When many users and unique applications share a congested edge link (e.g., a home network), everyone wants their own application to continue to perform well despite contention over network resources. Traditionally, network engineers have focused on fairness as the key objective to ensure that competing applications are equitably and led by the switch, and hence have deployed fair queueing mechanisms. However, for many network workloads today, strict fairness is directly at odds with equitable application performance. Real-time streaming applications, such as videoconferencing, suffer the most when network performance is volatile (with delay spikes or sudden and dramatic drops in throughput). Unfortunately, "fair" queueing mechanisms lead to extremely volatile network behavior in the presence of bursty and multi-flow applications such as Web traffic. When a sudden burst of new data arrives, fair queueing algorithms rapidly shift resources away from incumbent flows, leading to severe stalls in real-time applications. In this paper, we present Confucius, the first practical queue management scheme to effectively balance fairness against volatility, providing performance outcomes that benefit all applications sharing the contended link. Confucius outperforms realistic queueing schemes by protecting the real-time streaming flows from stalls in competing with more than 95% of websites. Importantly, Confucius does not assume the collaboration of end-hosts, nor does it require manual parameter tuning to achieve good performance. less

Routers employ queues to temporarily hold packets when the scheduler cannot immediately process them. Congestion occurs when the arrival rate of packets exceeds the processing capacity, leading to increased queueing delay. Over time, Active Queue Management (AQM) strategies have focused on directly draining packets from queues to alleviate congestion and reduce queuing delay. On Programmable Data Plane (PDP) hardware, AQMs traditionally reside in the Egress pipeline due to the availability of queue delay information there. We argue that this approach wastes the router's resources because the dropped packet has already consumed the entire pipeline of the device. In this work, we propose ingress Random Early Detection (iRED), a more efficient approach that addresses the Egress drop problem. iRED is a disaggregated P4-AQM fully implemented in programmable data plane hardware and also supports Low Latency, Low Loss, and Scalable Throughput (L4S) framework, saving device pipeline resources by dropping packets in the Ingress block. To evaluate iRED, we conducted three experiments using a Tofino2 programmable switch: i) An in-depth analysis of state-of-the-art AQMs on PDP hardware, using 12 different network configurations varying in bandwidth, Round-Trip Time (RTT), and Maximum Transmission Unit (MTU). The results demonstrate that iRED can significantly reduce router resource consumption, with up to a 10x reduction in memory usage, 12x fewer processing cycles, and 8x less power consumption for the same traffic load; ii) A performance evaluation regarding the L4S framework. The results prove that iRED achieves fairness in bandwidth usage for different types of traffic (classic and scalable); iii) A comprehensive analysis of the QoS in a real setup of a DASH) technology. iRED demonstrated up to a 2.34x improvement in FPS and a 4.77x increase in the video player buffer fill. less

Active Queue Management (AQM) is a mechanism employed to alleviate transient congestion in network device buffers, such as routers and switches. Traditional AQM algorithms use fixed thresholds, like target delay or queue occupancy, to compute random packet drop probabilities. A very small target delay can increase packet losses and reduce link utilization, while a large target delay may increase queueing delays while lowering drop probability. Due to dynamic network traffic characteristics, where traffic fluctuations can lead to significant queue variations, maintaining a fixed threshold AQM may not suit all applications. Consequently, we explore the question: \textit{What is the ideal threshold (target delay) for AQMs?} In this work, we introduce DESiRED (Dynamic, Enhanced, and Smart iRED), a P4-based AQM that leverages precise network feedback from In-band Network Telemetry (INT) to feed a Deep Reinforcement Learning (DRL) model. This model dynamically adjusts the target delay based on rewards that maximize application Quality of Service (QoS). We evaluate DESiRED in a realistic P4-based test environment running an MPEG-DASH service. Our findings demonstrate up to a 90x reduction in video stall and a 42x increase in high-resolution video playback quality when the target delay is adjusted dynamically by DESiRED. less

Leveraging non-terrestrial platforms in 6G networks holds immense significance as it opens up opportunities to expand network coverage, enhance connectivity, and support a wide range of innovative applications, including global-scale Internet of Things and ultra-high-definition content delivery. To accomplish the seamless integration between terrestrial and non-terrestrial networks, substantial changes in radio access network (RAN) architecture are required. These changes involve the development of new RAN solutions that can efficiently manage the diverse characteristics of both terrestrial and non-terrestrial components, ensuring smooth handovers, resource allocation, and quality of service across the integrated network ecosystem. Additionally, the establishment of robust interconnection and communication protocols between terrestrial and non-terrestrial elements will be pivotal to utilize the full potential of 6G technology. Additionally, innovative approaches have been introduced to split the functionalities within the RAN into centralized and distributed domains. These novel paradigms are designed to enhance RAN's flexibility while simultaneously lowering the costs associated with infrastructure deployment, all while ensuring that the quality of service for end-users remains unaffected. In this work, we provide an extensive examination of various Non-Terrestrial Networks (NTN) architectures and the necessary adaptations required on the existing 5G RAN architecture to align with the distinct attributes of NTN. Of particular significance, we emphasize the crucial RAN functional split choices essential for the seamless integration of terrestrial and non-terrestrial components within advanced 6G networks. less

