Theoretical and Computational Basis for CATNETS - Annual Report Year 3

In this document the developments in defining the computational and theoretical framework for economical resource allocation are described. Accordingly the formal specification of the market mechanisms, bidding strategies of the involved agents and the integration of the market mechanisms into the simulator were refined. --Grid Computing

Theoretical and Computational Basis for CATNETS - Annual Report Year 2

In this work the self-organising potential of the CATNETS allocation mechanism is described to provide a more comprehensive view on the research done in this project. The formal description of either the centralised and decentralised approach is presented. Furthermore the agents' bidding model is described and a comprehensive overview on how the catallactic mechanism is incorporated into the middleware and simulator environments is given. --Decentralized Market Mechanisms,Centralized Market Mechanisms,Catallaxy,Market Engineering,Simulator Integration,Prototype Integration

Theoretical and computational basis for CATNETS - annual report year 3

In this document the developments in defining the computational and theoretical framework for economical resource allocation are described. Accordingly the formal specification of the market mechanisms, bidding strategies of the involved agents and the integration of the market mechanisms into the simulator were refined

Applications of Game Theory and Microeconomics in Cognitive Radio and Femtocell Networks

Cognitive radio networks have recently been proposed as a promising approach to overcome the serious problem of spectrum scarcity. Other emerging concept for innovative spectrum utilization is femtocells. Femtocells are low-power and short-range wireless access points installed by the end-user in residential or enterprise environments. A common feature of cognitive radio and femtocells is their two-tier nature involving primary and secondary users (PUs, SUs). While this new paradigm enables innovative alternatives to conventional spectrum management and utilization, it also brings its own technical challenges. A main challenge in cognitive radio is the design of efficient resource (spectrum) trading methods. Game and microeconomics theories provide tools for studying the strategic interactions through rationality and economic benefits between PUs and SUs for effective resource allocation. In this thesis, we investigate some efficient game theoretic and microeconomic approaches to address spectrum trading in cognitive networks. We propose two auction frameworks for shared and exclusive use models. In the first auction mechanism, we consider the shared used model in cognitive radio networks and design a spectrum trading method to maximize the total satisfaction of the SUs and revenue of the Wireless Service Provider (WSP). In the second auction mechanism, we investigate spectrum trading via auction approach for exclusive usage spectrum access model in cognitive radio networks. We consider a realistic valuation function and propose an efficient concurrent Vickrey-Clarke-Grove (VCG) mechanism for non-identical channel allocation among r-minded bidders in two different cases. The realization of cognitive radio networks in practice requires the development of effective spectrum sensing methods. A fundamental question is how much time to allocate for sensing purposes. In the literature on cognitive radio, it is commonly assumed that fixed time durations are assigned for spectrum sensing and data transmission. It is however possible to improve the network performance by finding the best tradeoff between sensing time and throughput. In this thesis, we derive an expression for the total average throughput of the SUs over time-varying fading channels. Then we maximize the total average throughput in terms of sensing time and the number of SUs assigned to cooperatively sense each channel. For practical implementation, we propose a dynamical programming algorithm for joint optimization of sensing time and the number of cooperating SUs for sensing purpose. Simulation results demonstrate that significant improvement in the throughput of SUs is achieved in the case of joint optimization. In the last part of the thesis, we further address the challenge of pricing in oligopoly market for open access femtocell networks. We propose dynamic pricing schemes based on microeconomic and game theoretic approaches such as market equilibrium, Bertrand game, multiple-leader-multiple-follower Stackelberg game. Based on our approaches, the per unit price of spectrum can be determined dynamically and mobile service providers can gain more revenue than fixed pricing scheme. Our proposed methods also provide residential customers more incentives and satisfaction to participate in open access model.1 yea

Smart Sustainable Mobility: Analytics and Algorithms for Next-Generation Mobility Systems

