Open RAN systems, with their virtualized base stations (vBSs), offer increased flexibility and reduced costs, vendor diversity, and interoperability. However, optimizing the allocation of radio resources in such systems raises new challenges due to the volatile vBSs operation, and the dynamic network conditions and user demands they are called to support. Leveraging the novel O-RAN multi-tier control architecture, we propose a new set of resource allocation threshold policies with the aim of balancing the vBSs' performance and energy consumption in a robust and provably optimal fashion. To that end, we introduce an online learning algorithm that operates under minimal assumptions and without requiring knowledge of the environment, hence being suitable even for "challenging"environments with non-stationary or adversarial demands and conditions. We also develop a meta-learning scheme that utilizes other available algorithmic schemes, e.g., tailored for more "easy"environments, by choosing dynamically the best-performing algorithm; thus enhancing the system's effectiveness. We prove that the proposed solutions achieve sub-linear regret (zero optimality gap), and characterize their dependence on the main system parameters. The performance of the algorithms is evaluated with real-world data from a testbed, in stationary and adversarial conditions, indicating energy savings of up to 64.5% compared with several state-of-the-art benchmarks.

Quantum entanglement is so fundamentally differ- ent from a network packet that several quantum network stacks have been proposed; one of which has even been experimentally demonstrated. Several simulators have also been developed to make up for limited hardware availability, and which facili- tate the design and evaluation of quantum network protocols. However, the lack of shared tooling and community-agreed node architectures has resulted in protocol implementations that are tightly coupled to their simulators. Besides limiting their reusabil- ity between different simulators, it also makes building upon prior results and simulations difficult. To address this problem, we have developed QuIP: a P4-based Quantum Internet Protocol prototyping framework for quantum network protocol design. QuIP is a framework for designing and implementing quantum network protocols in a platform-agnostic fashion. It achieves this by providing the means to flexibly, but rigorously, define device architectures against which quantum network protocols can be implemented in the network programming language P416. QuIP also comes with the necessary tooling to enable their execution in existing quantum network simulators. We demonstrate its use by showcasing V1Quantum, a completely new device architecture, implementing a link- and network-layer protocol, and simulating it in the existing simulator NetSquid.

To avoid exploitation of known vulnerabilities, it is standard security practice to not disclose any model information regarding the antennas used in cellular infrastructure. However, in this work, we show that end-user devices receive enough information to infer, with high accuracy, the model-family of antennas. We demonstrate how low-cost hardware and software setups can fingerprint the cellular infrastructure of whole regions within a few minutes by only listening to cellular broadcast messages. To show the effectiveness and hence risk of such fingerprinting, we collected an extensive dataset of broadcast messages from three different countries. We then trained a machine-learning model to classify broadcast messages based on the model-family they belong to. Our results reveal a worryingly high average accuracy of 97% for model-family classification. We further discuss how inferring the model-family with such high accuracy can lead to a class of identification attacks on cellular infrastructure and we subsequently suggest countermeasures to mitigate the fingerprint effectiveness.

Cellular networks rely on handovers (HOs) as a fundamental element to enable seamless connectivity for mobile users. A comprehensive analysis of HOs can be achieved through data from Mobile Network Operators (MNOs); however, the vast majority of studies employ data from measurement campaigns within confined areas and with limited end-user devices, thereby providing only a partial view of HOs. This paper presents the first countrywide analysis of HO performance, from the perspective of a top-tier MNO in a European country. We collect traffic from approximately 40M users for 4 weeks and study the impact of the radio access technologies (RATs), device types, and manufacturers on HOs across the country. We characterize the geo-temporal dynamics of horizontal (intra-RAT) and vertical (inter-RATs) HOs, at the district level and at millisecond granularity, and leverage open datasets from the country's official census office to associate our findings with the population. We further delve into the frequency, duration, and causes of HO failures, and model them using statistical tools. Our study offers unique insights into mobility management, highlighting the heterogeneity of the network and devices, and their effect on HOs.

