360° video streaming is rapidly advancing, with emerging applications in immersive communications envisioned for 6G Networks. However, dynamic user VR navigation (e.g., with head-mounted displays) under variable network conditions remains challenging, leading to severe stalls and quality degradation. In this work, we experimentally evaluate 360° video streaming over state-of-the-art protocols such as HTTP over QUIC, i.e., HTTP/3, focusing on the impact of stream scheduling. We implement and evaluate scheduling policies in two classes: (1) explicit priority, coordinated with QUIC, which dynamically prioritises 360° video tile requests based on temporal and spatial criteria; and (2) implicit priority, confined to the application layer and hidden from QUIC, relying on request order strategies. In realistic experiments with network emulation and 360° user navigation, we observe that explicit priority policies applying temporal criteria to urgent tiles reduce stall time by up to 81 % while maintaining competitive video quality compared to application-only stream scheduling.

The promises of multipath transport are to aggregate bandwidth, improve resource utilization, and enhance reliability. In this paper, we demonstrate that the way multipath coupled congestion control is defined today leads to a suboptimal resource utilisation when network paths are disjoint, i.e., they do not share a bottleneck link. With growing interest in standardising Multipath QUIC (MPQUIC), we have implemented the practical shared bottleneck detection (SBD) algorithm from RFC8382 in MPQUIC (MPQUIC-SBD). Through extensive experiments, we evaluate MPQUIC-SBD in the context of video streaming with various Adaptive Bitrate (ABR) algorithms, addressing both ABR classes of rule-based and learning-based solutions. We demonstrate that MPQUIC-SBD accurately detects shared bottlenecks over 90% of the time, depending on the ABR algorithm, as the size of the video segments increases. In non-shared bottleneck scenarios, when MPQUIC-SBD detects that its QUIC subflows do not share the same network resources, it decouples their congestion windows accordingly, enabling video throughput gains of up to 37% compared to MPQUIC. These gains translate directly into improved video quality metrics, including higher bitrate, better resolution, and reduced buffering, resulting in an enhanced quality of experience for users.

In today's digital landscape, the importance of timely and accurate vulnerability detection has significantly increased. This paper presents a novel approach that leverages transformer-based models and machine learning techniques to automate the identification of software vulnerabilities by analyzing GitHub issues. We introduce a new dataset specifically designed for classifying GitHub issues relevant to vulnerability detection. We then examine various classification techniques to determine their effectiveness. The results demonstrate the potential of this approach for real-world application in early vulnerability detection, which could substantially reduce the window of exploitation for software vulnerabilities. This research makes a key contribution to the field by providing a scalable and computationally efficient framework for automated detection, enabling the prevention of compromised software usage before official notifications. This work has the potential to enhance the security of open-source software ecosystems.

Software-Defined Networking (SDN) has increasingly shifted toward hardware solutions that accelerate packet processing within data planes. However, optimizing the interaction between the data plane and the control plane, commonly referred to as the slow path, remains a significant challenge since the control plane installs rules in the data plane reactively as new flows arrive, which requires time-consuming transitions to user space. In this paper, we demonstrate how to prevent a bottleneck in the slow path by investigating the impact of predicting flows and preemptively installing their rules in the data plane. Using a workload containing different levels of "coflows", our results demonstrate that this predictive approach varies with the flow rate of traffic traces. Notably, a system that could predict 25% of the traffic flows would decrease the average latency by up to approximately 24% and reduce CPU utilization by approximately 12%.

360 degrees video streaming represents a shift in the way media is consumed: Users can immerse themselves, interact and engage with the viewing environment. This paper proposes the design and implementation of an end-to-end 360 degrees video streaming stack on top of emerging Internet standards such as HTTP/3 and MPQUIC. Preliminary experiments with real network traces show that, compared to single-path streaming, multi-path with stream-aware scheduler and non-blocking buffers achieves up to 25% reduction in tile download times, lower rebuffering ratio (down to 0.53), and decreasing tile misses, while providing a higher Structured Similarity Index Measure (SSIM) of around 17.2 dB.

