IMDEA Networks Institute Digital Repository
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OctoMap: Supporting Service Function Chaining via Supervised Learning and Online Contextual Bandit
No full textNetwork Function Virtualization (NFV) replaces physical middleboxes with elastic Virtual Network Functions (VNFs). Those VNFs need to be instantiated, and their resources dynamically scaled to meet application and traffic fluctuation requirements. Despite recent extensive research, deciding how to map virtual resources optimally to the underlying infrastructure remains practically a challenge. Existing approaches mostly assign fixed resources to each VNF instance, and transfer virtual flows using a single physical path, without prior knowledge of traffic patterns and available bandwidth. Such resource binding strategies lead to suboptimal physical link utilization. We advance the state of the art in this regard by presenting OctoMap, a system designed to support with learning theory any chain embedding algorithm. OctoMap utilizes a Convolution Neural Network for traffic prediction and provisioning, and a contextual multi-armed bandit algorithm to solve the online VNF chain embedding problem. We show the performance benefits of OctoMap with a trace-driven simulation campaign using publicly available datasets. In particular, we show how OctoMap reduces the costs of provisioning network services under node and link constraints, comparing different predictors and different multi-armed bandit policies.TRUEpu
Practical Null Steering in Millimeter Wave Networks
Get PDFMillimeter wave (mmWave) is playing a central role in pushing the performance and scalability of wireless networks by offering huge bandwidth and extremely high data rates. Millimeter wave radios use phased array technology to modify the antenna beam pattern and focus their power towards the transmitter or receiver. In this paper, we explore the practicality of modifying the beam pattern to suppress interference by creating nulls, i.e. directions in the beam pattern where almost no power is received. Creating nulls in
practice, however, is challenging due to the fact that practical mmWave phased arrays offer very limited control in setting the parameters of the beam pattern and suffer from hardware imperfections which prevent us from nulling interference.
We introduce Nulli-Fi, the first practical mmWave null steering system. Nulli-Fi combines a novel theoretically optimal algorithm that accounts for limitations in practical
phased arrays with a discrete optimization framework that
overcomes hardware imperfections. Nulli-Fi also introduces
a fast null steering protocol to quickly null new unforeseen
interferers. We implement and extensively evaluate Nulli-Fi
using commercial off-the-shelf 60 GHz mmWave radios with
16-element phased arrays transmitting IEEE 802.11ad packets [33] . Our results show that Nulli-Fi can create nulls that
reduce interference by up to 18 dB even when the phased array offers only 4 bits of control. In a network with 10 links (20 nodes), Nulli-Fi’s ability to null interference enables 2.68X higher total network throughput compared to recent past work.TRUEpu
Storage Capacity of Opportunistic Information Dissemination Systems
Get PDFFloating Content (FC) is a paradigm for localized infrastructure-less content dissemination, that aims at sharing information among nodes within a restricted geographical area by relying only on opportunistic content exchanges.
FC provides the basis for the probabilistic spatial storage of shared information in a completely decentralized fashion, usually without support from dedicated infrastructure.
One of the key open issues in FC is the characterization of its performance limits as functions of the system parameters, accounting for its reliance on volatile wireless exchanges and on limited user resources. This paper takes a first step towards tackling this issue, by elaborating a model for the storage capacity of FC, i.e., for the maximum amount of information that can be stored through the FC paradigm.
The storage capacity of FC, and of similar probabilistic content dissemination systems, is evaluated with a powerful information theoretical approach, based on a mean field model of opportunistic information exchange. In addition, an extremely simple explicit approximate expression for storage capacity is derived.
