Theoretical-practical study of automated radioelectric spectrum trading mechanisms

[SPA]El incremento de la demanda de comunicaciones inalámbricas frente a un estático espectro radioeléctrico con el que hacerle frente ha terminado con éste casi asignado por completo, que no ocupado: la solución pasa por utilizarlo de forma más eficiente y uno de los mecanismos planteados es el comercio automático de espectro: hacer posible que los operadores con licencia alquilen porciones a otros para satisfacer demandas de usuarios en tiempo real en un mercado secundario, que permitiría un uso mayor y más dinámico del espectro al tiempo que mantiene los incentivos de los operadores que ya poseen licencia. La casuística de este área se debe al hecho de ser una problemática reciente, así como lo es la herramienta más hábitual para su resolución, Teoría de Juegos; y al número de modelos económicos de comercio pre-existentes con que se puede estudiar, que además no pueden aplicarse directamente por las peculiaridades del bien con que se comercia así como de los agentes. Este trabajo busca exponer una visión general, ordenada y didáctica de las líneas de investigación existentes en este concepto. Se muestra como los distintos trabajos desglosan el comercio de espectro en diferentes subbproblemas y sus combinaciones, con aplicaciones reales todavía lejanas y cómo la Teoría de Juegos es la solución que se adapta de forma más natural al sentido del mismo. [ENG] The increasing demand of wireless communications versus an static radio-electric spectrum to cope with it has led to an almost fully assigned but sparsely used spectrum. This work studies one of the mechanisms proposed to improve spectrum efficiency, automated spectrum trading: licensed operators would be able to lease unused bandwidth to unlicensed ones so as to satisfy real time demands from users in secondary markets, resulting in a higher and more dynamic usage of spectrum while having the advantage over any other resource allocation method that there is an incentive to those who got a license. The different case studies in the area exist due to the fact that it is a recent field of study and so it is the main tool used here: Game Theory; along with the number of economic models to study it, which can’t be directly applied because of the particular characteristics of the trading good and agents. We are looking to give a general, organized and didactic view of the diverse research lines on the area, showing how different works break spectrum trading up into what sub-problems and their combinations, still far from real applications, and how Game Theory is the most common and natural approach to deal with them.Escuela Técnica Superior de Ingeniería de TelecomunicaciónUniversidad Politécnica de Cartagen

Evolutionary Solutions and Internet Applications for Algorithmic Game Theory

The growing pervasiveness of the internet has created a new class of algorithmic problems: those in which the strategic interaction of autonomous, self-interested entities must be accounted for. So motivated, we seek to (1) use game theoretic models and techniques to study practical problems in load balancing, data streams and internet traffic congestion, and (2) demonstrate the usefulness of evolutionary game theory's adaptive learning model as an analytical and evaluative tool.First we consider the evolutionary game theory concept of stochastic stability, and propose the price of stochastic anarchy as an alternative to the price of anarchy for quantifying the cost of having no central authority. Unlike Nash equilibria, stochastically stable states are the result of natural dynamics of large populations of computationally bounded agents, and are resilient to small perturbations from ideal play. To illustrate the utility of stochastic stability, we study the load balancing game on related machines, which has an unbounded price of anarchy, even in the case of two jobs and two machines. We show that in contrast, even in the general case, the price of stochastic anarchy is bounded.Next, we propose auction-based mechanisms for admission control of continuous queries to a Data Stream Management System. When submitting a query, each user also submits a bid: how much she is willing to pay for her query to run. Our mechanisms must admit queries and set payments in a way that maximizes system revenue while incentivizing customers to use the system honestly. We propose several manipulation-resistant payment mechanisms and prove that one guarantees a profit close to a standard profit benchmark, and the others perform well experimentally.Finally, we study the long standing problem of congestion control at bottleneck routers on the internet. We examine the effectiveness of commonly-used queuing policies when each network endpoint is self-interested and has no information about the other endpoints' actions or preferences. By employing evolutionary game theory, we find that while bottleneck routers face heavy congestion at stochastically stable states under policies being currently deployed, a practical policy that was recently proposed yields fair and efficient conditions with no congestion

Combinatorial exchange models for a user-driven air traffic flow management in Europe

