The 5G Radio Access Network has shifted towards virtualization and disaggregation. This change aims to reduce costs and foster innovation by promoting vendor interoperability and by expanding the ecosystem. In this environment, smaller RAN vendors and open-source projects have emerged, focusing on low-cost, modular stacks. However, challenges such as achieving state-of-the-art performance and accessing data and control knobs hinder their widespread adoption. To address these issues, we propose a middlebox architecture, called RANBooster, that enhances the RAN capabilities without modifying existing network functions, by leveraging the open fronthaul interface. To demonstrate the benefits of the RANBooster framework, we build four reference applications (distributed antenna system, distributed MIMO, RU sharing, realtime physical resource block monitoring), and evaluate them on an enterprise-scale, commercial-grade 5G testbed.

The Open RAN architecture, with disaggregated and virtualized RAN functions communicating over standardized interfaces, promises a diversified and multi-vendor RAN ecosystem. However, these same features contribute to increased operational complexity, making it highly challenging to troubleshoot RAN related performance issues and failures. Tackling this challenge requires a dependable, explainable anomaly detection method that Open RAN is currently lacking. To address this problem, we introduce SpotLight, a tailored system archtecture with a distributed deep generative modeling based method running across the edge and cloud. SpotLight takes in a diverse, fine grained stream of metrics from the RAN and the platform, to continually detect and localize anomalies. It introduces a novel multi-stage generative model to detect potential anomalies at the edge using a light-weight algorithm, followed by anomaly confirmation and an explain-ability phase at the cloud, that helps identify the minimal set of KPIs that caused the anomaly. We evaluate SpotLight using the metrics collected from an enterprise-scale 5G Open RAN deployment in an indoor office building. Our results show that compared to a range of baseline methods, SpotLight yields significant gains in accuracy (13% higher F1 score), explain-ability (2.3 -- 4X reduction in the number of reported KPIs) and efficiency (4 -- 7X bandwidth reduction).

The Open RAN architecture, featuring disaggregated and virtualized RAN functions communicating over standardized interfaces, promises a diverse, multi-vendor ecosystem. However, these features also increase operational complexity, complicating the troubleshooting of RAN performance issues and failures. Addressing this challenge requires a reliable, explainable anomaly detection method, which Open RAN currently lacks. To address this problem, we have developed SpotLight, a tailored distributed deep learning method running across the edge and cloud. SpotLight continuously detects and localizes anomalies by analyzing a diverse, fine-grained stream of metrics from the RAN and platform. It employs a novel multi-stage generative model to identify potential anomalies at the edge using a lightweight algorithm, followed by anomaly confirmation and an explainability phase in the cloud, which pinpoints the minimal set of KPIs responsible for the anomaly. In this demo, using a carrier-grade indoor Open RAN testbed with configurable anomaly event generation and replay, we highlight (1) the difficulty of troubleshooting problems in Open RAN and (2) accurate, efficient, and explainable online anomaly detection with SpotLight and corresponding visualization in comparison with prior art.

Open RAN is an emerging paradigm in mobile networks where the Radio Access Network (RAN) functions are disaggregated and virtualized on commodity servers. Despite the importance of Open RAN research, existing platforms often lack the fidelity and stability required to address a wide range of research problems. In response to this limitation, we have developed an enterprise-scale Open RAN testbed aimed at conducting state-of-the-art research in key areas that have received limited attention due to the lack of suitable platforms. In this poster, we provide an overview of the testbed we have created and examples of the research it has enabled, with the hope of catalyzing future open RAN research and innovation.

Open RAN, a modular and disaggregated design paradigm for 5G radio access networks (RAN), promises programmability through the RAN Intelligent Controller (RIC). However, due to latency and safety challenges, the telemetry and control provided by the RIC is mainly limited to higher layers and higher time scales (> 10ms), while also relying on predefined service models which are hard to change. We address these issues by proposing Janus, a fully programmable monitoring and control system, specifically designed with the RAN idiosyncrasies in mind, focused on flexibility, efficiency and safety. Janus builds on eBPF to allow third-parties to load arbitrary codelets inline in the RAN functions in a provably safe manner. We extend eBPF with a novel bytecode patching algorithm that enforces codelet runtime thresholds, and a safe way to collect user-defined telemetry. We demonstrate Janus' flexibility and efficiency by building 3 different classes of applications (18 applications in total) and deploying them on a 100MHz 4×4 MIMO 5G cell without affecting the RAN performance.