The numerical results generated by our analytical models are compared to the predictions of realistic simulations under different setups, proving the accuracy of our analytical approaches, and characterizing the properties of the FC storage capacity.pu
Maria Serna’s Contributions to Adversarial Queuing Theory
Get PDFAdversarial Queuing Theory (AQT) is one of the areas to which Maria Serna has deeply contributed in her scientific career. Most of her research in this area took place during the lustrum 2001-2005, while advising the PhD of Maria J. Blesa, and also in conjunction with other researchers. AQT is an adversarial model for the study of packet-switching communication networks under worst case conditions. In this model a fundamental concept is that of stability of a combination of network and packet scheduling protocol in front of an adversary that controls the arrival of packets. There is stability if the adversary cannot cause the number of packets in the network to grow unbounded.Maria’s contributions, summarized in this document, include results in the stability of network and protocols, and the definition and study of AQT models with important new characteristics,like priorities, failures, or networks with different bandwidths and packet lengths. These results keep impacting the new research currently done in the AQT mode.pu
Network Intelligence in 6G: challenges and opportunities
Get PDFThe success of the upcoming 6G systems will largely depend on the quality of the Network Intelligence (NI) that will fully automate network management. Artificial Intelligence (AI) models are commonly regarded as the cornerstone for NI design, as they have proven extremely successful at solving hard problems that require inferring complex relationships from entangled, massive (network traffic) data. However, the common approach of plugging ‘vanilla’ AI models into controllers and orchestrators does not fulfil the potential of the technology. Instead, AI models should be tailored to the specific network level and respond to the specific needs of network functions, eventually coordinated by an end-to-end NI-native architecture for 6G. In this paper, we discuss these challenges and provide results for a candidate NI-driven functionality that is properly integrated into the proposed architecture: network capacity forecasting.European UnionTRUEpu
Site-specific millimeter-wave compressive channel estimation algorithms with hybrid MIMO architectures
Get PDFIn this paper, we present and compare three novel model‑cum‑data‑driven channel estimation procedures in a millimeter‑wave Multi‑Input Multi‑Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) wireless communication system. The transceivers employ a hybrid analog‑digital architecture. We adapt techniques from a wide range of signal processing methods, such as detection and estimation theories, compressed sensing, and Bayesian inference, to learn the unknown virtual beamspace domain dictionary, as well as the delay‑and‑beamspace sparse channel. We train the model‑based algorithms with a site‑speciϔic training dataset generated using a realistic ray tracing‑based wireless channel simulation tool. We assess the performance of the proposed channel estimation algorithms with the same site’s test data. We benchmark the performance of our novel procedures in terms of normalized mean squared error against an existing fast greedy method and empirically show that model‑based approaches combined with data‑driven customization unanimously outperform the state‑of‑the‑art techniques by a large margin. The proposed algorithms were selected as the top three solutions in the “ML5G‑PHY Channel Estimation Global Challenge 2020” organized by the International Telecommunication Union.TRUEpu
Byzantine-tolerant Distributed Grow-only Sets: Specification and Applications
Get PDFIn order to formalize Distributed Ledger Technologies and their interconnections, a recent line of research work has formulated the notion of Distributed Ledger Object (DLO), which is a concurrent object that maintains a totally ordered sequence of records, abstracting blockchains and distributed ledgers. Through DLO, the Atomic Appends problem, intended as the need of a primitive able to append multiple records to distinct ledgers in an atomic way, is studied as a basic interconnection problem among ledgers.
In this work, we propose the Distributed Grow-only Set object (DSO), which instead of maintaining a sequence of records, as in a DLO, maintains a set of records in an immutable way: only Add and Get operations are provided. This object is inspired by the Grow-only Set (G-Set) data type which is part of the Conflict-free Replicated Data Types. We formally specify the object and we provide a consensus-free Byzantine-tolerant implementation that guarantees eventual consistency. We then use our Byzantine-tolerant DSO (BDSO) implementation to provide consensus-free algorithmic solutions to the Atomic Appends and Atomic Adds (the analogous problem of atomic appends applied on G-Sets) problems, as well as to construct consensus-free Single-Writer BDLOs.
We believe that the BDSO has applications beyond the above-mentioned problems.TRUEpu
Positioning and Sensing for Vehicular Safety Applications in 5G and Beyond
Get PDFThis paper presents a shared vision among stake-holders across the value chain on the use of radio positioning and sensing for road safety in the 5G ecosystem. The key enabling technologies and architectural functionalities are explored, focusing on the extremely stringent localization and communication requirements. A case study for joint radar and communication using experimental data showcases the potential of the new enablers that are paving the way towards enhanced road safety in Beyond 5G scenarios.European UnionTRUEpu
Accurate Ubiquitous Localization with Off-the-Shelf IEEE 802.11ac Devices
Get PDFWiFi location systems are remarkably accurate, with decimeter-level errors for recent CSI-based systems. However, such high accuracy is achieved under Line-of-Sight (LOS) conditions and with an access point (AP) density that is much higher than that typically found in current deployments that primarily target good coverage. In contrast, when many of the APs within range are in Non-Line-of-Sight (NLOS), the location accuracy degrades drastically.
In this paper we present UbiLocate, a WiFi location system that copes well with common AP deployment densities and works ubiquitously, i.e., without excessive degradation under NLOS. UbiLocate demonstrates that meter-level median accuracy NLOS localization is possible through (i) an innovative angle estimator
based on a Nelder-Mead search, (ii) a fine-grained time of flight ranging system with nanosecond resolution, and (iii) the accuracy improvements brought about by the increase in bandwidth and number of antennas of IEEE 802.11ac. In combination, they provide superior resolvability of multipath components, significantly improving location accuracy over prior work. We implement our location system on off-the-shelf 802.11ac devices and make the implementation, CSI-extraction tool and custom Fine Timing Measurement design publicly available to the research community. We carry out an extensive performance analysis of our system and show that it outperforms current state-of-the-art location systems by a factor of 2-3, both under LOS and NLOS.TRUEpu