140 research outputs found
Selfish Computation Offloading for Mobile Cloud Computing in Dense Wireless Networks
Offloading computation to a mobile cloud is a promising solution to augment
the computation capabilities of mobile devices. In this paper we consider
selfish mobile devices in a dense wireless network, in which individual mobile
devices can offload computations via multiple access points (APs) to a mobile
cloud so as to minimize their computation costs, and we provide a game
theoretical analysis of the problem. We show that in the case of an elastic
cloud, all improvement paths are finite, and thus a pure strategy Nash
equilibrium exists and can be computed easily. In the case of a non-elastic
cloud we show that improvement paths may cycle, yet we show that a pure Nash
equilibrium exists and we provide an efficient algorithm for computing one.
Furthermore, we provide an upper bound on the price of anarchy (PoA) of the
game. We use simulations to evaluate the time complexity of computing Nash
equilibria and to provide insights into the PoA under realistic scenarios. Our
results show that the equilibrium cost may be close to optimal, and the cost
difference is due to too many mobile users offloading simultaneously.Comment: 10 pages, 6 figure
Game Theoretic Approaches in Vehicular Networks: A Survey
In the era of the Internet of Things (IoT), vehicles and other intelligent
components in Intelligent Transportation System (ITS) are connected, forming
the Vehicular Networks (VNs) that provide efficient and secure traffic,
ubiquitous access to information, and various applications. However, as the
number of connected nodes keeps increasing, it is challenging to satisfy
various and large amounts of service requests with different Quality of Service
(QoS ) and security requirements in the highly dynamic VNs. Intelligent nodes
in VNs can compete or cooperate for limited network resources so that either an
individual or group objectives can be achieved. Game theory, a theoretical
framework designed for strategic interactions among rational decision-makers
who faced with scarce resources, can be used to model and analyze individual or
group behaviors of communication entities in VNs. This paper primarily surveys
the recent advantages of GT used in solving various challenges in VNs. As VNs
and GT have been extensively investigate34d, this survey starts with a brief
introduction of the basic concept and classification of GT used in VNs. Then, a
comprehensive review of applications of GT in VNs is presented, which primarily
covers the aspects of QoS and security. Moreover, with the development of
fifth-generation (5G) wireless communication, recent contributions of GT to
diverse emerging technologies of 5G integrated into VNs are surveyed in this
paper. Finally, several key research challenges and possible solutions for
applying GT in VNs are outlined
Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systems
In mobile edge computing systems, an edge node may have a high load when a
large number of mobile devices offload their tasks to it. Those offloaded tasks
may experience large processing delay or even be dropped when their deadlines
expire. Due to the uncertain load dynamics at the edge nodes, it is challenging
for each device to determine its offloading decision (i.e., whether to offload
or not, and which edge node it should offload its task to) in a decentralized
manner. In this work, we consider non-divisible and delay-sensitive tasks as
well as edge load dynamics, and formulate a task offloading problem to minimize
the expected long-term cost. We propose a model-free deep reinforcement
learning-based distributed algorithm, where each device can determine its
offloading decision without knowing the task models and offloading decision of
other devices. To improve the estimation of the long-term cost in the
algorithm, we incorporate the long short-term memory (LSTM), dueling deep
Q-network (DQN), and double-DQN techniques. Simulation results with 50 mobile
devices and five edge nodes show that the proposed algorithm can reduce the
ratio of dropped tasks and average task delay by 86.4%-95.4% and 18.0%-30.1%,
respectively, when compared with several existing algorithms
Applications of Game Theory in Vehicular Networks: A Survey
In the Internet of Things (IoT) era, vehicles and other intelligent
components in an intelligent transportation system (ITS) are connected, forming
Vehicular Networks (VNs) that provide efficient and secure traffic and
ubiquitous access to various applications. However, as the number of nodes in
ITS increases, it is challenging to satisfy a varied and large number of
service requests with different Quality of Service and security requirements in
highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for
limited network resources to achieve either an individual or a group's
objectives. Game Theory (GT), a theoretical framework designed for strategic
interactions among rational decision-makers sharing scarce resources, can be
used to model and analyze individual or group behaviors of communicating
entities in VNs. This paper primarily surveys the recent developments of GT in
solving various challenges of VNs. This survey starts with an introduction to
the background of VNs. A review of GT models studied in the VNs is then
introduced, including its basic concepts, classifications, and applicable
vehicular issues. After discussing the requirements of VNs and the motivation
of using GT, a comprehensive literature review on GT applications in dealing
with the challenges of current VNs is provided. Furthermore, recent