2008/2009Air Traffic Flow Management (ATFM) is the service responsible to guarantee that the available capacity of the air transportation system is efficiently used and never exceeded. It guarantees safety of air transportation by adopting a series of measures which range from strategic long-term ones to the imposition of ground delays to flights at a tactical level. These ATFM delays are imposed to individual flights at the departure airport prior to their take-off, since it is safer and less costly to anticipate on the ground any delay predicted somewhere in the system. They are assigned by a central authority according to a First-Planned-First-Served principle, without taking into account individual Airlines' preferences. This criteria of assignment can cause an aggregated cost of delay experienced by users, higher than the minimal one, due to the fact that the cost of delay is a non-linear function of the duration and it depends on many variables such as the type of aircraft, the specific origin-destination pair, ecc. This thesis tackles the issue of formalizing and analyzing alternative models for the assignment of ATFM resources which take into account individual airlines preferences. In particular mathematical programming models are analyzed, that extend the concept of ATFM slot currently adopted to the one of Target Window, as proposed in the CATS European project. Such a concept is in line with the SESAR program, recently adopted in Europe to develop the new generation system of Air Traffic Management, which imposes a direct involvement of Airspace users whenever external constraints need to be enforced that modify their original requests. The first Chapter provides a general introduction to the context of Air Traffic Management and Air Traffic Control. In the second Chapter the principles, methods and performances of the ATFM system are described according to the current situation as well as to the SESAR target concept. The problem of optimally assign ATFM resources is then described mathematically and then analyzed to uncover two fundamental structures that determine its tractability: one corresponds to the case in which there is a unique capacity constrained resource while in the second there is an unrestricted number of constrained resources. In Chapter three a number of properties are proved that give insight into the applicability of different mechanisms for a central calculation of the optimal solution by the ATFM authority. Since such mechanisms involve cost minimization for several agents they are formulated as exchanges, i.e. particular types of auctions in which each participant may buy and/or sell several indivisible goods. The last part of the thesis included in Chapter four deals with the design of iterative exchange mechanisms, whose application in real world presents several advantages with respect to centralized models, from the distribution of computational complexity among participants to the preservation of disclosure of private information by Aircraft Operators. In this case an optimal model based on the Lagrangian relaxation of the separable central problem is first formulated and analyzed. To overcome practical issues possibly deriving from its application in real operations, an heuristic iterative Market-based mechanism is finally formalized. This algorithm exploits some of the underlying characteristics specific to the problem to derive near-optimal solutions in an acceptable time. Computational results are obtained by simulating its implementation on real traffic data and they show that considerable cost savings are possible with respect to a First-Planned-First-Served central allocation. The contribute of this thesis is twofold. The first is to provide a mathematical description, modeling and analysis of the ATFM resource exchange problem faced by Airspace users when network capacity needs to be rationed among them. The second consists in the methodological innovation represented by the formulation of the Market Mechanism which is compliant with several requirements represented by legislative and practical constraints and whose simulation provided encouraging results.XXII Cicl

Combinatorial Auction-Based Virtual Machine Provisioning And Allocation In Clouds

Current cloud providers use fixed-price based mechanisms to allocate Virtual Machine (VM) instances to their users. But economic theory states that when there are large amount of resources to be allocated to large number of users, auctions are the most efficient allocation mechanisms. Auctions achieve efficiency of allocation and also maximize the providers\u27 revenue, which a fixed-price based mechanism is unable to do. We argue that combinatorial auctions are best suited for the problem of VM provisioning and allocation in clouds, since they provide the users with the most flexible way to express their requirements. In combinatorial auctions, users bid for bundles of items rather than individual ones, therefore they are able to express whether the items they require are complementary to each other. The objective of this Ph.D. dissertation is to design, study, and implement combinatorial auction-based mechanisms for efficient provisioning and allocation of VM instances in clouds. The central hypothesis is that allocation efficiency and revenue maximization can be obtained by inducing users to fully express and truthfully report their preferences to the system. The rationale for our research is that, once efficient resource provisioning and allocation mechanisms that take into account the incentives of the users and cloud providers are developed and implemented, it will become more efficient to utilize cloud computing environments for solving challenging problems in business, science and engineering. In this dissertation, we present three combinatorial auction-based offline mechanisms to provision and allocation VM instances in clouds. We also present an online mechanism for dynamic provisioning of virtual machine instances in clouds. Finally, we designed an efficient bidding algorithm to assist users submitting bids to combinatorial auction-based mechanisms to execute parallel jobs the cloud. We outline our contribution and possible direction for future research in this field