Recent years have witnessed video streaming grad- ually evolve into one of the most popular Internet applications. With the rapidly growing personalized demand for real-time video streaming services, maximizing their Quality of Experience (QoE) is a long-standing challenge. The emergence of the server- less computing paradigm has potential to meet this challenge through its fine-grained management and highly parallel comput- ing structures. However, it is still ambiguous how to implement and configure serverless components to optimize video streaming services. In this paper, we propose EAVS, an Edge-assisted Adaptive Video streaming system with Serverless pipelines, which facilitates fine-grained management for multiple concurrent video transmission pipelines. Then, we design a chunk-level optimiza- tion scheme to address video bitrate adaptation. We propose a Deep Reinforcement Learning (DRL) algorithm based on Proximal Policy Optimization (PPO) with a trinal-clip mechanism to make bitrate decisions efficiently for better QoE. Finally, we implement the serverless video streaming system prototype and evaluate the performance of EAVS on various real-world network traces. Our results show that EAVS significantly improves QoE and reduces the video stall rate, achieving over 9.1% QoE improvement and 60.2% latency reduction compared to state- of-the-art solutions.

The conventional definition of fairness in congestion control is flow rate fairness. However, Internet users typically care about flow completion times (FCTs) and flow rate fairness does not lead to equitable FCTs for different users. Therefore, we reconsider what it means for congestion control to be fair and posit a novel stance on fairness: it is fair when no flow unnecessarily prolongs another flow. Based on this stance, we propose an evaluation framework for congestion control fairness that uses slowdown (normalized FCT) as the metric.
We demonstrate the usefulness of our framework through surprising experiment results: in theory, prioritizing short flows should outperform fair queueing, but we show that this is not the case due to slow start dominating short flows. The framework can also analyze traditional flow rate fairness; we do so and verify well-known "fairness" issues, but additionally, we show that flow rate unfairness does not induce slowdown and is thus not a problem per se.

With the emergence of remote education and work in universi- ties due to COVID-19, the ‘zoomification’ of higher education, i.e., the migration of universities to the clouds, reached the public dis- course. Ongoing discussions reason about how this shift will take control over students’ data away from universities, and may ulti- mately harm the privacy of researchers and students alike. How- ever, there has been no comprehensive measurement of universi- ties’ use of public clouds and reliance on Software-as-a-Service of- ferings to assess how far this migration has already progressed. We perform a longitudinal study of the migration to public clouds among universities in the U.S. and Europe, as well as institutions listed in the Times Higher Education (THE) Top100 between Jan- uary 2015 and October 2022. We find that cloud adoption differs between countries, with one cluster (Germany, France, Austria, Switzerland) showing a limited move to clouds, while the other (U.S., U.K., the Netherlands, THE Top100) frequently outsources universities’ core functions and services—starting long before the COVID-19 pandemic. We attribute this clustering to several socio- economic factors in the respective countries, including the general culture of higher education and the administrative paradigm taken towards running universities. We then analyze and interpret our results, finding that the implications reach beyond individuals’ pri- vacy towards questions of academic independence and integrity.

To cater to constantly changing network needs, enabling stateful reconfiguration of Network Functions (NFs) is crucial. Recently, there has been growing interest in offloading NFs to programmable network devices. Unfortunately, it is currently not possible to maintain the full state of NFs during a switch reconfiguration without consuming network resources from and to neighboring switches. In this paper, we present State4, a framework that maintains the state of P4 programs during the reconfiguration of a P4-programmab1e network device, by only using a small amount of local resources on the switch undergoing reconfiguration. State4 acts on both the in-switch control-plane and the data-plane. By utilizing the in-switch local controller, State4 requires no external network resources to achieve reconfiguration while preserving states. As such, State4 enables on-The-fly reconfiguration of stateful NFs, at minimal traffic disruption, where previously traffic had to be re-routed.

In this paper, we study the risk of earthquakes to global Internet infrastructure, namely Internet eXchange Point (IXP) facilities. Leveraging the CAIDA IXPs dataset and publicly available earthquake models and hazard computation tools, we find that more than 50% of the facilities have at least a 2% probability of experiencing potentially damaging levels of shaking, due to earthquakes, within a period of 50 years. Furthermore, we estimate that there is a 10% probability that at least 20 facilities will simultaneously experience potentially damaging levels of shaking within a period of 50 years. Fortunately, our analysis shows that IXPs that host many Autonomous Systems (ASes) tend to be located in less earthquake-prone areas, and that spreading out over multiple facilities significantly reduces the impact of earthquakes to IXPs. Following this observation, we propose a novel metric to help AS operators select peering facilities based on the probability of simultaneous facility failures. We show that applying our metric can significantly increase the resilience of individual ASes, as well as that of the Internet as a whole.

