109 research outputs found

    On the Shapley-like Payoff Mechanisms in Peer-Assisted Services with Multiple Content Providers

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    This paper studies an incentive structure for cooperation and its stability in peer-assisted services when there exist multiple content providers, using a coalition game theoretic approach. We first consider a generalized coalition structure consisting of multiple providers with many assisting peers, where peers assist providers to reduce the operational cost in content distribution. To distribute the profit from cost reduction to players (i.e., providers and peers), we then establish a generalized formula for individual payoffs when a "Shapley-like" payoff mechanism is adopted. We show that the grand coalition is unstable, even when the operational cost functions are concave, which is in sharp contrast to the recently studied case of a single provider where the grand coalition is stable. We also show that irrespective of stability of the grand coalition, there always exist coalition structures which are not convergent to the grand coalition. Our results give us an important insight that a provider does not tend to cooperate with other providers in peer-assisted services, and be separated from them. To further study the case of the separated providers, three examples are presented; (i) underpaid peers, (ii) service monopoly, and (iii) oscillatory coalition structure. Our study opens many new questions such as realistic and efficient incentive structures and the tradeoffs between fairness and individual providers' competition in peer-assisted services.Comment: 13 pages, 4 figures, an extended version of the paper to be presented in ICST GameNets 2011, Shanghai, China, April 201

    Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory

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    Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization

    Multi-attribute demand characterization and layered service pricing

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    As cloud computing gains popularity, understanding the pattern and structure of its workload is increasingly important in order to drive effective resource allocation and pricing decisions. In the cloud model, virtual machines (VMs), each consisting of a bundle of computing resources, are presented to users for purchase. Thus, the cloud context requires multi-attribute models of demand. While most of the available studies have focused on one specific attribute of a virtual request such as CPU or memory, to the best of our knowledge there is no work on the joint distribution of resource usage. In the first part of this dissertation, we develop a joint distribution model that captures the relationship among multiple resources by fitting the marginal distribution of each resource type as well as the non-linear structure of their correlation via a copula distribution. We validate our models using a public data set of Google data center usage. Constructing the demand model is essential for provisioning revenue-optimal configuration for VMs or quality of service (QoS) offered by a provider. In the second part of the dissertation, we turn to the service pricing problem in a multi-provider setting: given service configurations (qualities) offered by different providers, choose a proper price for each offered service to undercut competitors and attract customers. With the rise of layered service-oriented architectures there is a need for more advanced solutions that manage the interactions among service providers at multiple levels. Brokers, as the intermediaries between customers and lower-level providers, play a key role in improving the efficiency of service-oriented structures by matching the demands of customers to the services of providers. We analyze a layered market in which service brokers and service providers compete in a Bertrand game at different levels in an oligopoly market while they offer different QoS. We examine the interaction among players and the effect of price competition on their market shares. We also study the market with partial cooperation, where a subset of players optimizes their total revenue instead of maximizing their own profit independently. We analyze the impact of this cooperation on the market and customers' social welfare

