Realistic emulation of wireless communication is essential for the development and validation of protocols in lowpower networks. While tools like Wmediumd provide accurate medium emulation for IEEE 802.11, no equivalent exists for IEEE 802.15.4, an important standard underlying protocols such as ZigBee, Thread, and 6LoWPAN. This paper presents an extension of Wmediumd to support IEEE 802.15.4, enabling probabilistic modeling of wireless effects such as interference, attenuation, and packet loss. By integrating with the existing mac802154_hwsim kernel module, our approach allows for accurate, reproducible experimentation in virtual environments. We demonstrate the emulator’s effectiveness through scenarios involving range-based communication and unstable links, highlighting its potential for advancing research and testing in IoT and low-power wireless networks.

Efficient routing in entanglement distribution networks is a challenging task that requires the coordinated allocation of quantum memories, repeaters, and quantum operations such as entanglement swapping and distillation across dynamic network conditions. In this paper, we present a Deep Q-Learning (DQN) Two-Stage Routing strategy designed to improve both path and quantum operations selection. Our approach enables the agent to make decisions over a diverse action space, including request scheduling, multipath routing, and the strategic application of quantum operations to maximize entanglement success and resource efficiency. We further extend DQN to integrate Double DQN, Dueling DQN, and Prioritized Experience Replay (PER) architectures. Simulation results indicate that our approach significantly outperforms baseline methods regarding request success rate, especially in large-scale networks under strict fidelity requirements.

The increasing demand for mobile 360-degree video streaming drives the need for highly immersive and smooth user experiences. However, maintaining a smooth Quality of Experience (QoE) for 360-degree video is challenging due to strict network requirements, particularly on variable mobile networks. These challenges can lead to poor streaming quality and cyber/motion sickness within immersive settings. Addressing these issues requires understanding the impact of network conditions and scalable testing under realistic scenarios. For this purpose, this paper presents an empirical analysis correlating real-world 5G radio Quality of Service (QoS) metrics from three U.S. operators with YouTube 360-degree video QoE. Our findings include a valuable dataset linking 5G radio QoS to video QoE.

With the advent of next-generation networks, new applications across multiple domains are gaining traction. This shift demands a redefined network paradigm, where ultrareliable, low-latency communication is key. In this work, we explore and extend the concept of in-network programmability in new directions. Unlike conventional approaches, we leverage P4 data plane programmability to implement an in-network collision avoidance algorithm in a UAV scenario. We evaluate our hardware-based implementation under different conditions, including latency and velocity, demonstrating that it efficiently detects and prevents collisions. Our results show the impact of end-to-end latency and highlight how in-network processing can be a valuable ally for time-sensitive tasks, paving the way for future advancements in hardware-based in-network applications.

Remote Direct Memory Access (RDMA) is a key technology in modern data centers, enabling low-latency and high-throughput communication. However, evaluating RDMA performance and validating network designs often requires costly hardware setups or simulation tools with limited performance and realism. In this work, we present P4DMA, a system that leverages programmable switch ASICs to generate realistic high-performance RDMA traffic. By implementing RDMA traffic patterns using the P4 language, P4DMA enables researchers and practitioners to generate RDMA workloads at line rate, without relying on traditional RDMA NICs (RNICS). With Tofino's traffic generation capacity of up to Tbps, P4DMA offers a novel approach to stress and evaluate RDMA-capable infrastructures, and accelerate the prototyping of new RDMA-based applications.

Interpreting complex 3GPP telecommunications standards for question and answering (QA) poses a challenge for general-purpose LLMs due to their specialized terminology and high computational demands, limiting their use in resourceconstrained environments. This work explores an efficient, opensource approach using the TeleQnA dataset of 10,000 telecom questions and the TSpec-LLM repository of processed 3GPP documents. We enhance a lightweight Llama 3.2 (3B parameters) model, quantized from 16-bit precision to 4 bits, through finetuning and RAG to improve accuracy without heavy resource reliance. Unlike prior resource-intensive or proprietary solutions, our method reduces memory demands, enabling deployment on modest hardware like edge devices or softwarized networks. Shared via GitHub repositories [1], this approach advances costeffective, reproducible AI for telecommunications QA, supporting contexts where budgets, computation, or public internet access are limited.

This paper presents an in-network machine learning (ML) approach for classifying Augmented Reality (AR) and Cloud Gaming (CG) traffic using programmable hardware. Random Forest (RF) models are deployed in a P411P4: Programming Protocol-independent Packet Processors data plane capable of processing Real-time Transport Protocol (RTP) traffic features like Frame Size (FS) and Inter-Frame Interval (IFI) for efficient classification. The classifier marks AR and CG traffic with Explicit Congestion Notification (ECN) codepoints to integrate with the Low Latency, Low Loss, Scalable Throughput (L4S) features of the programmable switch. The RF model prioritizes AR/CG traffic using Differentiated Services Code-Point (DSCP) assignments and modular ECN marking. The classification performance is evaluated using accuracy, precision, recall, and F1-score, while time overhead is assessed based on nodal processing time incurred during deployment by replaying AR/CG traffic. The P4 implementations for P4Pi22https://eng.ox.ac.uk/computing/projects/programmable-hardware/p4pi.(V1Model) and Tofino Native Architecture (TNA) are all publicly available.

Cloud Gaming (CG) research faces challenges due to the unpredictability of game engines and restricted access to commercial platforms and their logs. This creates major obstacles to conducting fair experimentation and evaluation. CGReplay captures and replays player commands and the corresponding video frames in an ordered and synchronized action-reaction loop, ensuring reproducibility. It enables Quality of Experience/Service (QoE/QoS) assessment under varying network conditions and serves as a foundation for broader CG research. The code is publicly available for further development11https://github.com/dcomp-leris/CGReplay.git.

Holographic-type communication demands multi-Gbps throughput and ultra-low latency, posing significant challenges to current 5G networks. This demo work presents a programmable testbed built on the P7 network emulator, which overcomes the throughput constraints of conventional platforms. Our demonstration deploys three edge computing slices—one at a far edge and two at near edges—to support volumetric media streaming directly to VR head-mounted display. By facilitating precise adjustment of network metrics such as latency and packet loss across the P7-enabled slices, the testbed allows users to visually observe the resulting effects on holographic content in VR. Although automated measurement of Quality of Experience (QoE) parameters are not incorporated, the testbed provides a valuable experimental environment where researchers can subjectively evaluate the relationship between network conditions and perceived quality, thereby gaining insights into optimizing network performance for enhanced Quality of Service (QoS).