A key transformation of the Radio Access Network (RAN) in 5G is the migration to an Open RAN architecture, that sees the RAN functions virtualized and disaggregated. Open RAN, aims at accelerating innovation through the introduction of a programmable RAN Intelligent Controller (RIC). However, due to latency and safety challenges, the telemetry and control provided by the RIC is mainly limited to higher layers and time scales (>10ms), while also relying on predefined service models which are hard to change. In this work, we demonstrate Minerva, a programmable monitoring and control platform, specifically designed with the RAN idiosyncrasies in mind. Minerva introduces two novel components called Janus and Decima, for the deployment of safe RAN control and telemetry applications by trusted third-parties. Janus enables the deployment of codelets in the RAN functions using userspace eBPF for fast inline control and filtering of data. Decima, enables the deployment of sandboxed real-time control applications, by leveraging data collected from Janus and the OS. We demonstrate the benefits of Minerva through two applications related to interference detection and inter-cell interference mitigation, by leveraging a commercial-grade 5G testbed deployment.

Virtualized radio access networks (vRANs), which allow running RAN processing on commodity servers instead of proprietary hardware, are gaining adoption in cellular networks. Two properties of the vRAN's "Distributed Unit (DU)" that implements the lower RAN layers---its real-time deadlines and its black-box nature---make it challenging to provide resilience features such as upgrades and failover without long service disruptions. These properties preclude the use of existing resilience techniques like virtual machine migration or state replication that are used for typical workloads. This paper presents Atlas, the first system that provides resilience for the DU. The central insight in Atlas is to repurpose existing cellular mechanisms for wireless resilience, namely handovers and cell reselection, to provide software resilience for the DU. For planned resilience events like upgrades, we design a novel technique that simultaneously serves cells from both the old and new DUs via the same radio, and uses handovers between these cells to migrate user devices. For unplanned failures, we identify deficiencies in existing RAN protocols that disrupt cell reselection after DU failure, and show how we can eliminate these disruptions using a middlebox between the DU and higher layers. Our evaluation with a state-of-the-art 5G vRAN testbed shows that Atlas achieves minimal disruption to cellular connectivity during resilience events, while incurring low overhead.

RAN energy consumption is a major OPEX source for mobile telecom operators, and 5G is expected to increase these costs by several folds. Moreover, paradigm-shifting aspects of the 5G RAN architecture like RAN disaggregation, virtualization and cloudification introduce new traffic-dependent resource management decisions that make the problem of energy-efficient 5G RAN orchestration harder. To address such a challenge, we present a first comprehensive virtualized RAN (vRAN) system model aligned with 5G RAN specifications, which embeds realistic and dynamic models for computational load and energy consumption costs. We then formulate the vRAN energy consumption optimization as an integer quadratic programming problem, whose NP-hard nature leads us to develop GreenRAN, a novel, computationally efficient and distributed solution that leverages Lagrangian decomposition and simulated annealing. Evaluations with real-world mobile traffic data for a large metropolitan area are another novel aspect of this work, and show that our approach yields energy efficiency gains up to 25% and 42%, over state-of-the-art and baseline traditional RAN approaches, respectively.

