Analysis and optimal configuration of distributed opportunistic scheduling techniques in wireless networks

The phenomenon of fading in wireless communications has traditionally been considered as a source of unreliability that needs to be mitigated. In contrast, Opportunistic Scheduling (OS) techniques exploit quick channel quality oscillations in fading links, during the assignment of transmission opportunities, to improve the performance of wireless networks. While centralized mechanisms rely on a central entity with global knowledge, Distributed Opportunistic Scheduling (DOS) techniques have recently been proposed to work in distributed networks, i.e., where either such a central entity is not available, or the communication overhead to feed timely information to this central entity is prohibitive. With DOS, each station contends for the channel with a certain access probability. If a contention is successful, the station measures the channel conditions and transmits if the channel quality is above a certain threshold. Otherwise, the station does not use the transmission opportunity, allowing all stations to recontend. Given the fact that different stations, in different time instances, experience different channel conditions, it is likely that the channel is used by a link with better conditions, improving overall performance. In this thesis we first propose ADOS, an adaptive mechanism that drives the system to an optimal allocation of resources in terms of proportional fairness. We show that this mechanism outperforms previous approaches, particularly in scenarios with non-saturated stations (that do not always have data to transmit). The distributed nature of DOS makes it particularly vulnerable to selfish users that seek to maximize their own performance at the expense of those that cooperate for the common welfare. We thus design a punishing mechanism, namely DOC, that (i) drives the system to the optimal point of operation when all stations follow the protocol, and (ii) removes any potential gain by deviating from it (and thus, the incentive to misbehave). Finally, we propose a novel allocation criterion, namely the EF criterion, to balance between the most energy-eficient configuration (where all resources are given to the most energy e cient devices) and the throughput-optimal allocation (where all devices evenly share the resources regardless of their power consumption). Due to the lack of models that accurately predict the power consumption behavior of wireless devices, we perform a thorough experimental study to devise a power consumption model that completes existing literature. Finally, we apply these findings to design an EF-optimal strategy in DOS networks. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------El fenómeno de "fading" o desvanecimiento en comunicaciones inalámbricas se ha considerado tradicionalmente como una fuente de problemas de fiabilidad que debe ser mitigada. En contraste, las técnicas de asignación de recursos oportunistas aprovechan las oscilaciones en la calidad de enlaces para mejorar el rendimiento global. Mientras que los mecanismos centralizados requieren una entidad central con información global, recientemente se han propuesto técnicas oportunistas distribuidas (DOS, por sus siglas en inglés) para operar en redes donde dicha entidad no está disponible, o donde el coste en la comunicación para proporcionarle información puntual es prohibitivo. Con DOS, cada estación contiende por el canal con una cierta probabilidad. Si la contienda resulta exitosa, la estación mide la calidad del canal y transmite si ésta supera un cierto umbral. De lo contrario, la estación no aprovecha esa oportunidad para transmitir, permitiendo a todas las estaciones contender de nuevo. Dado que estaciones diferentes, en distintas instancias de tiempo, experimentan diferentes condiciones de canal, es probable que un enlace con mejores condiciones use el canal, mejorando el rendimiento global. En esta tesis proponemos primero ADOS, un mecanismo adaptativo que lleva al sistema a un reparto óptimo de los recursos en términos de equidad proporcional. Mostramos que este mecanismo supera el rendimiento de trabajos previos, particularmente en escenarios con estaciones no saturados (que no siempre tienen datos que transmitir). La naturaleza distribuida de DOS lo hace particularmente vulnerable a usuarios egoístas que buscan maximizar su rendimiento a expensas de aquellos que cooperan por el bien común. Así, diseñamos un mecanismo, llamado DOC, que (i) optimiza el rendimiento si todos los nodos obedecen el protocolo, y (ii) elimina cualquier posible beneficio por desviarse del mismo (y así, el incentivo a no cooperar). Finalmente, proponemos un nuevo criterio de asignación de recursos, llamado EF, que supone un compromiso entre la configuración más eficiente energéticamente (donde todos los recursos se asignan a los nodos más eficientes) y una asignación donde todos comparten de forma equitativa los recursos sin tener en cuenta su consumo. Dada la falta de modelos para predecir de forma precisa el consumo de dispositivos inalámbricos, llevamos a cabo un estudio experimental que resulta en un modelo energético que completa a la literatura existente. Finalmente, aplicamos lo anterior para diseñar una estrategia que optimiza EF en redes basadas en DOS

