1,580 research outputs found
A Game-theoretic Framework for Revenue Sharing in Edge-Cloud Computing System
We introduce a game-theoretic framework to ex- plore revenue sharing in an
Edge-Cloud computing system, in which computing service providers at the edge
of the Internet (edge providers) and computing service providers at the cloud
(cloud providers) co-exist and collectively provide computing resources to
clients (e.g., end users or applications) at the edge. Different from
traditional cloud computing, the providers in an Edge-Cloud system are
independent and self-interested. To achieve high system-level efficiency, the
manager of the system adopts a task distribution mechanism to maximize the
total revenue received from clients and also adopts a revenue sharing mechanism
to split the received revenue among computing servers (and hence service
providers). Under those system-level mechanisms, service providers attempt to
game with the system in order to maximize their own utilities, by strategically
allocating their resources (e.g., computing servers).
Our framework models the competition among the providers in an Edge-Cloud
system as a non-cooperative game. Our simulations and experiments on an
emulation system have shown the existence of Nash equilibrium in such a game.
We find that revenue sharing mechanisms have a significant impact on the
system-level efficiency at Nash equilibria, and surprisingly the revenue
sharing mechanism based directly on actual contributions can result in
significantly worse system efficiency than Shapley value sharing mechanism and
Ortmann proportional sharing mechanism. Our framework provides an effective
economics approach to understanding and designing efficient Edge-Cloud
computing systems
Recommended from our members
Using EPECs to model bilevel games in restructured electricity markets with locational prices
CWPE0619 (EPRG0602) Xinmin Hu and Daniel Ralph (Feb 2006) Using EPECs to model bilevel games in restructured electricity markets with locational prices We study a bilevel noncooperative game-theoretic model of electricity markets with locational marginal prices. Each player faces a bilevel optimization problem that we remodel as a mathematical program with equilibrium constraints, MPEC. This gives an EPEC, equilibrium problem with equilibrium constraints. We establish sufficient conditions for existence of pure strategy Nash equilibria for this class of bilevel games and give some applications. We show by examples the effect of network transmission limits, i.e. congestion, on existence of equilibria. Then we study, for more general EPECs, the weaker pure strategy concepts of local Nash and Nash stationary equilibria. We model the latter via complementarity problems, CPs. Finally, we present numerical examples of methods that attempt to find local Nash or Nash stationary equilibria of randomly generated electricity market games. The CP solver PATH is found to be rather effective in this context
Applications of Repeated Games in Wireless Networks: A Survey
A repeated game is an effective tool to model interactions and conflicts for
players aiming to achieve their objectives in a long-term basis. Contrary to
static noncooperative games that model an interaction among players in only one
period, in repeated games, interactions of players repeat for multiple periods;
and thus the players become aware of other players' past behaviors and their
future benefits, and will adapt their behavior accordingly. In wireless
networks, conflicts among wireless nodes can lead to selfish behaviors,
resulting in poor network performances and detrimental individual payoffs. In
this paper, we survey the applications of repeated games in different wireless
networks. The main goal is to demonstrate the use of repeated games to
encourage wireless nodes to cooperate, thereby improving network performances
and avoiding network disruption due to selfish behaviors. Furthermore, various
problems in wireless networks and variations of repeated game models together
with the corresponding solutions are discussed in this survey. Finally, we
outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference
Equilibrium Computation in Resource Allocation Games
We study the equilibrium computation problem for two classical resource
allocation games: atomic splittable congestion games and multimarket Cournot
oligopolies. For atomic splittable congestion games with singleton strategies
and player-specific affine cost functions, we devise the first polynomial time
algorithm computing a pure Nash equilibrium. Our algorithm is combinatorial and
computes the exact equilibrium assuming rational input. The idea is to compute
an equilibrium for an associated integrally-splittable singleton congestion
game in which the players can only split their demands in integral multiples of
a common packet size. While integral games have been considered in the
literature before, no polynomial time algorithm computing an equilibrium was
known. Also for this class, we devise the first polynomial time algorithm and
use it as a building block for our main algorithm.