The increasing demand for real-time industrial applications demands deterministic communication across networked devices. Time-sensitive networking (TSN) offers reliable performance to meet these requirements, compatible with the current Real-Time Ethernet (RTE) solutions applied in the industry. While TSN ensures determinism inside Ethernet networks, its integration with 5G/6G wireless technologies can overcome flexibility limitations. This paper explores key challenges in integrating TSN with mobile networks, focusing on device synchronization and flow mapping. We introduce a tool aligned with the 3GPP specifications that covers time synchronization across TSN domains via 5G and implements flow mapping based on application-specific quality of service (QoS) requirements. We evaluate these features under various scenarios, including a use case for an industrial production line use case. Our solution is developed as an open-source framework for the OMNeT++ simulator, supporting reproducibility and continuous updates and paving the way for customizable network solutions.

Accurate time synchronization is essential for emerging networked applications that demand low latency, high precision, and coordinated operations, such as those found in data centers, time-sensitive networking (TSN), and distributed systems. In this demo, we introduce P4Timely, a flexible and programmable framework designed to evaluate and prototype time synchronization protocols in real time. Built using P4 and commodity programmable hardware, P4Timely enables users to easily configure synchronization settings through a simple interface, specify network conditions such as delay, jitter, and packet loss, and define whether nodes act as synchronizing or synchronized entities. P4Timely supports dynamic reconfiguration at runtime and provides built-in monitoring tools to assess synchronization accuracy under diverse network conditions.

Nature has perfected efficient ways to process information-like how our brains quickly recognize faces or learn new skills using minimal energy. Neuromorphic computing is a biologically inspired computing paradigm that mimics the brain's neural architecture to achieve high efficiency and adaptive learning. Current research focuses on scaling neuromorphic systems for energy-efficient AI acceleration, robotics, video recognition, alongside advances in Spiking Neural Networks (SNNs), in-memory computing architectures (e.g., memristors), and hardware-software codesign for applications like autonomous systems and healthcare diagnostics. While neuromorphic computing has shown promise in other domains, its role in softwarized networks remains underexplored. This paper presents the early stage of the doctoral research, outlining the required investigations to leverage neuromorphic computing in softwarized networks. We discuss the methodology, involved challenges, limitations, and potential impacts of this novel intersection.

Service meshes are indispensable for enabling advanced communication features in cloud-native infrastructures. Though, due to the tight coupling of service meshes with the data path, they introduce significant overheads, resulting in degraded networking performance, including increased latency, poor throughput, and inefficient resource utilization. This paper presents an eBPF-based Service Mesh Acceleration framework, eSeMeshA, a proposal designed to mitigate these bottlenecks in intra-node communication by employing an in-kernel method to bypass costly data paths in modern service mesh architectures, such as Istio Ambient Mesh and Cilium, while also maintaining full support for legacy sidecar-based approaches, like Istio Sidecar. Experimental results show the potential of eSeMeshA to achieve significant gains, over 70% in throughput, up to$\mathbf{4 1. 9 \%}$latency reduction, and$\mathbf{3 5. 8 \%}$jitter improvement, all with minimal memory overhead of at most 30.4 MB and 23.2% less CPU usage, demonstrating its effectiveness as a scalable and efficient solution for cloud-native networking.

In-Network Computing (INC) is transforming how we design and evaluate modern networks by enabling programmable data planes to execute computational tasks at line rate. However, deciding which functions to offload, to which device and using which strategy is not a trivial task. This PhD project explores the opportunities, challenges and boundaries of offloading network functions and application logic to programmable switches and SmartNICs. Through a series of case studies, we demonstrate that INC-based solutions enhance both network efficiency and application performance. On the other hand, we also demonstrate the tradeoffs of these offloadings and how they can impact the infrastructure in terms of performance and resource utilization. Our findings provide actionable insights for the deployment of INC in real-world scenarios, highlighting where offloading delivers the most value and where traditional approaches remain preferable.

Evaluating the Quality of Experience (QoE) for over-the-top (OTT) video streaming is crucial due to increasing video traffic demands for delivering satisfactory user experiences. This work presents and evaluates a rich dataset capturing real-world 5G network performance and its impact on YouTube video streaming QoE. We utilized two US-based 5G networks to stream 2D YouTube videos and monitor cellular Key Performance Indicators (KPIs) at 1-second granularity. These KPIs include Channel Level Metrics (CLMs), such as RSRP, RSRQ, and SNR, and objective QoE metrics for various scenarios, including indoor and outdoor environments with different mobility conditions. We correlate CLMs KPIs in 5G Standalone (SA) and Non-Standalone (NSA) deployments with YouTube's objective QoE. Our study shows performance differences where 5G SA encounters more streaming stalls during outdoor mobility than NSA, while SA supports higher resolutions in static indoor settings.

The emergence of quantum computing and related technologies presents opportunities for enhancing network security. The transition towards quantum computational power paves the way for creating strategies to mitigate the constantly advancing threats to network integrity. In response to this technological advancement, our research presents QML-IDS, a novel Intrusion Detection System (IDS) that combines quantum and classical computing techniques. QML-IDS employs Quantum Machine Learning (QML) methodologies to analyze network patterns and detect attack activities. Through extensive experimental tests on publicly available datasets, we show that QML-IDS is effective at attack detection and performs well in binary and multiclass classification tasks. Our findings reveal that QML-IDS outperforms classical Machine Learning methods, demonstrating the promise of quantum-enhanced cybersecurity solutions for the age of quantum utility.

Network demand for real-time applications like self-driving cars and cloud gaming strains existing networks. Latency and congestion hurt user experience. Realistic testing is vital to improving networks, but real-world data is scarce. In this context, we propose to analyze existing network data and identify traffic patterns and anomalies. We believe this knowledge can be used to feed generative adversarial network (GAN) models, which can create realistic synthetic data, supplementing existing real traces while protecting end-user privacy. This augmented data can then be used, for instance, to empower improved routing algorithms designed to benefit from programmable hardware (e.g., SmartNICs) and collected data plane metrics, paving the way for improved network performance and enhanced user experience with more autonomous decisions. This paper presents our initial analysis of synthetic network data generation technologies and summarizes the main ideas guiding my Ph. D. research.