Virtualized Radio Access Network (vRAN) offers a cost-efficient solution for running the 5G RAN as a virtualized network function (VNF) on commodity hardware. The vRAN is more efficient than traditional RANs, as it multiplexes several base station workloads on the same compute hardware. Our measurements show that, whilst this multiplexing provides efficiency gains, more than 50% of the CPU cycles in typical vRAN settings still remain unused. A way to further improve CPU utilization is to collocate the vRAN with general-purpose workloads. However, to maintain performance, vRAN tasks have sub-millisecond latency requirements that have to be met 99.999% of times. We show that this is difficult to achieve with existing systems. We propose Concordia, a userspace deadline scheduling framework for the vRAN on Linux. Concordia builds prediction models using quantile decision trees to predict the worst case execution times of vRAN signal processing tasks. The Concordia scheduler is fast (runs every 20 us) and the prediction models are accurate, enabling the system to reserve a minimum number of cores required for vRAN tasks, leaving the rest for general-purpose workloads. We evaluate Concordia on a commercial-grade reference vRAN platform. We show that it meets the 99.999% reliability requirements and reclaims more than 70% of idle CPU cycles without affecting the RAN performance.

The recent trend towards cloudifying mobile networks brings more flexibility and shortens deployment times. However, it results in an architecture spanning several independent layers from the bare metal to the service level thus complicating troubleshooting and service assurance. In this work, we experimentally explore whether we can accurately and efficiently identify bottlenecks across the different locations of the network and layers of the cloudified architecture. Our findings confirm the complexity of this task and lead us to promising solutions through the use of Machine Learning.

We consider the problem of monitoring in the context of emerging and future mobile networks which are shaping up to feature diverse set of services composed of customized chains of virtual network functions (VNFs) realized over (edge) cloud environments. In such a setting, not only is monitoring a critical component for service quality assurance, but it also needs to be efficient, adaptable and flexible. Informed by the experience analyzing state-of-the-art management and orchestration (MANO) platforms and monitoring solutions for softwarized mobile networks, we present our monitoring system design termed PliMon that aims to meet the above requirements by exploiting diverse temporal variability characteristics across different metrics (measurement features) and VNFs, and by grouping such metrics into tiers based on their relative significance. Using an experimental testbed, we verify the hypothesis that different measurement features and VNFs exhibit diversity in their variability and crucially show substantial reduction in monitoring overhead compared to representative monitoring solution from the literature. Additionally, we integrate PliMon with OSM, a well known open source MANO platform, and demonstrate the salient aspects of our approach using the integrated PliMon-OSM system.

This study is a first attempt to experimentally explore the range of performance bottlenecks that 5G mobile networks can experience. To this end, we leverage a wide range of measurements obtained with a prototype testbed that captures the key aspects of a cloudified mobile network. We investigate the relevance of the metrics and a number of approaches to accurately and efficiently identify bottlenecks across the different locations of the network and layers of the system architecture. Our findings validate the complexity of this task in the multi-layered architecture and highlight the need for novel monitoring approaches that intelligently fuse metrics across network layers and functions. In particular, we find that distributed analytics performs reasonably well both in terms of bottleneck identification accuracy and incurred computational and communication overhead.

We consider indoor mobile access, a vital use case for current and future mobile networks. For this key use case, we outline a vision that combines a neutral-host-based shared small-cell infrastructure with a common pool of spectrum for dynamic sharing as a way forward to proliferate indoor small-cell deployments and open up the mobile operator ecosystem. Toward this vision, we focus on the challenges pertaining to managing access to shared spectrum [e.g., 3.5-GHz U.S. Citizen Broadband Radio Service (CBRS) spectrum]. We propose Iris, a practical shared spectrum access architecture for indoor neutral-host small-cells. At the core of Iris is a deep reinforcement learning-based dynamic pricing mechanism that efficiently mediates access to shared spectrum for diverse operators in a way that provides incentives for operators and the neutral-host alike. We then present the Iris system architecture that embeds this dynamic pricing mechanism alongside cloud-RAN and RAN slicing design principles in a practical neutral-host design tailored for the indoor small-cell environment. Using a prototype implementation of the Iris system, we present the extensive experimental evaluation results that not only offer insight into the Iris dynamic pricing process and its superiority over alternative approaches but also demonstrate its deployment feasibility.

