...
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