Related papers: Stack Management for MPLS Network Actions: Integra…
In MPLS, packets are encapsulated with labels that add domain-specific forwarding information. Special purpose labels were introduced to trigger special behavior in MPLS nodes but their number is limited. Therefore, the IETF proposed the…
In the paper, a new mechanism for Software-Defined Networking (SDN) flow aggregation accompanied with multipath transmission is proposed. The aggregation results in a low number of flow entries in core switches. This type of scalability…
The highly dynamic nature of the current network traffics, makes the network managers to exploit the flexibility of the state-of-the-art paradigm called SDN. In this way, there has been an increasing interest in hybrid networks of SDN-MPLS.…
Multiprotocol Label Switching (MPLS) is a high-performance telecommunications technology that directs data from one network node to another based on short path labels rather than long network addresses. Its efficiency and scalability have…
Multi-Protocol Label Switching (MPLS) had been deployed by many data networking service providers, including the next-generation mobile backhaul networks, because of its undeniable potential in terms of virtual private network (VPN)…
Multiprotocol Label Switching (MPLS) fasten the speed of packet forwarding by forwarding the packets based on labels and reduces the use of routing table look up from all routers to label edge routers(LER), where as the label switch routers…
Scalability of the network layer mobility management solution is one of the most important requirements for the mobility control plane. Mobility Label Based Network (MLBN) is a new approach to the network layer mobility management problem…
The new generation of machine learning processors have evolved from multi-core and parallel architectures that were designed to efficiently implement matrix-vector-multiplications (MVMs). This is because at the fundamental level, neural…
Large Language Models (LLMs) are computationally intensive, particularly during inference. Neuron-adaptive techniques, which selectively activate neurons in Multi-Layer Perceptron (MLP) layers, offer some speedups but suffer from…
Designing state encoders for reinforcement learning (RL) with multiple information sources -- such as sensor measurements, time-series signals, image observations, and textual instructions -- remains underexplored and often requires manual…
The Multi-Protocol Label Switching (MPLS) is an emerging technology which have quality, effectiveness and administration quality. On the contrary, traditional network inside part passage steering conventions ruin the viable acknowledgment…
Network device and system health management is the foundation of modern network operations and maintenance. Traditional health management methods, relying on expert identification or simple rule-based algorithms, struggle to cope with the…
Limited memory bandwidth is a critical bottleneck in modern systems. 3D-stacked DRAM enables higher bandwidth by leveraging wider Through-Silicon-Via (TSV) channels, but today's systems cannot fully exploit them due to the limited internal…
This work develops an LLM-based optimization framework ensuring strict constraint satisfaction in network optimization. While LLMs possess contextual reasoning capabilities, existing approaches often fail to enforce constraints, causing…
Ensuring that information flowing through a network is secure from manipulation and eavesdropping by unauthorized parties is an important task for network administrators. Many cyber attacks rely on a lack of network-level information flow…
This paper is focused on multiuser load modulation arrays (MU-LMAs) which are attractive due to their low system complexity and reduced cost for millimeter wave (mmWave) multi-input multi-output (MIMO) systems. The existing precoding…
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)…
The adoption of intelligent systems with Artificial Neural Networks (ANNs) embedded in hardware for real-time applications currently faces a growing demand in fields like the Internet of Things (IoT) and Machine to Machine (M2M). However,…
Network (or Graph) Alignment Algorithms aims to reveal structural similarities among graphs. In particular Local Network Alignment Algorithms (LNAs) finds local regions of similarity among two or more networks. Such algorithms are in…
While message-passing neural networks (MPNNs) have shown promising results, their real-world impact remains limited. Although various limitations have been identified, their theoretical foundations remain poorly understood, leading to…