The pursuit of self-driving networks is increasing pressure on adopting intelligent, edge-based networking services. However, deploying autonomous network models within operational and large-scale infrastructures entails substantial risks that require rigorous verification and validation procedures. In this context, the application of a Network Digital Twin (NDT) is emerging as a viable approach towards intelligent network decision-making based on high-fidelity models built upon digital representations of physical network devices (i.e., Digital Twins). In this paper, we take the first steps towards efficiently provisioning NDT models. To that end, we introduce the Digital Twin Network Provisioning Problem (DigiNet), which encompasses the optimal placement of NDT models and the efficient collection of telemetry data for synchronizing NDT models with their physical counterparts. We theoretically formalize DigiNet as a Mixed-Integer Linear Programming (MILP) model and present a polynomial-time heuristic. Our results show that DigiNet outperforms baseline approaches by up to 10x regarding the number of NDT models provisioned.

Augmented Reality (AR) real-time interaction between users and digital overlays in the real world demands low latency to ensure seamless experiences. To address computational and battery constraints, AR devices often offload processing-intensive tasks to edge servers, enhancing performance and user experience. With the increasing adoption and complexity of AR applications, especially in remote rendering, accurately classifying AR network traffic becomes essential for effective resource allocation. This paper explores two methods based on Decision Tree (DT) and Random Forest (RF) to classify network traffic among AR, Cloud Gaming (CG), and other categories. We rigorously analyze specific features to precisely identify AR and CG traffic. Our models demonstrate robust performance, achieving accuracy rates ranging from 88.40% to 94.87% against pre-existing datasets. Moreover, we contribute with a novel dataset encompassing AR and CG traffic, curated specifically for this study and made publicly available to facilitate reproducible research in AR network traffic classification.

In recent years, the increasing demand for network resources due to real-time applications and data-intensive activities has posed challenges in managing and optimizing network performance. To assess network performance, security, and efficiency, traffic generation plays a crucial role. We introduce PIPO-TG, a Tofino-based traffic generation for high-performance experiments. The primary objective of PIPO-TG is to generate realistic and diverse traffic patterns, enabling researchers to evaluate network performance under varying conditions providing customizable packet forwarding with P4 programmable data planes. Our main contributions include user-defined packet header customization and open-source code for reproducibility. These efforts foster collaboration within the research community to advance traffic generation techniques. We show that PIPO-TG only requires a few lines of code to simulate heterogeneous network scenarios (e.g., traffic bursts and DDoS attacks) while maintaining hardware performance and flexibility.

Given the dominance of adaptive video streaming services on the Internet traffic, understanding how YouTube Quality of Experience (QoE) relates to real 4G and 5G Channel Level Metrics (CLM) is of interest to not only the research community but also to Mobile Network Operators (MNOs) and content creators. In this context, we collect YouTube and CLM logs with 1-second granularity spanning a six-month period. We group the traces by their context, i.e., Mobility, Pedestrian, Bus/Railway terminals, and Static Outdoor, and derive key performance footprints of real 4G and 5G video streaming in the wild. We also develop Machine Learning (ML) classifiers to predict objective QoE video stalls by using past patterns from CLM traces. We release all datasets and software artifacts for reproducibility purposes.

In-band Network Telemetry (INT) is a novel network monitoring approach mainly fostered by programmable network devices. Despite existing efforts toward the orchestration of INT, little has yet been done to provide fault-tolerant mechanisms in the data plane (e.g., to address hardware failure). In this paper, we introduce InPatching - an in-network approach to fast recover INT-based monitoring from network link failures. InPatching is implemented in the data plane and allows the application of detours in an autonomous and coordinated manner without the control plane intervention. To provide efficient detours to INT solutions, we formalize the fault-tolerant probing planning for INT by means of a MILP (Mixed-Integer Linear Programming) model. We prototype InPatching in P4 and we show that it can recover from fault conditions much faster than control plane solutions (up to 18X), while not imposing substantial overhead.

Network emulation traditionally relies on software-based solutions. While extremely useful in many scenarios, it suffers from performance fidelity and inherent scalability constraints. With the advent of P4 and programmable switches like Tofino, new opportunities for hardware-based network emulation are emerging. P7 (P4 Programmable Patch Panel) offers a solution for high-fidelity 100G traffic network emulation, including different link characteristics such as latency, jitter, packet loss, and bandwidth, as well as the ability to define custom topologies. However, it currently lacks support for custom P4 code in emulated devices. This is where multiple pipelines network emulation comes in. In this demonstration, we show how to emulate a topology using P7 and incorporate custom P4 code into each emulated node. We allocate a dedicated pipe for user-defined P4 code and allow users to conFigure tables for each node separately.

This work presents Chat-IBN-RASA, a conversational AI chatbot based on the open-source RASA framework, acting as an Intent Translator component for Intent-Based Networking (IBN) architectures. The IBN-driven RASA chatbot allows users to interact with the network management system using high-level language communication without the need for in-depth technical knowledge of the network. The chatbot is trained using Natural Language Understanding (NLU) models, a defined domain, stories, and custom actions for API communication and database queries. We present the prototype implementation in a use case of survivability intents in packet-optical networks. The custom actions featured include communication with the network database, path computation, and the recommended path for intent deployment, considering availability, protection type, data rate, and link distances.

The growing popularity of eXtended Reality (XR) is being driven by technological advancements and the demand for advanced immersive digital experiences, including the vision around the metaverse. Within the XR realm, 360-degree immersive video streaming is essential for Virtual Reality (VR) adventures and experiences. The use of E2E encryption for content delivery in 360-VR streaming poses challenges for network operators, making it difficult to manage their networks and assess potential Quality of Experience (QoE) impairments, specifically in 5G and beyond networks. Therefore, we propose a Machine Learning (ML) approach for inferring 360-VR video QoE metrics from network-level encrypted traffic. Our solution uses packet-level information for feature engineering, which serves as input for the ML model to predict target QoE estimators. We evaluate our solution using real 4G and 5G drive test traces with encrypted VR traffic using HTTPS and QUIC protocols. The experimental results show that the trained ML model yields reasonable accuracy with minimal residual error in predicting target VR QoE for both HTTPS and QUIC. Network operators can use such a model to passively monitor the real-time QoE of encrypted VR video sessions and optimize network performance.

In recent years, advances in virtual reality (VR) technologies (e.g., high-quality VR headsets) have enabled a new perspective of experiences for users (e.g., gaming, online events). However, ensuring the user experience is still a challenge. Existing solutions are limited to measuring and estimating QoE at the user plane (e.g., VR player) or at the control plane, imposing unfeasible latency for different scenarios (5G networks and beyond). In this work, we propose QoEyes, an in-network QoE estimation based on the use of Inter-Packet-Gap (IPG) in programmable devices. Our results show that the IPG measured on the data plane is strongly linked to QoE, yielding an accurate data plane QoE estimate.