Tactile Internet based Cyber-Physical Systems (TCPS) are highly sensitive to component and communication la- tencies and packet drops. Building a high performing TCPS, thus, necessitates experimenting with different hardware, algorithms, access technologies, and communication protocols. To facilitate such experiments, we have developed TCPSbed, a modular testbed for TCPS. TCPSbed facilitates the integration of different components, both real and simulated, to realize different TCPS applications and evaluate their latency and control performances. TCPSbed’s latency analyzer tool employs a novel method to isolate latencies of individual TCPS components such as the latencies contributed by actuation, sensing, algorithms, and by the network, all in an online fashion. TCPSbed’s method of analyzing stability is also novel. It involves the use of the step response analysis method, a classic control-theoretic method used for analyzing the stability of generic control systems. TCPSbed’s support for edge intelligence modules enables prediction of command and feedback signals at the network’s edge allowing TCPS applications to perform well in adverse network conditions. TCPSbed’s source-code, made available through our GitHub page TactileInternet, allows developers to extend its features and functionalities further. In this paper, we describe the architecture and implementation details of TCPSbed and demonstrate its features through several proof-of-concept experiments.

Networks often need to concurrently process mil- lions of flows with varying Quality-of-Service (QoS) require- ments. Doing so by deploying flow-specific rules at network nodes would require significant memory and overhead.
In this paper, we take a fundamentally different approach, called P4QoS, by embedding QoS requirements in the packets themselves and leveraging P4-programmable network switches to process the traffic based on them. We illustrate and evaluate our approach with latency as our QoS metric, but our concept can be applied to other metrics as well. Our evaluation, both in software (Mininet) and in hardware (Intel Tofino), shows that P4QoS can satisfy application-specific QoS requirements with negligible memory overhead.

The concept of network slicing enables operators to provision multiple virtual networks on top of a single (shared) physical infrastructure. Adding elasticity to slicing, i.e., the ability to on-demand provision/release dedicated network resources, improves resource utilization. However, efficiently allocating and scaling slice resources, while maintaining state consistency, is challenging. Especially with P4-programmab1e network devices that process packets at Tbps speeds, controller-driven scaling of network functions would be too time-consuming, and data-plane scaling is needed. In this paper, we address this need, by developing a custom scaling protocol and framework that can consistently, with negligible delay, scale network slices and functions transparently to the slice end-users. We compare, via emulation and experiments on programmable hardware, our approach to state-of-the-art scaling techniques and demonstrate significant slice resource utilization improvements and scaling duration reductions.

The efficacy of Network Function Virtualization (NFV) depends critically on (1) where the virtual network functions (VNFs) are placed and (2) how the traffic is routed. Unfortunately, these aspects are not easily optimized, especially under time-varying network states with different quality of service (QoS) requirements. Given the importance of NFV, many approaches have been proposed to solve the VNF placement and traffic routing problem. However, those prior approaches mainly assume that the state of the network is static and known, disregarding real-time network variations. To bridge that gap, in this paper, we formulate the VNF placement and traffic routing problem as a Markov Decision Process model to capture the dynamic network state transitions. In order to jointly minimize the delay and cost of NFV providers and maximize the revenue, we devise a customized Deep Reinforcement Learning (DRL) algorithm, called A-DDPG, for VNF placement and traffic routing in a real-time network. A-DDPG uses the attention mechanism to ascertain smooth network behavior within the general framework of network utility maximization (NUM). The simulation results show that A-DDPG outperforms the state-of- the-art in terms of network utility, delay, and cost.