To this date, mobility ecosystems around the world operate on an uncoordinated, inefficient and unsustainable basis. Yet, many technology-enabled solutions that have the potential to remedy these societal negatives are already at our disposal or just around the corner. Innovations in vehicle technology, IoT devices, mobile connectivity and AI-powered information systems are expected to bring about a mobility system that is connected, autonomous, shared and electric (CASE). In order to fully leverage the sustainability opportunities afforded by CASE, system-level coordination and management approaches are needed. This Thesis sets out an agenda for Information Systems research to shape the future of CASE mobility through data, analytics and algorithms (Chapter 1). Drawing on causal inference, (spatial) machine learning, mathematical programming and reinforcement learning, three concrete contributions toward this agenda are developed. Chapter 2 demonstrates the potential of pervasive and inexpensive sensor technology for policy analysis. Connected sensing devices have significantly reduced the cost and complexity of acquiring high-resolution, high-frequency data in the physical world. This affords researchers the opportunity to track temporal and spatial patterns of offline phenomena. Drawing on a case from the bikesharing sector, we demonstrate how geo-tagged IoT data streams can be used for tracing out highly localized causal effects of large-scale mobility policy interventions while offering actionable insights for policy makers and practitioners. Chapter 3 sets out a solution approach to a novel decision problem faced by operators of shared mobility fleets: allocating vehicle inventory optimally across a network when competition is present. The proposed three-stage model combines real-time data analytics, machine learning and mixed integer non-linear programming into an integrated framework. It provides operational decision support for fleet managers in contested shared mobility markets by generating optimal vehicle re-positioning schedules in real time. Chapter 4 proposes a method for leveraging data-driven digital twin (DT) frameworks for large multi-stage stochastic design problems. Such problem classes are notoriously difficult to solve with traditional stochastic optimization. Drawing on the case of Electric Vehicle Charging Hubs (EVCHs), we show how high-fidelity, data-driven DT simulation environments fused with reinforcement learning (DT-RL) can achieve (close-to) arbitrary scalability and high modeling flexibility. In benchmark experiments we demonstrate that DT-RL-derived designs result in superior cost and service-level performance under real-world operating conditions

