相关论文: MACS: Deep Reinforcement Learning based SDN Contro…
Recent research on Software-Defined Networking (SDN) strongly promotes the adoption of distributed controller architectures. To achieve high network performance, designing a scheduling function (SF) to properly dispatch requests from each…
Traditional communication networks consist of large sets of vendor-specific manually configurable devices which are hardwired with specific control logic or algorithms. The resulting networks comprise distributed control plane architectures…
The cross-domain multicast routing problem in a software-defined wireless network with multiple controllers is a classic NP-hard optimization problem. As the network size increases, designing and implementing cross-domain multicast routing…
The superiority of Multi-Robot Systems (MRS) in various complex environments is unquestionable. However, in complex situations such as search and rescue, environmental monitoring, and automated production, robots are often required to work…
Software defined networking (SDN) promises unprecedented flexibility and ease of network operations. While flexibility is an important factor when leveraging advantages of a new technology, critical infrastructure networks also have…
In multi-agent deep reinforcement learning (MADRL), agents can communicate with one another to perform a task in a coordinated manner. When multiple tasks are involved, agents can also leverage knowledge from one task to improve learning in…
The aim of this paper is to analyze methods of flexible control in SDN networks and to propose a self-developed solution that will enable intelligent adaptation of SDN controller performance. This work aims not only to review existing…
Software Defined Networking (SDN) has emerged as a programmable approach for provisioning and managing network resources by defining a clear separation between the control and data forwarding planes. Nowadays SDN has gained significant…
In recent years, Deep Reinforcement Learning has made impressive advances in solving several important benchmark problems for sequential decision making. Many control applications use a generic multilayer perceptron (MLP) for non-vision…
Software-Defined Networking (SDN) allows to control the available network resources by an intelligent and centralized authority in order to optimize traffic flows in a flexible manner. However, centralized control may face scalability…
Convolutional neural networks (CNNs) are deep learning frameworks which are well-known for their notable performance in classification tasks. Hence, many skeleton-based action recognition and segmentation (SBARS) algorithms benefit from…
Mobile Ad Hoc Networks (MANETs) are decentralized wireless networks, characterized by their dynamic topologies and node mobility. In the era of cutting-edge technologies, integrating Software-Defined Networking (SDN) with MANETs offers a…
The concept of Software Defined Networking (SDN) represents a modern approach to networking that separates the control plane from the data plane through network abstraction, resulting in a flexible, programmable and dynamic architecture…
The optimal multicast tree problem in the Software-Defined Networking (SDN) multicast routing is an NP-hard combinatorial optimization problem. Although existing SDN intelligent solution methods, which are based on deep reinforcement…
With the advent of 21st century and increasing advancements in the field of technology and connectivity, inter-networking in real-time has achieved great importance. Distributed control and multi-agent paradigm has groped rapidly with…
Network security is a critical concern in the digital landscape of today, with users demanding secure browsing experiences and protection of their personal data. This study explores the dynamic integration of Machine Learning (ML)…
We consider networked control systems consisting of multiple independent controlled subsystems, operating over a shared communication network. Such systems are ubiquitous in cyber-physical systems, Internet of Things, and large-scale…
To improve traffic management ability, Internet Service Providers (ISPs) are gradually upgrading legacy network devices to programmable devices that support Software-Defined Networking (SDN). The coexistence of legacy and SDN devices gives…
Deep neural networks (DNNs) can enable precise control while maintaining low computational costs by circumventing the need for dynamic modeling. However, the deployment of such black-box approaches remains challenging for heavy-duty wheeled…
The evolution of Software-Defined Networking (SDN) has so far been predominantly geared towards defining and refining the abstractions on the forwarding and control planes. However, despite a maturing south-bound interface and a range of…