The availability of multiple collocated wireless networks using heterogeneous technologies and the multiaccess support of contemporary mobile devices have allowed wireless connectivity optimization, enabled through vertical handover (VHO) operations. However, this comes at high energy consumption on the mobile device due to the inherently expensive nature of some of the involved operations. This work proposes exploiting short‐range cooperation among collocated mobile devices to improve the energy efficiency of vertical handover operations. The proactive exchange of handover‐related information through low‐energy short‐range communication technologies, like Bluetooth, can help in eliminating expensive signaling steps when the need for a VHO arises. A model is developed for capturing the mean energy expenditure of such an optimized VHO scheme in terms of relevant factors by means of closed‐form expressions. The descriptive power of the model is demonstrated by investigating various typical usage scenarios and is validated through simulations. It is shown that the proposed scheme has superior performance in several realistic usage scenarios considering important relevant factors, including network availability, the local density of mobile devices, and the range of the cooperation technology. Finally, the paper explores cost/benefit trade‐offs associated with the short‐range cooperation protocol. It is demonstrated that the protocol may be parametrized so that the trade‐off becomes nearly optimized and the cost is maintained affordable for a wide range of operational scenarios.

Machine Type Communications (MTC) collectively refers to the exchange of data among devices that operate without human intervention. A significant number of such devices are currently served by cellular networks, and that number is expected to grow in the near future. However, cellular networks, including current fourth generation LTE networks, face several challenges when it comes to handling MTC traffic as they were primarily designed for Human Type Communications (HTC) which have very different traffic patterns. In this paper we focus on periodic MTC devices, such as sensors and meters, which cause significant signaling load and increased collisions over standard LTE networks. We propose ASPIS, a holistic mechanism designed to overcome these problems. ASPIS reduces the signaling load by partly preserving a device's connection to the network in conjunction with a new Random Access process and efficient support for short message transmissions. In addition, it uses a proactive preamble split scheme to alleviate collisions. ASPIS is easy to implement without requiring hardware changes while at the same time maintains security and can be incrementally deployed alongside legacy devices/infrastructure. We showcase the practicality of ASPIS by implementing it on the OpenAirInterface platform. Further, we demonstrate its effectiveness through extensive evaluations via a combination of small-scale experimental evaluation and large-scale, realistic simulations.

Emerging 5G mobile networks are envisioned to become multi-service environments, enabling the dynamic deployment of services with a diverse set of performance requirements, accommodating the needs of mobile network operators, verticals and over-the-top (OTT) service providers. Virtualizing the mobile network in a flexible way is of paramount importance for a cost-effective realization of this vision. While virtualization has been extensively studied in the case of the mobile core, virtualizing the radio access network (RAN) is still at its infancy. In this paper, we present Orion, a novel RAN slicing system that enables the dynamic on-the-fly virtualization of base stations, the flexible customization of slices to meet their respective service needs and which can be used in an end-to-end network slicing setting. Orion guarantees the functional and performance isolation of slices, while allowing for the efficient use of RAN resources among them. We present a concrete prototype implementation of Orion for LTE, with experimental results, considering alternative RAN slicing approaches, indicating its efficiency and highlighting its isolation capabilities. We also present an extension to Orion for accommodating the needs of OTT providers.

Emerging 5G mobile networks are envisioned to support the dynamic deployment of services with diverse performance requirements, accommodating the needs of mobile network operators and verticals. Virtualizing the mobile network components in a flexible and cost-effective way is therefore of paramount importance. In this work, we highlight the capabilities of Orion, a novel RAN slicing architecture that enables the dynamic virtualization of base stations and flexible customization of slices to meet their respective service needs. Our demonstration of Orion's capabilities is based on a prototype implementation employing a modified version of the OpenAirInterface software LTE platform. Using this prototype, we demonstrate the functional and performance isolation, and the efficient sharing of radio hardware and spectrum that can be achieved among Orion RAN slices. Moreover, we show how Orion can be used in an end-to-end network slicing setting and demonstrate the effects of the slices' configuration and placement of virtual functions in the overall quality of the deployed services.