We then develop a polynomial time computable transformation mapping a
multimarket Cournot competition game with firm-specific affine price functions
and quadratic costs to an associated atomic splittable congestion game as
described above. The transformation preserves equilibria in either games and,
thus, leads -- via our first algorithm -- to a polynomial time algorithm
computing Cournot equilibria. Finally, our analysis for integrally-splittable
games implies new bounds on the difference between real and integral Cournot
equilibria. The bounds can be seen as a generalization of the recent bounds for
single market oligopolies obtained by Todd [2016].Comment: This version contains some typo corrections onl
Evolutionary Poisson Games for Controlling Large Population Behaviors
Emerging applications in engineering such as crowd-sourcing and
(mis)information propagation involve a large population of heterogeneous users
or agents in a complex network who strategically make dynamic decisions. In
this work, we establish an evolutionary Poisson game framework to capture the
random, dynamic and heterogeneous interactions of agents in a holistic fashion,
and design mechanisms to control their behaviors to achieve a system-wide
objective. We use the antivirus protection challenge in cyber security to
motivate the framework, where each user in the network can choose whether or
not to adopt the software. We introduce the notion of evolutionary Poisson
stable equilibrium for the game, and show its existence and uniqueness. Online
algorithms are developed using the techniques of stochastic approximation
coupled with the population dynamics, and they are shown to converge to the
optimal solution of the controller problem. Numerical examples are used to
illustrate and corroborate our results
Price of Competition and Dueling Games
We study competition in a general framework introduced by Immorlica et al.
and answer their main open question. Immorlica et al. considered classic
optimization problems in terms of competition and introduced a general class of
games called dueling games. They model this competition as a zero-sum game,
where two players are competing for a user's satisfaction. In their main and
most natural game, the ranking duel, a user requests a webpage by submitting a
query and players output an ordering over all possible webpages based on the
submitted query. The user tends to choose the ordering which displays her
requested webpage in a higher rank. The goal of both players is to maximize the
probability that her ordering beats that of her opponent and gets the user's
attention. Immorlica et al. show this game directs both players to provide
suboptimal search results. However, they leave the following as their main open
question: "does competition between algorithms improve or degrade expected
performance?" In this paper, we resolve this question for the ranking duel and
a more general class of dueling games.
More precisely, we study the quality of orderings in a competition between
two players. This game is a zero-sum game, and thus any Nash equilibrium of the
game can be described by minimax strategies. Let the value of the user for an
ordering be a function of the position of her requested item in the
corresponding ordering, and the social welfare for an ordering be the expected
value of the corresponding ordering for the user. We propose the price of
competition which is the ratio of the social welfare for the worst minimax
strategy to the social welfare obtained by a social planner. We use this
criterion for analyzing the quality of orderings in the ranking duel. We prove
the quality of minimax results is surprisingly close to that of the optimum
solution
Strategic Network Interdiction
We develop a strategic model of network interdiction in a non-cooperative game of flow. An adversary, endowed with a bounded quantity of bads, chooses a flow specifying a plan for carrying bads through a network from a base to a target. Simultaneously, an agency chooses a blockage specifying a plan for blocking the transport of bads through arcs in the network. The bads carried to the target cause a target loss while the blocked arcs cause a network loss. The adversary earns and the agency loses from both target loss and network loss. The adversary incurs the expense of carrying bads. In this model we study Nash equilibria and find a power law relation between the probability and the extent of the target loss. Our model contributes to the literature of game theory by introducing non-cooperative behavior into a Kalai-Zemel (cooperative) game of flow. Our research also advances models and results on network interdiction.Network Interdiction, Noncooperative Game of Flow, Nash Equilibrium, Power Law, Kalai-Zemel Game of Flow
- …