Efficient sharing mechanisms for virtualized multi-tenant heterogeneous networks

The explosion in data traffic, the physical resource constraints, and the insufficient financial incentives for deploying 5G networks, stress the need for a paradigm shift in network upgrades. Typically, operators are also the service providers, which charge the end users with low and flat tariffs, independently of the service enjoyed. A fine-scale management of the network resources is needed, both for optimizing costs and resource utilization, as well as for enabling new synergies among network owners and third-parties. In particular, operators could open their networks to third parties by means of fine-scale sharing agreements over customized networks for enhanced service provision, in exchange for an adequate return of investment for upgrading their infrastructures. The main objective of this thesis is to study the potential of fine-scale resource management and sharing mechanisms for enhancing service provision and for contributing to a sustainable road to 5G. More precisely, the state-of-the-art architectures and technologies for network programmability and scalability are studied, together with a novel paradigm for supporting service diversity and fine-scale sharing. We review the limits of conventional networks, we extend existing standardization efforts and define an enhanced architecture for enabling 5G networks' features (e.g., network-wide centralization and programmability). The potential of the proposed architecture is assessed in terms of flexible sharing and enhanced service provision, while the advantages of alternative business models are studied in terms of additional profits to the operators. We first study the data rate improvement achievable by means of spectrum and infrastructure sharing among operators and evaluate the profit increase justified by a better service provided. We present a scheme based on coalitional game theory for assessing the capability of accommodating more service requests when a cooperative approach is adopted, and for studying the conditions for beneficial sharing among coalitions of operators. Results show that: i) collaboration can be beneficial also in case of unbalanced cost redistribution within coalitions; ii) coalitions of equal-sized operators provide better profit opportunities and require lower tariffs. The second kind of sharing interaction that we consider is the one between operators and third-party service providers, in the form of fine-scale provision of customized portions of the network resources. We define a policy-based admission control mechanism, whose performance is compared with reference strategies. The proposed mechanism is based on auction theory and computes the optimal admission policy at a reduced complexity for different traffic loads and allocation frequencies. Because next-generation services include delay-critical services, we compare the admission control performances of conventional approaches with the proposed one, which proves to offer near real-time service provision and reduced complexity. Besides, it guarantees high revenues and low expenditures in exchange for negligible losses in terms of fairness towards service providers. To conclude, we study the case where adaptable timescales are adopted for the policy-based admission control, in order to promptly guarantee service requirements over traffic fluctuations. In order to reduce complexity, we consider the offline pre­computation of admission strategies with respect to reference network conditions, then we study the extension to unexplored conditions by means of computationally efficient methodologies. Performance is compared for different admission strategies by means of a proof of concept on real network traces. Results show that the proposed strategy provides a tradeoff in complexity and performance with respect to reference strategies, while reducing resource utilization and requirements on network awareness.La explosion del trafico de datos, los recursos limitados y la falta de incentivos para el desarrollo de 5G evidencian la necesidad de un cambio de paradigma en la gestion de las redes actuales. Los operadores de red suelen ser tambien proveedores de servicios, cobrando tarifas bajas y planas, independientemente del servicio ofrecido. Se necesita una gestion de recursos precisa para optimizar su utilizacion, y para permitir nuevas sinergias entre operadores y proveedores de servicios. Concretamente, los operadores podrian abrir sus redes a terceros compartiendolas de forma flexible y personalizada para mejorar la calidad de servicio a cambio de aumentar sus ganancias como incentivo para mejorar sus infraestructuras. El objetivo principal de esta tesis es estudiar el potencial de los mecanismos de gestion y comparticion de recursos a pequei\a escala para trazar un camino sostenible hacia el 5G. En concreto, se estudian las arquitecturas y tecnolog fas mas avanzadas de "programabilidad" y escalabilidad de las redes, junto a un nuevo paradigma para la diversificacion de servicios y la comparticion de recursos. Revisamos los limites de las redes convencionales, ampliamos los esfuerzos de estandarizacion existentes y definimos una arquitectura para habilitar la centralizacion y la programabilidad en toda la red. La arquitectura propuesta se evalua en terminos de flexibilidad en la comparticion de recursos, y de mejora en la prestacion de servicios, mientras que las ventajas de un modelo de negocio alternativo se estudian en terminos de ganancia para los operadores. En primer lugar, estudiamos el aumento en la tasa de datos gracias a un uso compartido del espectro y de las infraestructuras, y evaluamos la mejora en las ganancias de los operadores. Presentamos un esquema de admision basado en la teoria de juegos para acomodar mas solicitudes de servicio cuando se adopta un enfoque cooperativo, y para estudiar las condiciones para que la reparticion de recursos sea conveniente entre coaliciones de operadores. Los resultados ensei\an que: i) la colaboracion puede ser favorable tambien en caso de una redistribucion desigual de los costes en cada coalicion; ii) las coaliciones de operadores de igual tamai\o ofrecen mejores ganancias y requieren tarifas mas bajas. El segundo tipo de comparticion que consideramos se da entre operadores de red y proveedores de servicios, en forma de provision de recursos personalizada ya pequei\a escala. Definimos un mecanismo de control de trafico basado en polfticas de admision, cuyo rendimiento se compara con estrategias de referencia. El mecanismo propuesto se basa en la teoria de subastas y calcula la politica de admision optima con una complejidad reducida para diferentes cargas de trafico y tasa de asignacion. Con particular atencion a servicios 5G de baja latencia, comparamos las prestaciones de estrategias convencionales para el control de admision con las del metodo propuesto, que proporciona: i) un suministro de servicios casi en tiempo real; ii) una complejidad reducida; iii) unos ingresos elevados; y iv) unos gastos reducidos, a cambio de unas perdidas insignificantes en terminos de imparcialidad hacia los proveedores de servicios. Para concluir, estudiamos el caso en el que se adoptan escalas de tiempo adaptables para el control de admision, con el fin de garantizar puntualmente los requisitos de servicio bajo diferentes condiciones de trafico. Para reducir la complejidad, consideramos el calculo previo de las estrategias de admision con respecto a condiciones de red de referenda, adaptables a condiciones inexploradas por medio de metodologias computacionalmente eficientes. Se compara el rendimiento de diferentes estrategias de admision sobre trazas de trafico real. Los resultados muestran que la estrategia propuesta equilibra complejidad y ganancias, mientras se reduce la utilizacion de recursos y la necesidad de conocer el estado exacto de la red.Postprint (published version