Recent advances in VR technology have created new user experiences (e.g., online events, gaming). However, ensuring the user experience is still a challenge. Mostly because Quality of Experience (QoE) measurement is limited to the user or control plane, causing high latencies for different scenarios (e.g., 5G networks and beyond). To address this challenge, we present QoEyes, an in-network QoE estimation technique based on Inter-Packet-Gap (IPG) measured in programmable devices. Our results show that a strong estimate of the user’s QoE can be provided by measuring the IPG on the data plane. Additionally, in this demonstration, we show this QoE estimate and other related metrics in real time, using a Grafana dashboard running in our monitoring server.

The exponential increase in Dynamic Adaptive Streaming over HTTP (DASH) based video traffic with endto-end encryption poses many challenges for Mobile Network Operators (MNOs). To improve user-perceived video quality, MNOs must be aware of the end user’s Quality of Experience (QoE) by exploring network level Quality of Service (QoS). On the contrary, the network edge facility provides proximity for performing an intelligent operation to maintain smooth QoE from the network level QoS measurement. Therefore, in this work, we propose EFFECTOR, a framework to showcase lightweight in-band QoS features measurement technique at edge nodes from encrypted DASH video traffic. EFFECTOR uses an emulated environment with real 4G and 5G drive test traces to generate video traffic. Moreover, this work provides a massive dataset for analyzing the impact of 4G and 5G technology on video quality in the form of Interactive Jupyter Notebooks. The proposed framework is ideal for investigating QoS extracted from the network’s edge and finding its relations with QoE to ensure better video quality for end-users.

Demand for wireless video streaming services increases with users expecting to access high-quality video streaming experiences. Ensuring Quality of Experience (QoE) is quite challenging due to varying bandwidth and time constraints. Since most of today’s mobile devices are equipped with multiple network interfaces, one promising approach is to benefit from multipath communications. Multipathing leads to higher aggregate bandwidth and distributing video traffic over multiple network paths improves stability, seamless connectivity, and QoE. However, most of current transport protocols do not match the requirements of video streaming applications or are not designed to address relevant issues, such as networks heterogeneity, head-of-line blocking, and delay constraints. In this comprehensive survey, we first review video streaming standards and technology developments. We then discuss the benefits and challenges of multipath video transmission over wireless. We provide a holistic literature review of multipath wireless video streaming, shedding light on the different alternatives from an end-to-end layered stack perspective, reviewing key multipath wireless scheduling functions, unveiling trade-offs of each approach, and presenting a suitable taxonomy to classify the state-of-the-art. Finally, we discuss open issues and avenues for future work.

This paper focuses on hybrid pipeline designs for User Plane Function and next-generation NodeB leveraging target-specific features and an insightful discussion of P4 and target challenges and limitations. The entire or disaggregated UPF runs on P4 targets and allocates packet processing data paths in P4 hardware or DPDK/x86 software based on flow characteristics (e.g., heavy hitters) and QoS requirements (e.g., low-latency slices). For the hybrid gNodeB, most packet processing is executed in commodity Tofino hardware, while unsupported functions such as Automatic Repeat Request and cryptography are performed in DPDK/x86. We show that our hybrid UPF improves the scalability by 18× and reduces latency up to 50%. The results also suggest that careful traffic allocation to pipeline targets is required to optimize each target's strength and avoid processing delays. Finally, we demonstrate a QoS-oriented application of the hybrid UPF and present gNodeB buffer service benchmarks.

The research community has recently proposed several solutions based on modern programmable switches to detect entirely in the data plane the flows exceeding pre-determined threshold in a time window, i.e., Heavy Hitters (HH). This is commonly achieved by dividing the network stream into fixed time slots and identifying each separately without considering the traffic trends from previous intervals. In this work, we show that using specified time windows can lead to high inaccuracies. We make a case for rethinking how switches analyze the incoming packets and propose to leverage per-flow Inter Packet Gap (IPG) analytics instead of using flow counters for HH detection. We propose an algorithm and present a P4 pipeline design using this new metric in mind. We implement our solution on P4 hardware and experimentally evaluate it against real traffic traces. We show that our results are more accurate than related work by up to 20% while reducing the control channel overhead by up to two orders of magnitude. Finally, we showcase a QoS-oriented application of the proposed dataplane-only IPG-based HH detection in a mobile network scenario.

The edge computing infrastructure can scale from datacenters to single device. The well-known technology for fast packet processing is DPDK, which has outstanding performance regarding the throughput and latency. However, there are some drawbacks when the usage is done in the edge: (i) the polling mechanism for packet processing keeps the CPU exclusively occupied even if there is no traffic, leading to wasted resources; and (ii) DPDK interface becomes unavailable for the applications inside the host, so the integration between a non-DPDK application and a DPDK application becomes a hard task. In this paper, we propose an open-source in-kernel 5G UPF solution based on 3GPP Release 16 to be deployed in a restrictive environment like MEC, where MEC host and UPF are collocated with the Base Station, sharing the same computational and network resources. The solution leverages the eBPF/XDP, a novel Linux kernel technology for fast packet processing. We show it can scale and achieve 10 Mpps using only 60% of the CPU with 6 cores.

Vehicular networks are critical pieces in support of advanced intelligent transportation systems (ITS). These networks are formed by vehicles that can be connected to one another as well as to the infrastructure, and are subject to constant topology changes, disconnections, and data congestion. Each ITS application could have a different set of communication requirements, such as delay, bandwidth, and packet delivery ratio. Meeting these heterogeneous requirements in the complex dynamic environment of vehicular networks is a challenge. This paper develops a new framework for application-driven vehicular networks using 5G network slicing. We present the architecture of the proposed solution and design algorithms for heterogeneous traffic in a dynamic vehicular environment. Our simulations on realistic vehicular scenarios show significant improvements in network performance compared to the state-of-the-art approaches.

In this article, we introduce a novel dynamic controller assignment algorithm targeting connected vehicle services and applications, also known as Internet of Vehicles (IoV). The proposed approach considers a hierarchically distributed control plane, decoupled from the data plane, and uses vehicle location and control traffic load to perform controller assignment dynamically. We model the dynamic controller assignment problem as a multi-agent Markov game and solve it with cooperative multi-agent deep reinforcement learning. Simulation results using real-world vehicle mobility traces show that the proposed approach outperforms existing ones by reducing control delay as well as packet loss.