    Towards a human-centric data economy

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    Spurred by widespread adoption of artificial intelligence and machine learning, “data” is becoming a key production factor, comparable in importance to capital, land, or labour in an increasingly digital economy. In spite of an ever-growing demand for third-party data in the B2B market, firms are generally reluctant to share their information. This is due to the unique characteristics of “data” as an economic good (a freely replicable, non-depletable asset holding a highly combinatorial and context-specific value), which moves digital companies to hoard and protect their “valuable” data assets, and to integrate across the whole value chain seeking to monopolise the provision of innovative services built upon them. As a result, most of those valuable assets still remain unexploited in corporate silos nowadays. This situation is shaping the so-called data economy around a number of champions, and it is hampering the benefits of a global data exchange on a large scale. Some analysts have estimated the potential value of the data economy in US$2.5 trillion globally by 2025. Not surprisingly, unlocking the value of data has become a central policy of the European Union, which also estimated the size of the data economy in 827C billion for the EU27 in the same period. Within the scope of the European Data Strategy, the European Commission is also steering relevant initiatives aimed to identify relevant cross-industry use cases involving different verticals, and to enable sovereign data exchanges to realise them. Among individuals, the massive collection and exploitation of personal data by digital firms in exchange of services, often with little or no consent, has raised a general concern about privacy and data protection. Apart from spurring recent legislative developments in this direction, this concern has raised some voices warning against the unsustainability of the existing digital economics (few digital champions, potential negative impact on employment, growing inequality), some of which propose that people are paid for their data in a sort of worldwide data labour market as a potential solution to this dilemma [114, 115, 155]. From a technical perspective, we are far from having the required technology and algorithms that will enable such a human-centric data economy. Even its scope is still blurry, and the question about the value of data, at least, controversial. Research works from different disciplines have studied the data value chain, different approaches to the value of data, how to price data assets, and novel data marketplace designs. At the same time, complex legal and ethical issues with respect to the data economy have risen around privacy, data protection, and ethical AI practices. In this dissertation, we start by exploring the data value chain and how entities trade data assets over the Internet. We carry out what is, to the best of our understanding, the most thorough survey of commercial data marketplaces. In this work, we have catalogued and characterised ten different business models, including those of personal information management systems, companies born in the wake of recent data protection regulations and aiming at empowering end users to take control of their data. We have also identified the challenges faced by different types of entities, and what kind of solutions and technology they are using to provide their services. Then we present a first of its kind measurement study that sheds light on the prices of data in the market using a novel methodology. We study how ten commercial data marketplaces categorise and classify data assets, and which categories of data command higher prices. We also develop classifiers for comparing data products across different marketplaces, and we study the characteristics of the most valuable data assets and the features that specific vendors use to set the price of their data products. Based on this information and adding data products offered by other 33 data providers, we develop a regression analysis for revealing features that correlate with prices of data products. As a result, we also implement the basic building blocks of a novel data pricing tool capable of providing a hint of the market price of a new data product using as inputs just its metadata. This tool would provide more transparency on the prices of data products in the market, which will help in pricing data assets and in avoiding the inherent price fluctuation of nascent markets. Next we turn to topics related to data marketplace design. Particularly, we study how buyers can select and purchase suitable data for their tasks without requiring a priori access to such data in order to make a purchase decision, and how marketplaces can distribute payoffs for a data transaction combining data of different sources among the corresponding providers, be they individuals or firms. The difficulty of both problems is further exacerbated in a human-centric data economy where buyers have to choose among data of thousands of individuals, and where marketplaces have to distribute payoffs to thousands of people contributing personal data to a specific transaction. Regarding the selection process, we compare different purchase strategies depending on the level of information available to data buyers at the time of making decisions. A first methodological contribution of our work is proposing a data evaluation stage prior to datasets being selected and purchased by buyers in a marketplace. We show that buyers can significantly improve the performance of the purchasing process just by being provided with a measurement of the performance of their models when trained by the marketplace with individual eligible datasets. We design purchase strategies that exploit such functionality and we call the resulting algorithm Try Before You Buy, and our work demonstrates over synthetic and real datasets that it can lead to near-optimal data purchasing with only O(N) instead of the exponential execution time - O(2N) - needed to calculate the optimal purchase. With regards to the payoff distribution problem, we focus on computing the relative value of spatio-temporal datasets combined in marketplaces for predicting transportation demand and travel time in metropolitan areas. Using large datasets of taxi rides from Chicago, Porto and New York we show that the value of data is different for each individual, and cannot be approximated by its volume. Our results reveal that even more complex approaches based on the “leave-one-out” value, are inaccurate. Instead, more complex and acknowledged notions of value from economics and game theory, such as the Shapley value, need to be employed if one wishes to capture the complex effects of mixing different datasets on the accuracy of forecasting algorithms. However, the Shapley value entails serious computational challenges. Its exact calculation requires repetitively training and evaluating every combination of data sources and hence O(N!) or O(2N) computational time, which is unfeasible for complex models or thousands of individuals. Moreover, our work paves the way to new methods of measuring the value of spatio-temporal data. We identify heuristics such as entropy or similarity to the average that show a significant correlation with the Shapley value and therefore can be used to overcome the significant computational challenges posed by Shapley approximation algorithms in this specific context. We conclude with a number of open issues and propose further research directions that leverage the contributions and findings of this dissertation. These include monitoring data transactions to better measure data markets, and complementing market data with actual transaction prices to build a more accurate data pricing tool. A human-centric data economy would also require that the contributions of thousands of individuals to machine learning tasks are calculated daily. For that to be feasible, we need to further optimise the efficiency of data purchasing and payoff calculation processes in data marketplaces. In that direction, we also point to some alternatives to repetitively training and evaluating a model to select data based on Try Before You Buy and approximate the Shapley value. Finally, we discuss the challenges and potential technologies that help with building a federation of standardised data marketplaces. The data economy will develop fast in the upcoming years, and researchers from different disciplines will work together to unlock the value of data and make the most out of it. Maybe the proposal of getting paid for our data and our contribution to the data economy finally flies, or maybe it is other proposals such as the robot tax that are finally used to balance the power between individuals and tech firms in the digital economy. Still, we hope our work sheds light on the value of data, and contributes to making the price of data more transparent and, eventually, to moving towards a human-centric data economy.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Georgios Smaragdakis.- Secretario: Ángel Cuevas Rumín.- Vocal: Pablo Rodríguez Rodrígue

