521 research outputs found
CSMA Local Area Networking under Dynamic Altruism
In this paper, we consider medium access control of local area networks
(LANs) under limited-information conditions as befits a distributed system.
Rather than assuming "by rule" conformance to a protocol designed to regulate
packet-flow rates (e.g., CSMA windowing), we begin with a non-cooperative game
framework and build a dynamic altruism term into the net utility. The effects
of altruism are analyzed at Nash equilibrium for both the ALOHA and CSMA
frameworks in the quasistationary (fictitious play) regime. We consider either
power or throughput based costs of networking, and the cases of identical or
heterogeneous (independent) users/players. In a numerical study we consider
diverse players, and we see that the effects of altruism for similar players
can be beneficial in the presence of significant congestion, but excessive
altruism may lead to underuse of the channel when demand is low
Bounding the Inefficiency of Altruism Through Social Contribution Games
We introduce a new class of games, called social contribution games (SCGs),
where each player's individual cost is equal to the cost he induces on society
because of his presence. Our results reveal that SCGs constitute useful
abstractions of altruistic games when it comes to the analysis of the robust
price of anarchy. We first show that SCGs are altruism-independently smooth,
i.e., the robust price of anarchy of these games remains the same under
arbitrary altruistic extensions. We then devise a general reduction technique
that enables us to reduce the problem of establishing smoothness for an
altruistic extension of a base game to a corresponding SCG. Our reduction
applies whenever the base game relates to a canonical SCG by satisfying a
simple social contribution boundedness property. As it turns out, several
well-known games satisfy this property and are thus amenable to our reduction
technique. Examples include min-sum scheduling games, congestion games, second
price auctions and valid utility games. Using our technique, we derive mostly
tight bounds on the robust price of anarchy of their altruistic extensions. For
the majority of the mentioned game classes, the results extend to the more
differentiated friendship setting. As we show, our reduction technique covers
this model if the base game satisfies three additional natural properties
Collocation Games and Their Application to Distributed Resource Management
We introduce Collocation Games as the basis of a general framework for modeling, analyzing, and facilitating the interactions between the various stakeholders in distributed systems in general, and in cloud computing environments in particular. Cloud computing enables fixed-capacity (processing, communication, and storage) resources to be offered by infrastructure providers as commodities for sale at a fixed cost in an open marketplace to independent, rational parties (players) interested in setting up their own applications over the Internet. Virtualization technologies enable the partitioning of such fixed-capacity resources so as to allow each player to dynamically acquire appropriate fractions of the resources for unencumbered use. In such a paradigm, the resource management problem reduces to that of partitioning the entire set of applications (players) into subsets, each of which is assigned to fixed-capacity cloud resources. If the infrastructure and the various applications are under a single administrative domain, this partitioning reduces to an optimization problem whose objective is to minimize the overall deployment cost. In a marketplace, in which the infrastructure provider is interested in maximizing its own profit, and in which each player is interested in minimizing its own cost, it should be evident that a global optimization is precisely the wrong framework. Rather, in this paper we use a game-theoretic framework in which the assignment of players to fixed-capacity resources is the outcome of a strategic "Collocation Game". Although we show that determining the existence of an equilibrium for collocation games in general is NP-hard, we present a number of simplified, practically-motivated variants of the collocation game for which we establish convergence to a Nash Equilibrium, and for which we derive convergence and price of anarchy bounds. In addition to these analytical results, we present an experimental evaluation of implementations of some of these variants for cloud infrastructures consisting of a collection of multidimensional resources of homogeneous or heterogeneous capacities. Experimental results using trace-driven simulations and synthetically generated datasets corroborate our analytical results and also illustrate how collocation games offer a feasible distributed resource management alternative for autonomic/self-organizing systems, in which the adoption of a global optimization approach (centralized or distributed) would be neither practical nor justifiable.NSF (CCF-0820138, CSR-0720604, EFRI-0735974, CNS-0524477, CNS-052016, CCR-0635102); Universidad Pontificia Bolivariana; COLCIENCIAS–Instituto Colombiano para el Desarrollo de la Ciencia y la TecnologĂa "Francisco JosĂ© de Caldas
Tight Inefficiency Bounds for Perception-Parameterized Affine Congestion Games
Congestion games constitute an important class of non-cooperative games which
was introduced by Rosenthal in 1973. In recent years, several extensions of
these games were proposed to incorporate aspects that are not captured by the
standard model. Examples of such extensions include the incorporation of risk
sensitive players, the modeling of altruistic player behavior and the
imposition of taxes on the resources. These extensions were studied intensively
with the goal to obtain a precise understanding of the inefficiency of
equilibria of these games. In this paper, we introduce a new model of
congestion games that captures these extensions (and additional ones) in a
unifying way. The key idea here is to parameterize both the perceived cost of
each player and the social cost function of the system designer. Intuitively,
each player perceives the load induced by the other players by an extent of
{\rho}, while the system designer estimates that each player perceives the load
of all others by an extent of {\sigma}. The above mentioned extensions reduce
to special cases of our model by choosing the parameters {\rho} and {\sigma}
accordingly. As in most related works, we concentrate on congestion games with
affine latency functions here. Despite the fact that we deal with a more
general class of congestion games, we manage to derive tight bounds on the
price of anarchy and the price of stability for a large range of pa- rameters.