contributions of GT to VNs integrating with diverse emerging 5G technologies
are surveyed. Finally, the lessons learned are given, and several key research
challenges and possible solutions for applying GT in VNs are outlined.Comment: It has been submitted to "IEEE communication surveys and
tutorials".This is the revised versio
Mobile Edge Computation Offloading Using Game Theory and Reinforcement Learning
Due to the ever-increasing popularity of resource-hungry and
delay-constrained mobile applications, the computation and storage capabilities
of remote cloud has partially migrated towards the mobile edge, giving rise to
the concept known as Mobile Edge Computing (MEC). While MEC servers enjoy the
close proximity to the end-users to provide services at reduced latency and
lower energy costs, they suffer from limitations in computational and radio
resources, which calls for fair efficient resource management in the MEC
servers. The problem is however challenging due to the ultra-high density,
distributed nature, and intrinsic randomness of next generation wireless
networks. In this article, we focus on the application of game theory and
reinforcement learning for efficient distributed resource management in MEC, in
particular, for computation offloading. We briefly review the cutting-edge
research and discuss future challenges. Furthermore, we develop a
game-theoretical model for energy-efficient distributed edge server activation
and study several learning techniques. Numerical results are provided to
illustrate the performance of these distributed learning techniques. Also, open
research issues in the context of resource management in MEC servers are
discussed
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning in communications and networking. Modern networks, e.g.,
Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become
more decentralized and autonomous. In such networks, network entities need to
make decisions locally to maximize the network performance under uncertainty of
network environment. Reinforcement learning has been efficiently used to enable
the network entities to obtain the optimal policy including, e.g., decisions or
actions, given their states when the state and action spaces are small.
However, in complex and large-scale networks, the state and action spaces are
usually large, and the reinforcement learning may not be able to find the
optimal policy in reasonable time. Therefore, deep reinforcement learning, a
combination of reinforcement learning with deep learning, has been developed to
overcome the shortcomings. In this survey, we first give a tutorial of deep
reinforcement learning from fundamental concepts to advanced models. Then, we
review deep reinforcement learning approaches proposed to address emerging
issues in communications and networking. The issues include dynamic network
access, data rate control, wireless caching, data offloading, network security,
and connectivity preservation which are all important to next generation
networks such as 5G and beyond. Furthermore, we present applications of deep
reinforcement learning for traffic routing, resource sharing, and data
collection. Finally, we highlight important challenges, open issues, and future
research directions of applying deep reinforcement learning.Comment: 37 pages, 13 figures, 6 tables, 174 reference paper
Joint Wireless and Edge Computing Resource Management with Dynamic Network Slice Selection
Network slicing is a promising approach for enabling low latency computation
offloading in edge computing systems. In this paper, we consider an edge
computing system under network slicing in which the wireless devices generate
latency sensitive computational tasks. We address the problem of joint dynamic
assignment of computational tasks to slices, management of radio resources
across slices and management of radio and computing resources within slices. We
formulate the Joint Slice Selection and Edge Resource Management(JSS-ERM)
problem as a mixed-integer problem with the objective to minimize the
completion time of computational tasks. We show that the JSS-ERM problem is
NP-hard and develop an approximation algorithm with bounded approximation ratio
based on a game theoretic treatment of the problem. We provide extensive
simulation results to show that network slicing can improve the system
performance compared to no slicing and that the proposed solution can achieve
significant gains compared to the equal slicing policy. Our results also show
that the computational complexity of the proposed algorithm is approximately
linear in the number of devices.Comment: 12 pages, 7 figure
Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks
Mobile-Edge Computing (MEC) is an emerging paradigm that provides a capillary
distribution of cloud computing capabilities to the edge of the wireless access
network, enabling rich services and applications in close proximity to the end
users. In this article, a MEC enabled multi-cell wireless network is considered
where each Base Station (BS) is equipped with a MEC server that can assist
mobile users in executing computation-intensive tasks via task offloading. The
problem of Joint Task Offloading and Resource Allocation (JTORA) is studied in
order to maximize the users' task offloading gains, which is measured by the
reduction in task completion time and energy consumption. The considered
problem is formulated as a Mixed Integer Non-linear Program (MINLP) that
involves jointly optimizing the task offloading decision, uplink transmission
power of mobile users, and computing resource allocation at the MEC servers.
