6,252 research outputs found
A Game-Theoretic Approach to Multi-Objective Resource Sharing and Allocation in Mobile Edge Clouds
Mobile edge computing seeks to provide resources to different delay-sensitive
applications. However, allocating the limited edge resources to a number of
applications is a challenging problem. To alleviate the resource scarcity
problem, we propose sharing of resources among multiple edge computing service
providers where each service provider has a particular utility to optimize. We
model the resource allocation and sharing problem as a multi-objective
optimization problem and present a \emph{Cooperative Game Theory} (CGT) based
framework, where each edge service provider first satisfies its native
applications and then shares its remaining resources (if available) with users
of other providers. Furthermore, we propose an algorithm that
provides allocation decisions from the \emph{core}, hence the obtained
allocations are \emph{Pareto} optimal and the grand coalition of all the
service providers is stable. Experimental results show that our proposed
resource allocation and sharing framework improves the utility of all the
service providers compared with the case where the service providers are
working alone (no resource sharing). Our algorithm reduces the
time complexity of obtaining a solution from the core by as much as 71.67\%
when compared with the \emph{Shapley value}.Comment: The paper has been accepted for publication in ACM Mobicom workshop
"Technologies for the Wireless Edge" 201
A Comprehensive Survey of Potential Game Approaches to Wireless Networks
Potential games form a class of non-cooperative games where unilateral
improvement dynamics are guaranteed to converge in many practical cases. The
potential game approach has been applied to a wide range of wireless network
problems, particularly to a variety of channel assignment problems. In this
paper, the properties of potential games are introduced, and games in wireless
networks that have been proven to be potential games are comprehensively
discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on
Communications, vol. E98-B, no. 9, Sept. 201
Optimal association of mobile users to multi-access edge computing resources
Multi-access edge computing (MEC) plays a key role in fifth-generation (5G) networks in bringing cloud functionalities at the edge of the radio access network, in close proximity to mobile users. In this paper we focus on mobile-edge computation offloading, a way to transfer heavy demanding, and latency-critical applications from mobile handsets to close-located MEC servers, in order to reduce latency and/or energy consumption. Our goal is to provide an optimal strategy to associate mobile users to access points (AP) and MEC hosts, while contextually optimizing the allocation of radio and computational resources to each user, with the objective of minimizing the overall user transmit power under latency constraints incorporating both communication and computation times. The overall problem is a mixed-binary problem. To overcome its inherent computational complexity, we propose two alternative strategies: i) a method based on successive convex approximation (SCA) techniques, proven to converge to local optimal solutions; ii) an approach hinging on matching theory, based on formulating the assignment problem as a matching game
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
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