Terrestrial infrastructure-based wireless networks do not always guarantee their resources will be shared uniformly by nodes in vehicular networks. This is due mainly to the uneven and dynamic geographical distribution of vehicles and path loss effects. In this paper, we leverage multiple fifth-generation (5G) unmanned aerial vehicles (UAVs) to enhance fairness in network resource allocation among vehicles by positioning UAVs on-demand as “flying communication infrastructure”. We propose a deep reinforcement learning (DRL) approach to determine UAVs’ position to improve network resource allocation fairness and efficiency while considering the UAVs’ flying range, communication range, and energy constraints. We use a parametric fairness function to attain a number of resource allocation objectives ranging from maximizing the total throughput of vehicles, maximizing minimum throughput, and achieving proportional bandwidth allocation. Simulation results show that the proposed DRL approach to UAV positioning can improve network resource allocation according to the targeted fairness objective.

Fifth Generation (5G) networks provide high throughput and low delay, contributing to enhanced Quality of Experience (QoE) expectations. The exponential growth of multimedia traffic pose dichotomic challenges to simultaneously satisfy network operators, service providers, and end-user expectations. Building QoE-aware networks that provide run-time mechanisms to satisfy end-users' expectations while the end-to-end network Quality of Service (QoS) varies is challenging, and motivates many ongoing research efforts. The contribution of this work is twofold. Firstly, we present a reproducible data-driven framework with a series of pre-installed Dynamic Adaptive Streaming over HTTP (DASH) tools to analyse state-of-art Adaptive Bitrate Streaming (ABS) algorithms by varying key QoS parameters in static and mobility scenarios. Secondly, we introduce an interactive Jupyter notebook and Binder service providing a live analytical environment, which processes the output dataset of the framework and compares the relationship of five QoE models, three QoS parameters (RTT, throughput, packets), and seven different video KPIs.

Due to the advanced control and machine learning techniques, today's industrial robots are faster and more accurate than human workers in well-structured repetitive tasks. However, in case of sudden changes in the operational area, such as unexpected obstacles or humans, robots have to be continuously monitored by powerful controllers for swift interventions (i.e., send emergency stop signals). As in the case of many verticals (e.g., transportation, shopping), the proliferation of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) has started to captivate industry 4.0 as well in order to benefit from low infrastructure costs, and flexible management and resource provisioning. Besides all the advantages of the centralized approach, however, in critical situations (e.g., possible collisions, actuator damages or human injuries) the required ultra-low latency between the robots and the controller becomes an all-important factor, and one of the main concerns, at the same time, for industry leaders making the decision towards this paradigm shift. In this paper, we argue that by relying on recently emerged stateful and programmable data planes, it is possible to fill this gap by offloading latency-critical applications to the network, thereby bringing some intelligence much closer the robots. We present the first in-network robotic control application that is capable to intercept the communication between the robot and the controller and craft responses immediately if needed. In particular, we show that we can detect position threshold violations entirely in the data plane, close to the robot, and deliver emergency stop commands within no time with full compliance to the actual TCP session and application states.

Scenarios for distributed cloud with multiple edge clouds and centralized data centers are being investigated as the computing and networking underpinnings of next-generation network services such as augmented reality, self-driving vehicles, drones, and more. In such distributed environments, service providers will typically face tens, hundreds, or thousands of compute location candidates (edge, regional, and central) where network service components can be placed. To take optimized placement decisions of network services and execute the management workflows, orchestration systems require up-to-date and accurate resource availability representation, in the form of a network inventory that can be immense in distributed cloud scenarios. As a result, the service management and placement problems may become not tractable. In this work, we propose the Abstracted Network Inventory (ANI) component to generate service-optimized network views over the same network inventory. ANI implements a novel abstraction method where network service requirements are used as an input to generate an optimized abstract network inventory representation, called Logical Network Inventory (LNI). We also provide a formal definition of the network model and problem statement along with the development of three algorithms to efficiently build an LNI. Results show the potential benefits of using an LNI to streamline service management and placement: (i) the relationship between compute nodes and links (i.e., density) in an LNI is reduced between 1.8-2.7x compared to a full network inventory topology; and (ii) up to 50% of time can be saved for service placement after abstracting around 20% of the compute nodes.

Intent-Based Networking (IBN) proposals are based on autonomous closed-loop orchestration architectures that monitor and tune network performance. To this end, IBN defines high-level policies and actions implemented by a closed-loop system. This work demonstrates a Closed Control Loop (CCL) architecture for video service assurance using Machine Learning (ML) based Quality of Experience (QoE) estimation at edge nodes. As part of the solution, network-level Quality of Service (QoS) metrics patterns (e.g., RTT, Throughput) collected through flow-level monitoring are used to build a QoS-to-QoE correlation model tailored to specific target network regions, user groups, and services, in our case DASH video streaming. The demo will showcase the CCL workflow triggering the Orchestrator to take appropriate network-level actions to overcome network QoS degradations and restore the QoE target based on the intent associated with the video service.

The concept of cloud network slice features not only an end-to-end infrastructure with different types of resources (cloud, network and storage), but also the ability to provide services across multi-domain infrastructures. Therefore, an important challenge encountered is the ability of a slice to increase or decrease its resources according to SLAs/SLOs defined by a Tenant. This demo aims to present the implementation of a monitoring system architecture for cloud network slices, in which such main characteristics can be highlighted: (i) elasticity - as it is able to adapt as new elements are instantiated or removed in the slice; and (ii) heterogeneity - as it runs across multiple administrative and technological domains; which are intrinsic features of the cloud network slice concept.

A key feature of the cloud network slicing concept is the dynamic (de)provision of end-to-end infrastructures composed by computing, network, and storage resources, in order to meet the service needs of a variety of vertical industries. The resulting resource ensemble needs to be instantiated over a (potentially large) number of administrative and technological domains – a complex challenge for the management and orchestration of the allocated resources. Resource monitoring in this new so-called slice entity is extremely important in order to address the above operations. Therefore, in this work, we discuss the design and implementation of an elastic architecture for the monitoring of physical and virtual resources in cloud network slices that span both multiple administrative and technological domains. Since the concept of cloud network slices is quite recent in the literature, we highlight that our results present the performance evaluation of the proposed monitoring architecture when instantiating, stopping or performing elasticity operations on the slices. Additionally, we also analyze the performance of the architecture by scaling the number of metrics being monitored in overloaded scenarios.