This paper describes an architecture design process for Networked Music Performance (NMP) platform for medium-sized conducted music ensembles, based on remote rehearsals of Academic Choir of Gdansk University of Technology. The issues of real-time remote communication, in-person music performance, and NMP are described. Three iterative steps defining and extending the architecture of the NMP platform with additional features to enhance its utility in remote rehearsals are presented. The first iteration uses a regular video conferencing platform, the second iteration uses dedicated NMP devices and tools, and the third iteration adds video transmission and utilizes professional low-latency audio and video workstations. For each iteration, the platform architecture is defined and deployed with simultaneous usability tests. Its strengths and weaknesses are identified through qualitative and quantitative measurements - statistical analysis shows a significant improvement in rehearsal quality after each iteration. The final optimal architecture is described and concluded with guidelines for creating NMP systems for said music ensembles. less

We describe a method to achieve distributed consensus in a Content Centric Network using the PAXOS algorithm. Consensus is necessary, for example, if multiple writers wish to agree on the current version number of a CCNx name or if multiple distributed systems wish to elect a leader for fast transaction processing. We describe two forms of protocols, one using standard CCNx Interest request and Content Object response, and the second using a CCNx Push request and response. We further divide the protocols in to those using the CCNx 0.x protocol where Content Object name may continue Interest names and the CCNx 1.0 protocol where Content Object names exactly match Interest names. less

This proposed model introduces novel deep learning methodologies. The objective here is to create a reliable intrusion detection mechanism to help identify malicious attacks. Deep learning based solution framework is developed consisting of three approaches. The first approach is Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) with seven optimizer functions such as adamax, SGD, adagrad, adam, RMSprop, nadam and adadelta. The model is evaluated on NSL-KDD dataset and classified multi attack classification. The model has outperformed with adamax optimizer in terms of accuracy, detection rate and low false alarm rate. The results of LSTM-RNN with adamax optimizer is compared with existing shallow machine and deep learning models in terms of accuracy, detection rate and low false alarm rate. The multi model methodology consisting of Recurrent Neural Network (RNN), Long-Short Term Memory Recurrent Neural Network (LSTM-RNN), and Deep Neural Network (DNN). The multi models are evaluated on bench mark datasets such as KDD99, NSL-KDD, and UNSWNB15 datasets. The models self-learnt the features and classifies the attack classes as multi-attack classification. The models RNN, and LSTM-RNN provide considerable performance compared to other existing methods on KDD99 and NSL-KDD dataset less

The ever-growing end user data demands, and the simultaneous reductions in memory costs are fueling edge-caching deployments. Caching at the edge is substantially different from that at the core and needs to take into account the nature of individual data demands. For example, an individual user may not be interested in requesting the same data item again, if it has recently requested it. Such individual dynamics are not apparent in the aggregated data requests at the core and have not been considered in popularity-driven caching designs for the core. Hence, these traditional caching policies could induce significant inefficiencies when applied at the edges. To address this issue, we develop new edge caching policies optimized for the individual demands that also leverage overhearing opportunities at the wireless edge. With the objective of maximizing the hit ratio, the proposed policies will actively evict the data items that are not likely to be requested in the near future, and strategically bring them back into the cache through overhearing when they are likely to be popular again. Both theoretical analysis and numerical simulations demonstrate that the proposed edge caching policies could outperform the popularity-driven policies that are optimal at the core. less

Intractable is the problem of finding two link-disjoint paths of minimal cost if the path cost is limited since it can be a special case of the partition problem. In optical networks, this limit can be introduced by the signal modulation reach. Even without this limit, the existing literature suggested the problem intractable because of the spectrum continuity and contiguity constraints, but we show that the problem can be solved exactly with the recently-proposed generic Dijkstra algorithm over a polynomially-bounded search space, thus proving the problem tractable. less

The Open Radio Access Network (RAN) paradigm is transforming cellular networks into a system of disaggregated, virtualized, and software-based components. These self-optimize the network through programmable, closed-loop control, leveraging Artificial Intelligence (AI) and Machine Learning (ML) routines. In this context, Deep Reinforcement Learning (DRL) has shown great potential in addressing complex resource allocation problems. However, DRL -based solutions are inherently hard to explain, which hinders their deployment and use in practice. In this paper, we propose EXPLORA, a framework that provides explainability of DRL-based control solutions for the Open RAN ecosystem. EXPLORA synthesizes network-oriented explanations based on an attributed graph that produces a link between the actions taken by a DRL agent (i.e., the nodes of the graph) and the input state space (i.e., the attributes of each node). This novel approach allows EXPLORA to explain models by providing information on the wireless context in which the DRL agent operates. EXPLORA is also designed to be lightweight for real-time operation. We prototype EXPLORA and test it experimentally on an O-RAN-compliant near-real-time RIC deployed on the Colosseum wireless network emulator. We evaluate EXPLORA for agents trained for different purposes and showcase how it generates clear network-oriented explanations. We also show how explanations can be used to perform informative and targeted intent-based action steering and achieve median transmission bitrate improvements of 4% and tail improvements of 10%. less