    Development and management of collective network and cloud computing infrastructures

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    Pla de Doctorat industrial de la Generalitat de CatalunyaIn the search and development of more participatory models for infrastructure development and management, in this dissertation, we investigate models for the financing, deployment, and operation of network and cloud computing infrastructures. Our main concern is to overcome the inherent exclusion in participation in the processes of development and management and in the right of use in the current dominant models. Our work starts by studying in detail the model of Guifi.net, a successful bottom-up initiative for building network infrastructure, generally referred to as a community networks. We pay special attention to its governance system and economic organisation because we argue that these are the key components of the success of this initiative. Then, we generalise our findings for any community network, aiming at becoming sustainable and scalable, and we explore the suitability of the Guifi.net model to the cloud computing infrastructure. As a result of both, we coin the attribute extensible to refer to infrastructure that is relatively easy to expand and maintain in contrast to those naturally limited or hard to expand, such as natural resources or highly complex or advanced artificial systems. We conclude proposing a generic model which, in our opinion, is suitable, at least, for managing extensible infrastructure. The Guifi.net model is deeply rooted in the commons; thus, the research in this field, in general, and Elinor Ostrom’s work, in particular, have left a profound imprint in our work. Our results show that the \guifinet model meets almost entirely the principles of long-enduring commons identified by E. Ostrom. This work has been developed as an industrial doctorate. As such, it combines academic research with elements of practice and pursues an effective knowledge transfer between academia and the private sector. Given that the private sector’s partner is a not-for-profit organisation, the effort to create social value has prevailed over the ambition to advance the development of a specific industrial product or particular technology.En la recerca i desenvolupament de models més participatius per al desenvolupament i gestió d'infraestructura, en aquesta tesi investiguem sobre models per al finançament, desplegament i operació d'infraestructures de xarxa i de computació al núvol. La nostra preocupació principal és fer front a l’exclusió inherent dels models dominants actualment pel que fa a la participació en els processos de desenvolupament i gestió i, també, als drets d’us. El nostre treball comença amb un estudi detallat del model de Guifi.net, un cas d'èxit d'iniciativa ciutadana en la construcció d'infraestructura de xarxa, iniciatives que es coneixen com a xarxes comunitàries. En fer-ho, parem una atenció especial al sistema de governança i a l’organització econòmica perquè pensem que són els dos elements claus de l'èxit d'aquesta iniciativa. Tot seguit passem a analitzar d'altres xarxes comunitàries per abundar en la comprensió dels factors determinants per a la seva sostenibilitat i escalabilitat. Després ampliem el nostre estudi analitzant la capacitat i el comportament del model de Guifi.net en el camp de les infraestructures de computació al núvol. A resultes d'aquests estudis, proposem l'atribut extensible per a descriure aquelles infraestructures que són relativament fàcil d'ampliar i gestionar, en contraposició a les que o bé estan limitades de forma natural o be són difícils d'ampliar, com ara els recursos naturals o els sistemes artificials avançats o complexos. Finalitzem aquest treball fent una proposta de model genèric que pensem que és d'aplicabilitat, com a mínim, a tot tipus d'infraestructura extensible. El model de Guifi.net està fortament vinculat als bens comuns. És per això que la recerca en aquest àmbit, en general, i els treballs de Elinor Ostrom en particular, han deixat una forta empremta en el nostre treball. Els resultats que hem obtingut mostren que el model Guifi.net s'ajusta molt bé als principis que segons Ostrom han de complir els béns comuns per ser sostenibles. Aquest treball s'ha desenvolupat com a doctorat industrial. Com a tal, combina la investigació acadèmica amb elements de practica i persegueix una transferència efectiva de coneixement entre l'àmbit acadèmic i el sector privat. Ates que el soci del sector privat és una organització sense ànim de lucre, l’esforç per crear valor social ha prevalgut en l’ambició d’avançar en el desenvolupament d'un producte industrial específic o d'una tecnologia particularPostprint (published version