Dynamic end-to-end cloud network slices following a Slice-as-a-Service (SaaS) paradigm offer promising opportunities in support of diverse vertical industries attached to different administrative domains. This paper presents a distributed Marketplace model to dynamically create cloud network slices involving different slice resource providers. Portrayed through the lens of three reference use case scenarios, we discuss potential attributes of having a Marketplace and the outcomes feeding the use case requirements within a Slice Marketplace scope. Effective selection, negotiation, and proper monitoring and enforcement of service level agreements between multiple administrative and technological domains are among the prime challenges towards the realization of a distributed Marketplace of federated network and cloud resource providers.

In this special issue, we have collected and presented recent works on innovative approaches and emerged technologies for coping with dynamicity, heterogeneity, and the scale, which have been central to (or even enablers of) recent advances in communications and networking technologies. At a time of an ever-increasing demand for networking resources and a larger scale, communication networks have faced challenges due to the heterogeneity of the demands, the diversity of communication mechanisms, the high dynamicity of the environments, the virtualization of functions, and the stringent and dynamic quality requirements. In recent years, there have been notable advancements in research and development of concepts and methods for highly adaptive and scalable communication networks.This special issue focuses on recent advances in the field of adaptive and scalable communications.

Bandwidth constraints and high end-to-end delays are real challenges for achieving and sustaining high quality mobile video streaming services. Diverse multipath transmission techniques are being investigated as possible solutions, since recent developments have enabled mobile devices users to receive video data simultaneously over multiple interfaces (e.g., LTE and WiFi). While some multipath protocols have been recently standardized for this purpose (e.g., MPTCP), being network layer protocols they cannot properly handle challenging transmission scenarios subject to packet losses and congestion, such as lossy wireless channels. In this work, we adopt the MPEG Media Transport (MMT) protocol to propose an improvement for mobile multipath video streaming solutions. MMT is an application layer protocol with inherent hybrid media delivery properties. We propose a novel path-and-content-aware scheduling strategy for MMT by means of full cooperation between network metrics and video content features. Our strategy provides better models to adaptively cope with unstable communication channel conditions and to improve the final user quality of experience (QoE). For the experimental evaluation, we used NS3-DCE to simulate a realistic multipath network scenario which includes channel error models and background traffic. Results for two video sequences are presented in terms of PSNR, SSIM, goodput, delay and packet loss rates. When compared with a simple scheduling strategy for the traditional multipath MMT, our approach yields significant packet loss rate reductions (~90%) and video quality improvements of around 12 dB for PSNR and 0.15 for SSIM.

Despite having received less attention compared to the control and application plane aspects of software-defined networking (SDN), the data plane is a critical piece of the puzzle. P4 takes SDN datapaths to the next level by unlocking deep programmability through a target-independent high-level programming language that can be compiled to run on a variety of targets (e.g., ASIC, FPGA, and GPU). This paper presents the design and evaluation of our sweet spot approach on SDN datapaths, offering three contending characteristics, namely, performance, portability, and scalability in multiple realistic scenarios. The focus is on our Multi-Architecture Compiler System for Abstract Data Planes proposal, which blends the high-level protocol-independent programmability of P4 with low-level but cross-platform (HW & SW) Application Programming Interfaces brought by OpenDataPlane, this way supporting many different vendors and architectures. Besides the performance evaluation for varying packet sizes and memory lookup tables, we investigate the impact of increasing pipeline complexity ranging from elemental L2 switching to more complex data center and border network gateways. We investigate the scalability for increasing the number of cores and evaluate a novel method for run-time core reallocation. Furthermore, we run experiments on different target platforms (e.g., ×86, ARM, 10G/100G), inducing different ways of packet mangling through specific drivers (e.g., DPDK and Netmap), and compare the results to state-of-the-art datapath alternatives.

We propose D2-ITS, a flexible and extensible framework to dynamically distribute network control to enable message dissemination in Intelligent Transport Systems (ITS). By decoupling the control-from the data plane, D2-ITS leverages network programmability to address ITS scalability, delay intolerance and decentralization. It uses a distributed control plane based on a hierarchy of controllers that can dynamically adjust to environment- and network conditions in order to satisfy ITS application requirements. We demonstrate the benefits of D2-ITS through a proof-of-concept prototype using the ns-3 simulation platform. Results indicate lower message delivery latency with minimal additional overhead.

Accommodating sustained exponential traffic growth in optical networks requires scaling the spatial dimension using space-division multiplexing. Numerous uncoupled spatial channels may be realized by activating multiple parallel fibers or cores in multicore fibers. A multiplicity of uncoupled spatial channels will render the granularity provided by multiple wavelength channels less essential in enabling optical switching. In this paper, we investigate a possible paradigm shift in optical node architectures, in which nodes based on wavelength-selective switching are replaced by those based on simple spatial switching. We compare spatial switching of full-spectrum superchannels to wavelength switching of uncoupled spatial superchannels, considering the evolution of traffic over time. Our results show that spatial switching may achieve more efficient scaling than wavelength switching in roughly 10 to 17 years, depending on the traffic growth rate assumed.

Deep programmability of dataplane pipelines is one of the tenets of the evolving Software Defined Networking (SDN) paradigm. Despite recent efforts on high performance programmable devices, achieving fully programmability (protocol independent) of heterogeneous dataplane implementations still pose numerous challenges. The P4 language is emerging as a strong candidate top-down approach to describe a protocol independent datapath pipeline, agnostic to network platforms. Meanwhile, the OpenDataPlane (ODP) project follows an open-source, bottom-up approach seeking multi-architecture APIs to write platform independent dataplane applications. In this paper, we present Multi-Architecture Compiler System for Abstract Dataplanes (MACSAD) as an approach to converge P4 and ODP through a common compilation process delivering portability of dataplane applications without compromising target performance improvements. We validate our prototype implementation through experimental evaluation of L2 and L3 dataplane applications on different target platforms (×86, ×86+DPDK, ARM-SoC).