Although SDN is considered as one of the key technologies behind the impending 5G evolution of mobile networks, the opportunity of reaping its benefits is largely still untapped on the Radio Access Network (RAN) side due to the lack of a software-defined RAN (SD-RAN) platform. In this work we demonstrate the capabilities of FlexRAN, an open-source SD-RAN platform developed to fill this void. FlexRAN separates the RAN control and data planes with a custom-tailored southbound API. Besides it features a hierarchical control plane architecture that enables programmability, flexible and dynamic control function placement (allowing different degrees of coordination within and among base stations) and real-time control. Virtualized control functions and control delegation are two key features in FlexRAN that makes these capabilities possible. This demo illustrates the capabilities and the performance of FlexRAN based on a prototype implementation, while its applicability is highlighted through a Mobile Edge Computing use case, where it acts as an enabler of a video bitrate adaptation application based on the radio conditions at the network edge.

Although the radio access network (RAN) part of mobile networks offers a significant opportunity for benefiting from the use of SDN ideas, this opportunity is largely untapped due to the lack of a software-defined RAN (SD-RAN) platform. We fill this void with FlexRAN, a flexible and programmable SD-RAN platform that separates the RAN control and data planes through a new, custom-tailored southbound API. Aided by virtualized control functions and control delegation features, FlexRAN provides a flexible control plane designed with support for real-time RAN control applications, flexibility to realize various degrees of coordination among RAN infrastructure entities, and programmability to adapt control over time and easier evolution to the future following SDN/NFV principles. We implement FlexRAN as an extension to a modified version of the OpenAirInterface LTE platform, with evaluation results indicating the feasibility of using FlexRAN under the stringent time constraints posed by the RAN. To demonstrate the effectiveness of FlexRAN as an SD-RAN platform and highlight its applicability for a diverse set of use cases, we present three network services deployed over FlexRAN focusing on interference management, mobile edge computing and RAN sharing.

Usage of WiFi is becoming increasingly popular in public wireless LAN (WLAN) settings like malls, airports and train stations. Similarly to other prominent examples of WiFi usage like enterprise and home settings, public WLANs could also benefit from an SDN-based coordinated management framework that deals with issues like interference and mobility management. However, unlike these settings, public WLANs present a few differences in their characteristics, such as the need to offer location-aware services and dynamic categorization of users, and the consequent need to provide sophisticated association strategies. Motivated by this we propose VALI, an SDN management framework tailored to meet the needs of public WLAN settings. We give an overview of VALI and present initial results obtained using our prototype implementation deployed over a testbed that resembles a realistic public WLAN environment. Our results demonstrate that VALI is a promising solution that could be used to effectively manage public WLAN settings and enable location-aware WiFi access.

The availability of multiple collocated wireless networks using heterogeneous technologies and the multi-access support of contemporary mobile devices have allowed wireless connectivity optimization, enabled through vertical handover (VHO) operations. However, this comes at a high energy consumption on the mobile device, due to the inherently expensive nature of some of the involved operations. This work proposes exploiting short-range cooperation among collocated mobile devices to improve the energy efficiency of vertical handover operations. The proactive exchange of handover-related information through low-energy short-range communication technologies, like Bluetooth, can help in eliminating expensive signaling steps when the need for a VHO arises. A model is developed for capturing the mean energy expenditure of such an optimized VHO scheme in terms of relevant factors by means of closed-form expressions. This model is validated through simulations and results demonstrate that the proposed scheme has superior performance in several realistic usage scenarios considering important relevant factors, including network availability, the local density of mobile devices and the range of the cooperation technology.

Mixing time is a global property of a network that indicates how fast a random walk gains independence from its starting point. Mixing time is an essential parameter for many distributed algorithms, but especially those based on gossip. We design, implement, and evaluate a distributed protocol to measure mixing time. The protocol extends an existing algorithm that models the diffusion of information seen from each node in the network as the impulse response of a particular dynamic system. In its original formulation, the algorithm was susceptible to topology changes (or “churn”) and was evaluated only in simulation. Here we present a concrete implementation of an enhanced version of the algorithm that exploits multiple parallel runs to obtain a robust measurement, and evaluate it using a network testbed (Emulab) in combination with a peer-to-peer system (FreePastry) to assess both its performance and its ability to deal with network churn.