Network intelligence in 6G: challenges and opportunities

Proceeding of: the 16th ACM Workshop on Mobility in the Evolving Internet Architecture (in conjunction with MobiCom 2021: The 27th Annual International Conference On Mobile Computing And Networking, January 31-February 04, 2022, New Orleans, United States)The 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.The authors of this paper have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101017109 (DAEMON Network intelligence for aDAptive and sElf-Learning MObile Networks). This paper is also funded by the Spanish State Research Agency (TRUE5G project, PID2019-108713RB-C52PID2019-108713RB-C52 /AEI / 10.13039/501100011033)Publicad

Joint Optimization of Edge Computing Architectures and Radio Access Networks

Virtualized radio access network (vRAN) architectures and multiple-access edge computing (MEC) systems constitute two key solutions for the emerging Tactile Internet applications and the increasing mobile data traffic. Their efficient deployment, however, requires a careful design tailored to the available network resources and user demand. In this paper, we propose a novel modeling approach and a rigorous analytical framework, MEC-vRAN joint design problem (MvRAN), that minimizes vRAN costs and maximizes MEC performance. Our framework selects jointly the base-station function splits, the fronthaul routing paths, and the placement of MEC functions. We follow a data-driven evaluation method, using topologies of three operational networks and experiments with a typical face-recognition MEC service. Our results reveal that MvRAN achieves significant cost savings (up to 2.5 times) compared to non-optimized centralized RAN or decentralized RAN systems, and MEC pushes the vRAN functions to radio units and hence can increase substantially the network cost.Work supported by the EC under Grant No 761536 (5GTransformer) and by SFI under Grant No 17/CDA/4760

Orchestrating energy-efficient vRANs: Bayesian learning and experimental results

Virtualized base stations (vBS) can be implemented in diverse commodity platforms and are expected to bring unprecedented operational flexibility and cost efficiency to the next generation of cellular networks. However, their widespread adoption is hampered by their complex configuration options that affect in a non-traditional fashion both their performance and their power consumption requirements. Following an in-depth experimental analysis in a bespoke testbed, we characterize the vBS power cost profile and reveal previously unknown couplings between their various control knobs. Motivated by these findings, we develop a Bayesian learning framework for the orchestration of vBSs and design two novel algorithms: (i) BP-vRAN, which employs online learning to balance the vBS performance and energy consumption, and (ii) SBP-vRAN, which augments our optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient, i.e., converge an order of magnitude faster than state-of-the-art Deep Reinforcement Learning methods, and achieve optimal performance. We demonstrate the efficacy of these solutions in an experimental prototype using real traffic traces.This work has been supported by the European Commission through Grant No. 101017109 (DAEMON project), and the CERCA Programme/Generalitat de Catalunya

Fair coexistence of scheduled and random access wireless networks: unlicensed LTE/WiFi

We study the fair coexistence of scheduled and random access transmitters sharing the same frequency channel. Interest in coexistence is topical due to the need for emerging unlicensed LTE technologies to coexist fairly withWiFi. However, this interest is not confined to LTE/WiFi as coexistence is likely to become increasingly commonplace in IoT networks and beyond 5G. In this paper, we show that mixing scheduled and random access incurs an inherent throughput/delay cost and the cost of heterogeneity. We derive the joint proportional fair rate allocation, which casts useful light on current LTE/WiFi discussions. We present experimental results on inter-technology detection and consider the impact of imperfect carrier sensing.This work was supported in part by the Science Foundation Ireland under Grant 11/PI/1177 and Grant 13/RC/207, in part by the European Commission in the framework of the H2020-ICT-2014-2 Project Flex5Gware under Grant 671563, and in part by the Spanish Ministry of Economy and the FEDER regional development fund through SINERGIA Project under Grant TEC2015-71303-R

Adaptive mechanism for distributed opportunistic scheduling

Distributed opportunistic scheduling (DOS) techniques have been recently proposed for improving the throughput performance of wireless networks. With DOS, each station contends for the channel with a certain access probability. If a contention is successful, the station measures the channel conditions and transmits in case the channel quality is above a certain threshold. Otherwise, the station does not use the transmission opportunity, allowing all stations to recontend. A key challenge with DOS is to design a distributed algorithm that optimally adjusts the access probability and the threshold of each station. To address this challenge, in this paper, we first compute the configuration of these two parameters that jointly optimizes throughput performance in terms of proportional fairness. Then, we propose an adaptive algorithm based on control theory that converges to the desired point of operation. Finally, we conduct a control theoretic analysis of the algorithm to find a setting for its parameters that provides a good tradeoff between stability and speed of convergence. Simulation results validate the design of our mechanism and confirm its advantages over previous works.This work was funded by the European Community's 7th Framework Program FP7/2007-2013 under Grant 317941 (iJOIN) and by the Madrid Regional Government’s TIGRE5-CM program (S2013/ICE-2919)Publicad