Performance Isolation in Multi-Tenant Cloud Data Centers (MTCDCs) consists of a set of mechanisms to make sure tenants' use of resources does not impact other tenants. In this context, traffic shapers and rate limiters are fundamental to addressing the challenges of performance isolation in MTCDCs, which include predictable performance as minimum bandwidth guarantees, tenants-level fairness, and optimal resource utilization. However, the classical linear programming process to find the optimal rates to apply does not scale in terms of computing time, especially with the huge number of nodes, dominated mainly by virtual machines in an MTCDC environment. Motivated by this observation, this paper introduces gPerfIsol, a novel Graph Neural Network (GNN)-based approach designed to find near-optimal rates allocation in near-real-time to ensure performance isolation in MTCDC. gPerfIsol's key innovation leverages Heterogeneous GNNs to capture MTCDC-specific topological information and demand traffic matrix. Evaluations based on datasets generated through simulation demonstrate the effectiveness of gPerfIsol's binary classification model with a precision score of 0.964 and a recall score of 0.973. Ultimately, gPerfIsol offers a promising solution for near-optimal rate limit allocation for traffic shapers in multi-tenant environments, enhancing performance isolation.

The rise of Microservices architectures has introduced a surge in the demand for containerized software provisioning and orchestration. Kubernetes is a leading container orchestration platform widely adopted for its efficient management of containerized applications within cloud datacenters. However, the proliferation of containerized applications, particularly in resource-constrained environments like Internet of Things (IoT) edge devices, has prompted numerous software vendors to release lightweight Kubernetes-derived platforms, tailored for streamlined deployment in such infrastructures. Despite this, selecting the most suitable distribution for edge-based IoT applications remains a challenge due to a lack of comprehensive studies in this domain. This paper presents a comprehensive model for evaluating the performance of lightweight Container Orchestration Distributions (CODs), incorporating resource usage, control-plane performance, and data-plane performance metrics. Also, we conduct a comparative empirical analysis of four prominent CODs (i.e., k0s, k3s, Microk8s, and Microshift) by focusing on their suitability for edge environments. Our findings reveal that k3s is the overall best-performant COD.

Every Web session involves a DNS resolution. While, in the last decade, we witnessed a promising trend towards an encrypted Web in general, DNS encryption has only recently gained traction with the standardisation of DNS over TLS (DoT) and DNS over HTTPS (DoH). Meanwhile, the rapid rise of QUIC deployment has now opened up an exciting opportunity to utilise the same protocol to not only encrypt Web communications, but also DNS. In this paper, we evaluate this benefit of using QUIC to coalesce name resolution via DNS over QUIC (DoQ), and Web content delivery via HTTP/3 (H3) with 0-RTT. We compare this scenario using several possible combinations where H3 is used in conjunction with DoH and DoQ, as well as the unencrypted DNS over UDP (DoUDP). We observe, that when using H3 1-RTT, page load times with DoH can get inflated by >30% over fixed-line and by >50% over mobile when compared to unencrypted DNS with DoUDP. However, this cost of encryption can be drastically reduced when encrypted connections are coalesced (DoQ + H3 0-RTT), thereby reducing the page load times by 1/3 over fixed-line and 1/2 over mobile, overall making connection coalescing with QUIC the best option for encrypted communication on the Internet.

This paper explores opportunities to utilize Large Language Models (LLMs) to make network configuration human-friendly, simplifying the configuration of network devices & development of routing algorithms and minimizing errors. We design a set of benchmarks (NetConfEval) to examine the effectiveness of different models in facilitating and automating network configuration. More specifically, we focus on the scenarios where LLMs translate high-level policies, requirements, and descriptions (i.e., specified in natural language) into low-level network configurations & Python code. NetConfEval considers four tasks that could potentially facilitate network configuration, such as (i) generating high-level requirements into a formal specification format, (ii) generating API/function calls from high-level requirements, (iii) developing routing algorithms based on high-level descriptions, and (iv) generating low-level configuration for existing and new protocols based on input documentation. Learning from the results of our study, we propose a set of principles to design LLM-based systems to configure networks. Finally, we present two GPT-4-based prototypes to (i) automatically configure P4-enabled devices from a set of high-level requirements and (ii) integrate LLMs into existing network synthesizers.