Our bounds provide a complete picture of the inefficiency of equilibria for
these perception-parameterized congestion games. As a result, we obtain tight
bounds on the price of anarchy and the price of stability for the above
mentioned extensions. Our results also reveal how one should "design" the cost
functions of the players in order to reduce the price of anar- chy
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Smart Traffic Operation: from Human-Driven Cars to Mixed Vehicle Autonomy
The goal of my research is to enhance urban mobility by developing reliable and efficient traffic control and management strategies. As cities grow everywhere, and urban roadways become overburdened, the need for the development of such strategies becomes more evident. With the prevalence of smart sensing devices, such as smart phones and smart intersections, cities are becoming smart. Moreover, with the emergence of new and inevitable technologies, such as autonomous and connected vehicles, electric vehicles, and mobility on demand systems, smart cities are rapidly evolving. As we experience the arrival of such technologies, there is an opportunity to reclaim urban mobility. However, a blind utilization of these technologies may deflect us from reaching this goal. In this dissertation, we study the efficient operation of smart cities via management strategies that can guarantee overall societal benefits both in the cities of today and future.We focus on two natural instances of this agenda. In the first part, we tackle some of the existing challenges in the smart operation of traffic networks which are solely shared by human-driven cars. If all vehicles are human-driven, there is room for improving the efficiency of traffic networks by appropriate coordination and control of traffic signal lights. For these networks, we develop signal control algorithms that are capable of minimizing the number of stop-and-go movements, encoding fairness among vehicular arrivals, and are robust to the knowledge of system parameters. In the second part, we analyze fundamentals of traffic networks with mixed vehicle autonomy, where both human-driven and autonomous cars coexist on roadways. We study the mobility implications of selfish autonomy, i.e. autonomous cars that are not concerned about their overall impact and simply attempt to optimize their own travel benefits. Having shown the negative consequences that the increased deployment of selfish autonomy may have, we develop a pricing mechanism which can guarantee the overall societal-scale efficiency of traffic networks with mixed vehicle autonomy. Finally, we show how autonomy can act altruistically, i.e. by taking into account the decision making process of humans, autonomous cars can potentially plan for their actions in the favor of the overall good
Achieving reliability and fairness in online task computing environments
MenciĂłn Internacional en el tĂtulo de doctorWe consider online task computing environments such as volunteer computing platforms running
on BOINC (e.g., SETI@home) and crowdsourcing platforms such as Amazon Mechanical
Turk. We model the computations as an Internet-based task computing system under the masterworker
paradigm. A master entity sends tasks across the Internet, to worker entities willing to
perform a computational task. Workers execute the tasks, and report back the results, completing
the computational round. Unfortunately, workers are untrustworthy and might report an incorrect
result. Thus, the first research question we answer in this work is how to design a reliable masterworker
task computing system. We capture the workers’ behavior through two realistic models:
(1) the “error probability model” which assumes the presence of altruistic workers willing to
provide correct results and the presence of troll workers aiming at providing random incorrect
results. Both types of workers suffer from an error probability altering their intended response.
(2) The “rationality model” which assumes the presence of altruistic workers, always reporting
a correct result, the presence of malicious workers always reporting an incorrect result, and the
presence of rational workers following a strategy that will maximize their utility (benefit). The
rational workers can choose among two strategies: either be honest and report a correct result,
or cheat and report an incorrect result. Our two modeling assumptions on the workers’ behavior
are supported by an experimental evaluation we have performed on Amazon Mechanical Turk.
Given the error probability model, we evaluate two reliability techniques: (1) “voting” and (2)
“auditing” in terms of task assignments required and time invested for computing correctly a set
of tasks with high probability. Considering the rationality model, we take an evolutionary game
theoretic approach and we design mechanisms that eventually achieve a reliable computational
platform where the master receives the correct task result with probability one and with minimal
auditing cost. The designed mechanisms provide incentives to the rational workers, reinforcing
their strategy to a correct behavior, while they are complemented by four reputation schemes that
cope with malice. Finally, we also design a mechanism that deals with unresponsive workers by
keeping a reputation related to the workers’ response rate. The designed mechanism selects the
most reliable and active workers in each computational round. Simulations, among other, depict
the trade-off between the master’s cost and the time the system needs to reach a state where
the master always receives the correct task result. The second research question we answer in
this work concerns the fair and efficient distribution of workers among the masters over multiple computational rounds. Masters with similar tasks are competing for the same set of workers at
each computational round. Workers must be assigned to the masters in a fair manner; when the
master values a worker’s contribution the most. We consider that a master might have a strategic
behavior, declaring a dishonest valuation on a worker in each round, in an attempt to increase its
benefit. This strategic behavior from the side of the masters might lead to unfair and inefficient assignments
of workers. Applying renown auction mechanisms to solve the problem at hand can be
infeasible since monetary payments are required on the side of the masters. Hence, we present an
alternative mechanism for fair and efficient distribution of the workers in the presence of strategic
masters, without the use of monetary incentives. We show analytically that our designed mechanism
guarantees fairness, is socially efficient, and is truthful. Simulations favourably compare
our designed mechanism with two benchmark auction mechanisms.This work has been supported by IMDEA Networks Institute and the Spanish Ministry of Education grant FPU2013-03792.Programa Oficial de Doctorado en IngenierĂa MatemáticaPresidente: Alberto Tarable.- Secretario: JosĂ© Antonio Cuesta Ruiz.- Vocal: Juan Julián Merelo GuervĂł
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