To evaluate the expected availability of a backbone network service, the administrator should consider all possible failure scenarios under the specific service availability model stipulated in the corresponding service-level agreement. Given the increase in natural disasters and malicious attacks with geographically extensive impact, considering only independent single component failures is often insufficient. This paper builds a stochastic model of geographically correlated link failures caused by disasters to estimate the hazards an optical backbone network may be prone to and to understand the complex correlation between possible link failures. We first consider link failures only and later extend our model also to capture node failures. With such a model, one can quickly extract essential information such as the probability of an arbitrary set of network resources to fail simultaneously, the probability of two nodes to be disconnected, the probability of a path to survive a disaster. Furthermore, we introduce standard data structures and a unified terminology on Probabilistic Shared Risk Link Groups (PSRLGs), along with a pre-computation process, which represents the failure probability of a set of resources succinctly. In particular, we generate a quasilinear-sized data structure in polynomial time, which allows the efficient computation of the cumulative failure probability of any set of network elements. Our evaluation is based on carefully pre-processed seismic hazard data matched to real-world optical backbone network topologies.

Network outages have significant economic and societal costs. While network operators have become adept at managing smaller failures, this is not the case for larger, regional failures such as natural disasters. Although it is not possible, and certainly not economic, to prevent all potential disaster damage and impact, we can reduce their impact by adding cost-efficient, geographically redundant, cable connections to the network.
In this paper, we provide algorithms for finding cost-efficient, disaster-aware cable routes based on empirical hazard data. In contrast to previous work, our approach finds disaster-aware routes by considering the impact of a large set of input disasters on the network as a whole, as well as on the individual cable. For this, we propose the Disaster-Aware Network Augmentation Problem of finding a new cable connection that minimizes a function of disaster impact and cable cost. We prove that this problem is NP-hard and give an exact algorithm, as well as a heuristic, for solving it. Our algorithms are applicable to both planar and geographical coordinates. Using actual seismic hazard data, we demonstrate that by applying our algorithms, network operators can cost-efficiently raise the resilience of their network and future cable connections.

Software-defined networking (SDN) is a cornerstone of next-generation networks and has already led to numerous advantages for data-center networks and wide-area networks. However, SDN is not widely adopted in constrained networks, such as Wireless Sensor Networks (WSN), due to excessive control overhead, lossy medium, and in-band control channels. Therefore, a key challenge to enable Software-Defined Wireless Sensor Networks (SD-WSN) is to reduce the number of control messages required to configure the data plane. In this paper, we propose a cluster-based flow control approach in hybrid SDNs. Our approach is hybrid in the sense that it takes advantage of distributed legacy routing and centralized SDN routing. In addition, it makes a trade-off between the granularity of flow control and the communication overhead induced by the SDN controller. The approach partitions a network into clusters with minimum number of border nodes. Instead of handling the individual flows of each node, the SDN controller only manages incoming and outgoing traffic flows of clusters through border nodes, while the flows inside each cluster are controlled by a distributed legacy WSN routing algorithm. Our proof-of-concept implementations in both software and hardware show that our approach is efficient with respect to reducing the number of nodes that must be managed and the number of control messages. In comparison to benchmark solutions with and without clustering, our solution reduces communication costs for flow configuration in an SD-WSN at least by 27% and at most by 88% respectively, without degrading packet delay nor delivery rate.

The quantum technology revolution brings with it the promise of a quantum internet. A new --- quantum --- network stack will be needed to account for the fundamentally new properties of quantum entanglement. The first realisations of quantum networks are imminent and research interest in quantum network protocols has started growing. In the non-quantum world, programmable data planes have broken the pattern of ossification of the protocol stack and enabled a new --- software-defined --- network software architecture. Similarly, a programmable quantum data plane could pave the way for a software-defined quantum network architecture. In this paper, we demonstrate how we use P4-16 to explore abstractions and device architectures for quantum networks.

By targeting communication and power networks, malicious actors can significantly disrupt our society. As networks are more vulnerable after a natural disaster, this moment of weakness may be exploited to disrupt the network even further. However, the potential impact and mitigation of such a follow-up attack has yet to be studied.

In this paper, we propose a framework to analyze the impact of a combination of a natural disaster followed by a targeted single node failure. We apply this framework on empirical disaster data and two network topologies. Our experiments show that even small targeted attacks can significantly augment the already grave network disruption caused by a natural disaster. We further show that this effect can be mitigated by adopting a calculated repair strategy.