The Tactile Internet paradigm is set to revolutionize human society by enabling skill-set delivery and haptic communication over ultra-reliable, low-latency networks. The emerging sixth-generation (6G) mobile communication systems are envisioned to underpin this Tactile Internet ecosystem at the network edge by providing ubiquitous global connectivity. However, apart from a multitude of opportunities of the Tactile Internet, security and privacy challenges emerge at the forefront. We believe that the recently standardized QUIC protocol, characterized by end-to-end encryption and reduced round-trip delay would serve as the backbone of Tactile Internet. In this article, we envision a futuristic scenario where a QUIC-enabled network uses the underlying 6G communication infrastructure to achieve the requirements for Tactile Internet. Interestingly this requires a deeper investigation of a wide range of security and privacy challenges in QUIC, that need to be mitigated for its adoption in Tactile Internet. Henceforth, this article reviews the existing security and privacy attacks in QUIC and their implication on users. Followed by that, we discuss state-of-the-art attack mitigation strategies and investigate some of their drawbacks with possible directions for future work

Modern information technology (IT) infrastructures are becoming more complex to meet the diverse demands of emerging technology paradigms such as 5G/6G networks, edge, and internet of things (IoT). The intricacy of these infrastructures grows further when hosting containerized workloads as microservices, resulting in the challenge to detect and troubleshoot performance issues, incidents or even outages of critical use cases like industrial automation processes. Thus, fine-grained measurements and associated visualization are essential for operation observability of these IT infrastructures. However, most existing observability tools operate independently without systematically covering the entire data workflow. This paper presents an integrated design for multi-stage observability workflows, denoted as DistributEd obServability frameworK (DESK). The proposed framework aims to improve observability workflows for measurement, collection, fusion, storage, visualization, and notification. As a proof of concept, we deployed the framework in a Kubernetes-based testbed to demonstrate the successful integration of various components and usability of collected observability data. We also conducted a comprehensive study to determine the caused overhead by DESK agents at the reasonably powerful edge node hardware, which shows on average a CPU and memory overhead of around 2.5% of total available hardware resource.

The promises of multipath transport is to aggregate bandwidth and improve resource utilisation and reliability. We demonstrate in this paper that the way multipath coupled congestion control is defined today RFC6359 leads to a sub-optimal resource utilisation when network paths are mainly disjoint, i.e., they do not share a bottleneck. With growing interest to standardise Multipath QUIC (MPQUIC), we implement the practical shared bottleneck detection (SBD) algorithm from RFC8382 in MPQUIC, namely MPQUIC-SBD. We evaluate MPQUIC-SBD through extensive emulation experiments in the context of video streaming. We show that MPQUIC-SBD is able to correctly detect shared bottlenecks over 90% of the time as the video segments' size increase depending on the Adaptive Bitrate (ABR) algorithm. In non-shared bottleneck scenarios, MPQUIC-SBD results in video throughput gains of more than 13% compared to MPQUIC, which directly translates into better video quality metrics.

Network latency is a critical factor for the perceived quality of experience for many applications. With an increasing focus on interactive and real-time applications, which require reliable and low latency, the ability to continuously and efficiently monitor latency is becoming more important than ever. Always-on passive monitoring of latency can provide continuous latency metrics without injecting any traffic into the network. However, software-based monitoring tools often struggle to keep up with traffic as packet rates increase, especially on contemporary multi-Gbps interfaces. We investigate the feasibility of using eBPF to enable efficient passive network latency monitoring by implementing an evolved Passive Ping (ePPing). Our evaluation shows that ePPing delivers accurate RTT measurements and can handle over 1 Mpps, or correspondingly over 10 Gbps, on a single core, greatly improving on state-of-the-art software based solutions, such as PPing.

The lack of consideration for application delay requirements in standard loss-based congestion control algorithms (CCAs) has motivated the proposal of several alternative CCAs. Among these new CCAs, Copa is one of the promising few CCAs that have attracted attention from both academia and industry. The delay performance of Copa is governed by a mostly static latency-throughput tradeoff parameter, $\delta$ . However, a static $\delta$ parameter makes it difficult for Copa to achieve consistent delay and throughput over a range of bottleneck bandwidths. In particular, the coexistence of 4G and 5G networks and the wide range of bandwidths experienced in NG-RANs can result in inconsistent CCA performance. To this end, we propose Copa-D, a modification to Copa that dynamically tunes $\delta$ to achieve a consistent delay performance. We evaluate the modification over fixed, 4G, and 5G emulated bottlenecks. Our results show that Copa-D achieves consistent delay with minimal impact on throughput in fixed capacity bottlenecks. Copa-D also allows a more intuitive way of specifying the latency-throughput tradeoff and achieves more accurate and predictable delay in variable cellular bottlenecks.