    Development and management of collective network and cloud computing infrastructures

    Get PDF
    In the search and development of more participatory models for infrastructure development and management, in this dissertation, we investigate models for the financing, deployment, and operation of network and cloud computing infrastructures. Our main concern is to overcome the inherent exclusion in participation in the processes of development and management and in the right of use in the current dominant models. Our work starts by studying in detail the model of Guifi.net, a successful bottom-up initiative for building network infrastructure, generally referred to as a community networks. We pay special attention to its governance system and economic organisation because we argue that these are the key components of the success of this initiative. Then, we generalise our findings for any community network, aiming at becoming sustainable and scalable, and we explore the suitability of the Guifi.net model to the cloud computing infrastructure. As a result of both, we coin the attribute extensible to refer to infrastructure that is relatively easy to expand and maintain in contrast to those naturally limited or hard to expand, such as natural resources or highly complex or advanced artificial systems. We conclude proposing a generic model which, in our opinion, is suitable, at least, for managing extensible infrastructure. The Guifi.net model is deeply rooted in the commons; thus, the research in this field, in general, and Elinor Ostrom’s work, in particular, have left a profound imprint in our work. Our results show that the \guifinet model meets almost entirely the principles of long-enduring commons identified by E. Ostrom. This work has been developed as an industrial doctorate. As such, it combines academic research with elements of practice and pursues an effective knowledge transfer between academia and the private sector. Given that the private sector’s partner is a not-for-profit organisation, the effort to create social value has prevailed over the ambition to advance the development of a specific industrial product or particular technology.En la recerca i desenvolupament de models més participatius per al desenvolupament i gestió d'infraestructura, en aquesta tesi investiguem sobre models per al finançament, desplegament i operació d'infraestructures de xarxa i de computació al núvol. La nostra preocupació principal és fer front a l’exclusió inherent dels models dominants actualment pel que fa a la participació en els processos de desenvolupament i gestió i, també, als drets d’us. El nostre treball comença amb un estudi detallat del model de Guifi.net, un cas d'èxit d'iniciativa ciutadana en la construcció d'infraestructura de xarxa, iniciatives que es coneixen com a xarxes comunitàries. En fer-ho, parem una atenció especial al sistema de governança i a l’organització econòmica perquè pensem que són els dos elements claus de l'èxit d'aquesta iniciativa. Tot seguit passem a analitzar d'altres xarxes comunitàries per abundar en la comprensió dels factors determinants per a la seva sostenibilitat i escalabilitat. Després ampliem el nostre estudi analitzant la capacitat i el comportament del model de Guifi.net en el camp de les infraestructures de computació al núvol. A resultes d'aquests estudis, proposem l'atribut extensible per a descriure aquelles infraestructures que són relativament fàcil d'ampliar i gestionar, en contraposició a les que o bé estan limitades de forma natural o be són difícils d'ampliar, com ara els recursos naturals o els sistemes artificials avançats o complexos. Finalitzem aquest treball fent una proposta de model genèric que pensem que és d'aplicabilitat, com a mínim, a tot tipus d'infraestructura extensible. El model de Guifi.net està fortament vinculat als bens comuns. És per això que la recerca en aquest àmbit, en general, i els treballs de Elinor Ostrom en particular, han deixat una forta empremta en el nostre treball. Els resultats que hem obtingut mostren que el model Guifi.net s'ajusta molt bé als principis que segons Ostrom han de complir els béns comuns per ser sostenibles. Aquest treball s'ha desenvolupat com a doctorat industrial. Com a tal, combina la investigació acadèmica amb elements de practica i persegueix una transferència efectiva de coneixement entre l'àmbit acadèmic i el sector privat. Ates que el soci del sector privat és una organització sense ànim de lucre, l’esforç per crear valor social ha prevalgut en l’ambició d’avançar en el desenvolupament d'un producte industrial específic o d'una tecnologia particula