Overbooking Network Slices End-to-End: Implementation and Demonstration

This paper has been presented at: ACM SIGCOMM 2018 Conference on Posters and DemosThe novel network slicing paradigm allows service providers to open their infrastructure to new business players such as vertical industries. In this demo, we showcase the benefits of our proposed end-to-end network slicing orchestration solution that blends together i) an admission control engine able to handle heterogeneous network slice requests, ii) a resource allocation solution across multiple network domains: radio access, edge, transport and core networks and iii) a monitoring, forecasting and dynamic configuration solution that maximizes the statistical multiplexing of network slices resources. Our orchestration solution is operated through a dashboard that allows requesting network slices on-demand, monitors their performance once deployed and displays the achieved multiplexing gain through overbooking

Overbooking Network Slices through Yield-driven End-to-End Orchestration

Proceeding of: 14th International Conference on emerging Networking EXperiments and Technologies (CoNEXT '18)Network slicing allows mobile operators to offer, via proper abstractions, mobile infrastructure (radio, networking, computing) to vertical sectors traditionally alien to the telco industry (e.g., automotive, health, construction). Owning to similar business nature, in this paper we adopt yield management models successful in other sectors (e.g. airlines, hotels, etc.) and so we explore the concept of slice overbooking to maximize the revenue of mobile operators. The main contribution of this paper is threefold. First, we design a hierarchical control plane to manage the orchestration of slices end-to-end, including radio access, transport network, and distributed computing infrastructure. Second, we cast the orchestration problem as a stochastic yield management problem and propose two algorithms to solve it: an optimal Benders decomposition method and a suboptimal heuristic that expedites solutions. Third, we implement an experimental proof-of-concept and assess our approach both experimentally and via simulations with topologies from three real operators and a wide set of realistic scenarios. Our performance evaluation shows that slice overbooking can provide up to 3x revenue gains in realistic scenarios with minimal footprint on service-level agreements (SLAs).This work was supported in part by the H2020 5G-Transformer Project under Grant 761536 and in part by H2020-MSCA-ITN-2015 5G-Aura Project under Grant 675806

Latency-driven Network Slices Orchestration

This paper has been presented at: IEEE Conference on Computer Communications Workshops ( INFOCOM'19 )The novel concept of network slicing is envisioned to allow service providers to open their infrastructure to vertical industries traditionally alien to mobile networks, such as automotive, health or factories. In this way multiple vertical services can be delivered over the same physical facilities by means of advanced network virtualization techniques. However, the vertical service requirements heterogeneity (e.g., high throughput, low latency, high reliability) calls for novel orchestration solutions able to manage end-to-end network slice resources across different domains while satisfying stringent service level agreements. In this demonstration we will show a novel orchestration solution able to handle one of the most stringent requirements: end-to-end latency. Our testbed-evolution of the work presented in [1]-implements all the resource brokerage schemes and allocation operations necessary to complete the life-cycle management of network slices. In addition, the novel overbooking concept is applied to pursue the overall revenue maximization when admitting network slices. Finally, an advanced network slicing monitoring system will be provided as a user-friendly dashboard allowing users to interact with the proposed solution.This work was supported by the H2020 5G-Transformer Project under Grant 761536 and by the H2020-MSCA-ITN-2015 5G-AURA Project under Grant 675806

LaSR: A Supple Multi-Connectivity Scheduler for Multi-RAT OFDMA Systems

Network densification over space and spectrum is expected to be key to enabling the requirements of next generation mobile systems. The pitfall is that radio resource allocation becomes substantially more complex. In this paper we propose LaSR, a practical multi-connectivity scheduler for OFDMA-based multi-RAT systems. LaSR makes optimal discrete control actions by solving a sequence of simple optimization problems that do not require prior information of traffic patterns. In marked contrast to previous work, the flexibility of our approach allows us to construct scheduling policies that achieve a good balance between system cost and utility satisfaction, while jointly operate across heterogeneous RATs, accommodate real-system requirements, and guarantee system stability. Examples of system requirements considered in this paper include (but are not limited to): constraints on how scheduling data can be encoded onto signaling protocols (e.g. LTE’s DCI), delays when turning on/off radio units, or on/off cycles when using unlicensed spectrum. We evaluate our scheduler via a thorough simulation campaign in a variety of scenarios with e.g. mobile users, RATs using unlicensed spectrum (using a duty cycle access mechanism), imperfect queue state information, and constrained signaling protocol.The authors would like to thank the Spanish Government (Ministerio de Economía y Competitividad, Fondo Europeo de Desarrollo Regional, FEDER) for its support through the project ADVICE (TEC2015-71329-C2-1-R) and 5G-Transformer Project (Grant 761536)