Software-Defined Wireless Networking (SDWN) is an emerging approach based on decoupling radio control functions from the radio data plane through programmatic interfaces. Despite diverse ongoing efforts to realize the vision of SDWN, many questions remain open from multiple perspectives such as means to rapid prototype and experiment candidate software solutions applicable to real-world deployments. To this end, emulation of SDWN has the potential to boost research and development efforts by re-using existing protocol and application stacks while mimicking the behavior of real wireless networks. In this article, we provide an in-depth discussion on that matter focusing on the Mininet-WiFi emulator design to fill a gap in the experimental platform space. We showcase the applicability of our emulator in an SDN wireless context by illustrating the support of a number of use cases aiming to address the question on how far we can go in realistic SDWN experiments, including comparisons with the results obtained in a wireless testbed. Finally, we discuss the ability to replay packet-level and radio signal traces captured in the real testbed toward a virtual yet realistic emulation environment in support of SDWN research.

Software Defined Networking (SDN) is a promising architectural approach based on a programmatic separation of the control and data planes. For high availability purposes, logically centralized SDN controllers follow a distributed implementation. While controller role features in the OpenFlow protocol allow switches to communicate with multiple controllers, these mechanisms alone are not sufficient to guarantee a resilient control plane, leaving the actual implementation as open challenge for SDN designers. This paper explores OpenFlow roles for the design of resilient SDN control plane and proposes AR2C2 as an actively replicated multi-controller strategy. As proof of concept, AR2C2 is implemented based on the Ryu controller and relying on OpenReplica to ensure consistent state among the distributed controllers. Our prototype is experimentally evaluated using real commodity switches and Mininet emulated environment. Results of the measured times to recover from failures for different workloads shed some light on the practical trade-offs on replication overhead and latency as a step forward towards SDN resiliency.

Software-Defined Wireless Networking (SDWN) is being considered an appealing paradigm to design and operate wireless networks through higher-level abstractions and programmatic interfaces such as the OpenFlow protocol. Identified benefits include cost savings, service velocity and customization, resource optimization through novel approaches to user mobility, traffic offloading, multi-layer and multi-path routing, and so on. This demonstration features Mininet-WiFi as a SDWN emulator with the ability to run realistic experiments in hybrid physical-virtual environments, where users attending the conference are able to experience first hand by connecting their devices and interacting with virtual WiFi stations in a wireless mesh network or reach the Internet through the emulated SDWN infrastructure. OpenFlow 1.3 metering and IP header re-writing actions will showcase HTTP flow redirection and rate limitation of real users' wireless traffic.

Software Defined Networking (SDN) strives for deep programmable hardware and software dataplanes without giving up on performance. Domain Specific Languages (DSL) such as P4 seek to provide top-down high-level capabilities to define the datapath pipeline agnostic to the network platform and independent from any network protocols. At the crossroads, bottom-up industry efforts at the OpenDataPlane (ODP) initiative are pursuing open-source multiarchitecture APIs for dataplane programmability across various networking platforms. Towards P4 code reuse for various targets (portability), we propose MACSAD as a compiler system that brings together the higher-level P4 language and the abstract, target-independent ODP APIs. The demo showcases two P4 applications compiled into heterogeneous datapath platforms supporting ODP.

There is currently a strong interest in both industry and academia in the softwarization of telecommunication networks and cloud computing infrastructures. This evolution is enabled by three paradigms. First, Software-Defined Networking (SDN) allows network control to be separated from the forwarding plane and allows for a flexible management of the network resources. Second, Network Virtualization (NV) brings virtualization concepts to the network, similar to cloud computing, which was enabled by virtualization of servers. Third, Network Function Virtualization (NFV) focuses on virtualization of software-based network functions. Instead of installing and managing dedicated hardware devices for these functions, they are implemented as software components and deployed on commodity hardware infrastructures, in most cases operated by a network operator or cloud infrastructure provider. Service Function Chaining (SFC) consists of building services using virtual network functions (VNFs). These three paradigms are synergetic and reinforce each other when used together. Several initial SDN, NV and NFV deployments are already operational in providers’ networks.

As the density of wireless networks continues to grow with more clients, more base stations, and more traffic, designing cost-effective wireless solutions with efficient resource usage and ease to manage is an increasing challenging task due to the overall system complexity. A number of vendors offer scalable and high-performance wireless networks but at a high cost and commonly as a single-vendor solution, limiting the ability to innovate after roll-out. Recent Software-Defined Networking (SDN) approaches propose new means for network virtualization and programmability advancing the way networks can be designed and operated, including user-defined features and customized behaviour even at run-time. However, means for rapid prototyping and experimental evaluation of SDN for wireless environments are not yet available. This paper introduces Mininet-WiFi as a tool to emulate wireless OpenFlow/SDN scenarios allowing high-fidelity experiments that replicate real networking environments. Mininet-WiFi augments the well-known Mininet emulator with virtual wireless stations and access points while keeping the original SDN capabilities and the lightweight virtualization software architecture. We elaborate on the potential applications of Mininet-Wifi and discuss the benefits and current limitations. Two use cases based on IEEE 802.11 demonstrate available functionality in our open source developments.

Content-Centric Networking (CCN) is a promising architectural approach that focuses on the efficient distribution of uniquely named data objects. A piece of content is represented by a single object in the network and is divided into multiple chunks which can be uniquely named and cached by network nodes. However, in its current form, the potential of CCN is not fully exploited due to the lack of common means to express and take advantage from possible relations that may exist among different objects. Our work explores the simple yet effective idea of supporting and exploiting such relations in CCN. In this paper, we present CCNrel as a backward-compatible mechanism for CCN that enables publishers to distribute contents as related objects. Differently from existing relation mechanisms, which focus on one type of content and are application-specific, CCNrel is generic and enables the use of relations in both current and novel application domains. First, we discuss CCNrel fundamental concepts and main design aspects. Next, we use CCNrel as foundation for a case study of data redundancy elimination in multimedia content distribution. Through extensive simulation work we evaluate the potential benefits of leveraging relations measured by the clients experience and overall network efficiency. Results of the presented use case show that, on average and when compared to default CCN operations, content download times are improved in 34%, publishers load in 56%, and the network bandwidth usage in 43%.

The Internet has led to the creation of a digital society, where (almost) everything is connected and is accessible from anywhere. However, despite their widespread adoption, traditional IP networks are complex and very hard to manage. It is both difficult to configure the network according to predefined policies, and to reconfigure it to respond to faults, load, and changes. To make matters even more difficult, current networks are also vertically integrated: the control and data planes are bundled together. Software-defined networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns, introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper, we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound application programming interfaces (APIs), network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms - with a focus on aspects such as resiliency, scalability, performance, security, and dependability - as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.