    Opportunistic device-to-device communication in cellular networks: from theory to practice

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    Mención Internacional en el título de doctorCellular service providers have been struggling with users’ demand since the emergence of mobile Internet. As a result, each generation of cellular network prevailed over its predecessors mainly in terms of connection speed. However, the fifth generation (5G) of cellular network promises to go beyond this trend by revolutionizing the network architecture. Device-to-Device (D2D) communication is one of the revolutionary changes that enables mobile users to communicate directly without traversing a base station. This feature is being actively studied in 3GPP with special focus on public safety as it allows mobiles to operate in adhoc mode. Although under the (partial) control of the network, D2D communications open the door to many other use-cases. This dissertation studies different aspects of D2D communications and its impact on the key performance indicators of the network. We design an architecture for the collaboration of cellular users by means of timely exploited D2D opportunities. We begin by presenting the analytical study on opportunistic outband D2D communications. The study reveals the great potential of opportunistic outband D2D communications for enhancing energy efficiency, fairness, and capacity of cellular networks when groups of D2D users can be form and managed in the cellular network. Then we introduce a protocol that is compatible with the latest release of IEEE and 3GPP standards and allows for implementation of our proposal in a today’s cellular network. To validate our analytical findings, we use our experimental Software Defined Radio (SDR)-based testbed to further study our proposal in a real world scenario. The experimental results confirm the outstanding potential of opportunistic outband D2D communications. Finally, we investigate the performance merits and disadvantages of different D2D “modes”. Our investigation reveals, despite the common belief, that all D2D modes are complementary and their merits are scenario based.This work has been supported by IMDEA Networks Institute.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Douglas Leith.- Secretario: Albert Banchs Roca.- Vocal: Carla Fabiana Chiasserin

    A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

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    The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

    Facilitating Cooperative Truck Platooning for Energy Savings: Path Planning, Platoon Formation and Benefit Redistribution

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    Enabled by the connected and automated vehicle (CAV) technology, cooperative truck platooning that offers promising energy savings is likely to be implemented soon. However, as the trucking industry operates in a highly granular manner so that the trucks usually vary in their operation schedules, vehicle types and configurations, it is inevitable that 1) the spontaneous platooning over a spatial network is rare, 2) the total fuel savings vary from platoon to platoon, and 3) the benefit achieved within a platoon differs from position to position, e.g., the lead vehicle always achieves the least fuel-saving. Consequently, trucks from different owners may not have the opportunities to platoon with others if no path coordination is performed. Even if they happen to do so, they may tend to change positions in the formed platoons to achieve greater benefits, yielding behaviorally unstable platoons with less energy savings and more disruptions to traffic flows. This thesis proposes a hierarchical modeling framework to explicate the necessitated strategies that facilitate cooperative truck platooning. An empirical study is first conducted to scrutinize the energy-saving potentials of the U.S. national freight network. By comparing the performance under scheduled platooning and ad-hoc platooning, the author shows that the platooning opportunities can be greatly improved by careful path planning, thereby yielding substantial energy savings. For trucks assembled on the same path and can to platoon together, the second part of the thesis investigates the optimal platoon formation that maximizes total platooning utility and benefits redistribution mechanisms that address the behavioral instability issue. Both centralized and decentralized approaches are proposed. In particular, the decentralized approach employs a dynamic process where individual trucks or formed platoons are assumed to act as rational agents. The agents decide whether to form a larger, better platoon considering their own utilities under the pre-defined benefit reallocation mechanisms. Depending on whether the trucks are single-brand or multi-brand, whether there is a complete information setting or incomplete information setting, three mechanisms, auction, bilateral trade model, and one-sided matching are proposed. The centralized approach yields a near-optimal solution for the whole system and is more computationally efficient than conventional algorithms. The decentralized approach is stable, more flexible, and computational efficient while maintaining acceptable degrees of optimality. The mechanisms proposed can apply to not only under the truck platooning scenario but also other forms of shared mobility.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163047/1/xtsun_1.pd
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