Traffic optimization and smart buffering are fundamental to achieve both great application performance and resource efficiency in data centers with heterogeneous workloads, including incast and elephant traffics. However, general performance models providing insights on how various factors affect traffic performance metrics needed by these management functions are missing. For the special case of incast, the existing models are analytical ones, either tightly coupled with a particular protocol version or specific to certain empirical data. Motivated by this observation, this paper proposes an SDN-enabled machine-learning-based performance modeling approach in data center networks that leverages random forest predictions. Evaluations based on datasets constructed through intensive NS-3 simulations show that we can achieve accurate predictions of incast and elephant performance metrics based on various features. With this performance modeling capability, smart buffering schemes or traffic optimization algorithms could anticipate and efficiently optimize system parameters adjustment to achieve optimal performance continuously in data centers.

Edge computing is proposed as a technical enabler for meeting emerging network technologies (such as 5G and Industrial Internet of Things), stringent application requirements and key performance indicators (KPIs). It aims to alleviate the problems associated with centralized cloud computing systems by placing computational resources to the network’s edge, closer to the users. However, the complexity of distributed edge infrastructures grows when hosting containerized workloads as microservices, resulting in hard to detect and troubleshoot outages on critical use cases such as industrial automation processes. Observability aims to support operators in managing and operating complex distributed infrastructures and microservices architectures by instrumenting end-to-end runtime performance. To the best of our knowledge, no survey article has been recently proposed for distributed edge and containerized microservices observability. Thus, this article surveys and classifies state-of-the-art solutions from various communities. Besides surveying state-of-the-art, this article also discusses the observability concept, requirements, and design considerations. Finally, we discuss open research issues as well as future research directions that will inspire additional research in this area.

The COVID-19 pandemic disrupted the usual ways the networking research community operates. This article reviews experiences organising and participating in virtual conferences during the COVID-19 pandemic between 2020-2021. Thanks to the broader scope of the Dagstuhl seminar on 'Climate Friendly Internet Research' held in July 2021, here we focus the discussion on state-of-the-art in technologies and practices applied in online events such as conferences, teaching, and other meetings and identify approaches that are successful as well as others that need improvement. We also present a set of best practices and recommendations for the community.

Multipath transport protocols enable the concurrent use of different network paths, benefiting a fast and reliable data transmission. The scheduler of a multipath transport protocol determines how to distribute data packets over different paths. Existing multipath schedulers either conform to predefined policies or to online trained policies. The adoption of millimeter wave (mmWave) paths in 5th Generation (5G) networks and Wireless Local Area Networks (WLANs) introduces timevarying network conditions, under which the existing schedulers struggle to achieve fast and accurate adaptation. In this paper, we propose FALCON, a learning-based multipath scheduler that can adapt fast and accurately to time-varying network conditions. FALCON builds on the idea of meta-learning where offline learning is used to create a set of meta-models that represent coarse-grained network conditions, and online learning is used to bootstrap a specific model for the current fine-grained network conditions towards deriving the scheduling policy to deal with such conditions. Using trace-driven emulation experiments, we demonstrate FALCON outperforms the best state-of-the-art scheduler by up to 19.3% and 23.6% in static and mobile networks, respectively. Furthermore, we show FALCON is quite flexible to work with different types of applications such as bulk transfer and web services. Moreover, we observe FALCON has a much faster adaptation time compared to all the other learningbased schedulers, reaching almost an 8-fold speedup compared to the best of them. Finally, we have validated the emulation results in real-world settings illustrating that FALCON adapts well to the dynamicity of real networks, consistently outperforming all other schedulers.

During the COVID-19 pandemic, many smaller conferences have moved entirely online and larger ones are being held as hybrid events. Even beyond the pandemic, hybrid events reduce the carbon footprint of conference travel and makes events more accessible to parts of the research community that have difficulty traveling long distances, while preserving most advantages of in-person gatherings. While we have developed a solid understanding of how to design virtual events over the last two years, we are still learning how to properly run hybrid events. We present guidelines and considerations-spanning technology, organization and social factors-for organizing successful hybrid conferences. This paper summarizes and extends the discussions held at the Dagstuhl seminar on "Climate Friendly Internet Research" held in July 2021.