This paper presents a proposal for shrinking the number of IPv4 FIB (Forwarding Information Base) entries required on routers. Traffic forwarding under the proposed mechanism is based on the current ASNs (Autonomous System Numbers), and can be gradually adopted by ISPs.We find that, at the cost of adding 8 bytes per packet, the proposed ASN-FWD technique is capable of providing full IPv4 traffic forwarding based on ASN information, which is correspondent to 10% of the current number of IPv4 prefixes present on FIB of routers. Among its main benefits, the proposed approach alleviates the pressure on the amount of FIB shipped on routers, and paves the way for a worldwide adoption of IPv6.

Experimentally-driven research is crucial to the evaluation of any networking technology. In this paper, we focus on research experiments with Mini-CCNx, a convenient yet high-fidelity prototyping tool to evaluate Information-Centric Networking (ICN) proposals that bring named pieces of content as the main element of networks. More specifically, Mini-CCNx is tailored for the Named Data Networking (NDN) model and fills an existing gap in generally available experimental platforms. Using container-based emulation and resource isolation techniques, Mini-CCNx appears as a flexible, scalable, high-fidelity, and low-cost tool in support of NDN research. To evaluate Mini-CCNx as a valid research platform, we first reproduce a series of experiments from the NDN literature. We then run routing experiments on an emulated version of the real NDN testbed and provide insights into open research topics such as behavior of the OSPFN routing protocol, forwarding strategies, and network resiliency.

Software Defined Networking (SDN) is an active area for network research, with many organizations exploring the opportunities provided by the decoupling of network control from packet forwarding. However, questions remain over the operation of such systems in production. In order to build operational confidence, we built Cardigan -a distributed router using OpenFlow-and deployed it at a public Internet exchange. Cardigan applies a routing as a service abstraction to a RouteFlow controlled IP network in an effort to reduce operational complexity. The implementation and deployment efforts provide insights into the challenges involved with using these technologies, and suggests the viability of mixed device environments despite the limitations of early OpenFlow implementations.

Software Defined Networking (SDN) has opened new ways to design, deploy, and operate networks with new abstractions and programmability at network control and data planes. In this paper, we approach SDN to embed virtual data center networks employing the Network-as-a-Service model. The proposed architecture is built upon abstractions to create a virtual topology using BGP configurations that allow an efficient mapping to a physical network of OpenFlow 1.3 switches. In the control plane, an algorithm is designed to perform efficient allocation of network resources to virtual paths based on the data plane state, such as allocated virtual networks and resource utilization metrics. Requirements such as bandwidth and resilience are used to define the tenants policies and construct the virtual topology graph mappings. The experimental evaluation on an emulated testbed shows that the proposed algorithm performs efficient load balancing and altogether yields better utilization of the physical resources under different tenant traffic patterns.

Recent proposals on Data Center Networks (DCN) are based on centralized control and a logical network fabric following a well-controlled baseline topology. The architectural split of control and data planes and the new control plane abstractions have been touted as Software-Defined Networking (SDN), where the OpenFlow protocol is one common choice for the standardized programmatic interface to data plane devices. In this context, source routing has been proposed as a way to provide scalability, forwarding flexibility and simplicity in the data plane. One major caveat of source routing is network failure events, which require informing the source node and can take at least on the order of one RTT to the controller. This paper presents SlickFlow, a resilient source routing approach implemented with OpenFlow that allows fast failure recovery by combining source routing with alternative path information carried in the packet header. A primary and alternative paths are compactly encoded as a sequence of segments written in packet header fields. Under the presence of failures along a primary path, packets can be rerouted to alternative paths by the switches themselves without involving the controller. We evaluate SlickFlow on a prototype implementation based on Open vSwitch and demonstrate its effectiveness in a Mininet emulated scenario for fat-tree, BCube, and DCell topologies.

Network virtualization is one of the foundations for Future Internet architectures, able to catalyze technologies for the solution of classical problems in the IP layer. The work here gives a step forward in that direction, evaluating the performance of different virtual network tools under the perspective of a virtualized IP routing control plane. Two lightweight container-based virtualization systems, OpenVZ and LXC, were selected based on specific criteria and evaluated in the context of virtual routers. Obtained results allow for a comparative analysis about the convergence of the routing protocol and give insights about the adoption of these tools on a routing as a service platform.

Several recently proposed multicast protocols use in-packet Bloom filters to encode multicast trees. These mechanisms are in principle highly scalable because no per-flow state is required in the routers and because routing decisions can be made efficiently by simply checking for the presence of outbound links in the filter. Yet, the viability of previous approaches is limited by the possibility of forwarding anomalies caused by false positives inherent in Bloom filters. This paper explores such anomalies, namely (1) packets storms, (2) forwarding loops and (3) flow duplication. We propose stateless solutions that increase the robustness and the scalability of Bloom filter-based multicast protocols. In particular, we show that the parameters of the filter need to be varied to guarantee the stability of the packet forwarding, and we present a bit permutation technique that effectively prevents both accidental and maliciously created anomalies. We evaluate our solutions in the context of BloomCast, a source-specific inter-domain multicast protocol, using analytical methods and simulations.

Computing history has shown that open, multi-layer hardware and software stacks encourage innovation and bring costs down. Only recently this trend is meeting the networking world with the availability of entire open source networking stacks being closer than ever. Towards this goal, we are working on QuagFlow, a transparent interplay between the popular Quagga open source routing suite and the low level vendor-independent OpenFlow interface. QuagFlow is a distributed system implemented as a NOX controller application and a series of slave daemons running along the virtual machines hosting the Quagga routing instances.

A large fraction of today's Internet applications are internally publish/subscribe in nature; the current architecture makes it cumbersome and inept to support them. In essence, supporting efficient publish/subscribe requires data-oriented naming, efficient multicast, and in-network caching. Deployment of native IP-based multicast has failed, and overlay-based multicast systems are inherently inefficient. We surmise that scalable and efficient publish/subscribe will require substantial architectural changes, such as moving from endpoint-oriented systems to information-centric architectures.

In response to the limitations of the Internet architecture when used for applications for which it was not originally designed, a series of clean slate efforts have emerged to shape the so-called future Internet. Recently, visionary voices have advised a shift in the networking problem under research, moving from seamless host-reachability to internetworking of information. We contribute to the healthy debate on future Internet design and discuss ongoing information oriented efforts. Inspired by recent works in Bloom-filterlike data structures, we propose the SPSwitch as a novel switching engine to make wire speed forwarding decisions on flat information labels. We address part of the scalability issues in a data-oriented forwarding layer by trading overdeliveries for state reduction and line speed operations.