Due to the NP-hardness of this problem, solving for optimal solution is
difficult and impractical for a large-scale network. To overcome this drawback,
our approach is to decompose the original problem into (i) a Resource
Allocation (RA) problem with fixed task offloading decision and (ii) a Task
Offloading (TO) problem that optimizes the optimal-value function corresponding
to the RA problem. We address the RA problem using convex and quasi-convex
optimization techniques, and propose a novel heuristic algorithm to the TO
problem that achieves a suboptimal solution in polynomial time. Numerical
simulation results show that our algorithm performs closely to the optimal
solution and that it significantly improves the users' offloading utility over
traditional approaches
An Efficient Mechanism for Computation Offloading in Mobile-Edge Computing
Mobile edge computing (MEC) is a promising technology that provides cloud and
IT services within the proximity of the mobile user. With the increasing number
of mobile applications, mobile devices (MD) encounter limitations of their
resources, such as battery life and computation capacity. The computation
offloading in MEC can help mobile users to reduce battery usage and speed up
task execution. Although there are many solutions for offloading in MEC, most
usually only employ one MEC server for improving mobile device energy
consumption and execution time. Instead of conventional centralized
optimization methods, the current paper considers a decentralized optimization
mechanism between MEC servers and users. In particular, an assignment mechanism
called school choice is employed to assist heterogeneous users to select
different MEC operators in a distributed environment. With this mechanism, each
user can benefit from minimizing the price and energy consumption of executing
tasks while also meeting the specified deadline. The present research has
designed an efficient mechanism for a computation offloading scheme that
achieves minimal price and energy consumption under latency constraints.
Numerical results demonstrate that the proposed algorithm can attain efficient
and successful computation offloading.Comment: 36 page
Applications of Economic and Pricing Models for Resource Management in 5G Wireless Networks: A Survey
This paper presents a comprehensive literature review on applications of
economic and pricing theory for resource management in the evolving fifth
generation (5G) wireless networks. The 5G wireless networks are envisioned to
overcome existing limitations of cellular networks in terms of data rate,
capacity, latency, energy efficiency, spectrum efficiency, coverage,
reliability, and cost per information transfer. To achieve the goals, the 5G
systems will adopt emerging technologies such as massive Multiple-Input
Multiple-Output (MIMO), mmWave communications, and dense Heterogeneous Networks
(HetNets). However, 5G involves multiple entities and stakeholders that may
have different objectives, e.g., high data rate, low latency, utility
maximization, and revenue/profit maximization. This poses a number of
challenges to resource management designs of 5G. While the traditional
solutions may neither efficient nor applicable, economic and pricing models
have been recently developed and adopted as useful tools to achieve the
objectives. In this paper, we review economic and pricing approaches proposed
to address resource management issues in the 5G wireless networks including
user association, spectrum allocation, and interference and power management.
Furthermore, we present applications of economic and pricing models for
wireless caching and mobile data offloading. Finally, we highlight important
challenges, open issues and future research directions of applying economic and
pricing models to the 5G wireless networks
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