Automated Markets and Trading Agents

Computer automation has the potential, just starting to be realized, of transforming the design and operation of markets, and the behaviors of agents trading in them. We discuss the possibilities for automating markets, presenting a broad conceptual framework covering resource allocation as well as enabling marketplace services such as search and transaction execution. One of the most intriguing opportunities is provided by markets implementing computationally sophisticated negotiation mechanisms, for example combinatorial auctions. An important theme that emerges from the literature is the centrality of design decisions about matching the domain of goods over which a mechanism operates to the domain over which agents have preferences. When the match is imperfect (as is almost inevitable), the market game induced by the mechanism is analytically intractable, and the literature provides an incomplete characterization of rational bidding policies. A review of the literature suggests that much of our existing knowledge comes from computational simulations, including controlled studies of abstract market designs (e.g., simultaneous ascending auctions), and research tournaments comparing agent strategies in a variety of market scenarios. An empirical game-theoretic methodology combines the advantages of simulation, agent-based modeling, and statistical and game-theoretic analysis.http://deepblue.lib.umich.edu/bitstream/2027.42/49510/1/ace_galleys.pd

A Mechanism Design Approach to Bandwidth Allocation in Tactical Data Networks

The defense sector is undergoing a phase of rapid technological advancement, in the pursuit of its goal of information superiority. This goal depends on a large network of complex interconnected systems - sensors, weapons, soldiers - linked through a maze of heterogeneous networks. The sheer scale and size of these networks prompt behaviors that go beyond conglomerations of systems or `system-of-systems\u27. The lack of a central locus and disjointed, competing interests among large clusters of systems makes this characteristic of an Ultra Large Scale (ULS) system. These traits of ULS systems challenge and undermine the fundamental assumptions of today\u27s software and system engineering approaches. In the absence of a centralized controller it is likely that system users may behave opportunistically to meet their local mission requirements, rather than the objectives of the system as a whole. In these settings, methods and tools based on economics and game theory (like Mechanism Design) are likely to play an important role in achieving globally optimal behavior, when the participants behave selfishly. Against this background, this thesis explores the potential of using computational mechanisms to govern the behavior of ultra-large-scale systems and achieve an optimal allocation of constrained computational resources Our research focusses on improving the quality and accuracy of the common operating picture through the efficient allocation of bandwidth in tactical data networks among self-interested actors, who may resort to strategic behavior dictated by self-interest. This research problem presents the kind of challenges we anticipate when we have to deal with ULS systems and, by addressing this problem, we hope to develop a methodology which will be applicable for ULS system of the future. We build upon the previous works which investigate the application of auction-based mechanism design to dynamic, performance-critical and resource-constrained systems of interest to the defense community. In this thesis, we consider a scenario where a number of military platforms have been tasked with the goal of detecting and tracking targets. The sensors onboard a military platform have a partial and inaccurate view of the operating picture and need to make use of data transmitted from neighboring sensors in order to improve the accuracy of their own measurements. The communication takes place over tactical data networks with scarce bandwidth. The problem is compounded by the possibility that the local goals of military platforms might not be aligned with the global system goal. Such a scenario might occur in multi-flag, multi-platform military exercises, where the military commanders of each platform are more concerned with the well-being of their own platform over others. Therefore there is a need to design a mechanism that efficiently allocates the flow of data within the network to ensure that the resulting global performance maximizes the information gain of the entire system, despite the self-interested actions of the individual actors. We propose a two-stage mechanism based on modified strictly-proper scoring rules, with unknown costs, whereby multiple sensor platforms can provide estimates of limited precisions and the center does not have to rely on knowledge of the actual outcome when calculating payments. In particular, our work emphasizes the importance of applying robust optimization techniques to deal with the uncertainty in the operating environment. We apply our robust optimization - based scoring rules algorithm to an agent-based model framework of the combat tactical data network, and analyze the results obtained. Through the work we hope to demonstrate how mechanism design, perched at the intersection of game theory and microeconomics, is aptly suited to address one set of challenges of the ULS system paradigm - challenges not amenable to traditional system engineering approaches

LIPIcs, Volume 251, ITCS 2023, Complete Volume

LIPIcs, Volume 251, ITCS 2023, Complete Volum