798 research outputs found
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
A Parallel Optimal Task Allocation Mechanism for Large-Scale Mobile Edge Computing
We consider the problem of intelligent and efficient task allocation
mechanism in large-scale mobile edge computing (MEC), which can reduce delay
and energy consumption in a parallel and distributed optimization. In this
paper, we study the joint optimization model to consider cooperative task
management mechanism among mobile terminals (MT), macro cell base station
(MBS), and multiple small cell base station (SBS) for large-scale MEC
applications. We propose a parallel multi-block Alternating Direction Method of
Multipliers (ADMM) based method to model both requirements of low delay and low
energy consumption in the MEC system which formulates the task allocation under
those requirements as a nonlinear 0-1 integer programming problem. To solve the
optimization problem, we develop an efficient combination of conjugate
gradient, Newton and linear search techniques based algorithm with Logarithmic
Smoothing (for global variables updating) and the Cyclic Block coordinate
Gradient Projection (CBGP, for local variables updating) methods, which can
guarantee convergence and reduce computational complexity with a good
scalability. Numerical results demonstrate the effectiveness of the proposed
mechanism and it can effectively reduce delay and energy consumption for a
large-scale MEC system.Comment: 15 pages,4 figures, resource management for large-scale MEC. arXiv
admin note: text overlap with arXiv:2003.1284
Joint Offloading and Resource Allocation in Vehicular Edge Computing and Networks
The emergence of computation intensive on-vehicle applications poses a
significant challenge to provide the required computation capacity and maintain
high performance. Vehicular Edge Computing (VEC) is a new computing paradigm
with a high potential to improve vehicular services by offloading
computation-intensive tasks to the VEC servers. Nevertheless, as the
computation resource of each VEC server is limited, offloading may not be
efficient if all vehicles select the same VEC server to offload their tasks. To
address this problem, in this paper, we propose offloading with resource
allocation. We incorporate the communication and computation to derive the task
processing delay. We formulate the problem as a system utility maximization
problem, and then develop a low-complexity algorithm to jointly optimize
offloading decision and resource allocation. Numerical results demonstrate the
superior performance of our Joint Optimization of Selection and Computation
(JOSC) algorithm compared to state of the art solutions
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
Computation Rate Maximization in UAV-Enabled Wireless Powered Mobile-Edge Computing Systems
Mobile edge computing (MEC) and wireless power transfer (WPT) are two
promising techniques to enhance the computation capability and to prolong the
operational time of low-power wireless devices that are ubiquitous in Internet
of Things. However, the computation performance and the harvested energy are
significantly impacted by the severe propagation loss. In order to address this
issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless powered system is
studied in this paper. The computation rate maximization problems in a
UAV-enabled MEC wireless powered system are investigated under both partial and
binary computation offloading modes, subject to the energy harvesting causal
constraint and the UAV's speed constraint. These problems are non-convex and
challenging to solve. A two-stage algorithm and a three-stage alternative
algorithm are respectively proposed for solving the formulated problems. The
closed-form expressions for the optimal central processing unit frequencies,
user offloading time, and user transmit power are derived. The optimal
selection scheme on whether users choose to locally compute or offload
computation tasks is proposed for the binary computation offloading mode.
Simulation results show that our proposed resource allocation schemes
outperforms other benchmark schemes. The results also demonstrate that the
proposed schemes converge fast and have low computational complexity.Comment: This paper has been accepted by IEEE JSA
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
As the explosive growth of smart devices and the advent of many new
applications, traffic volume has been growing exponentially. The traditional
centralized network architecture cannot accommodate such user demands due to
heavy burden on the backhaul links and long latency. Therefore, new
architectures which bring network functions and contents to the network edge
are proposed, i.e., mobile edge computing and caching. Mobile edge networks
provide cloud computing and caching capabilities at the edge of cellular
networks. In this survey, we make an exhaustive review on the state-of-the-art
research efforts on mobile edge networks. We first give an overview of mobile
edge networks including definition, architecture and advantages. Next, a
comprehensive survey of issues on computing, caching and communication
techniques at the network edge is presented respectively. The applications and
use cases of mobile edge networks are discussed. Subsequently, the key enablers
of mobile edge networks such as cloud technology, SDN/NFV and smart devices are
discussed. Finally, open research challenges and future directions are
presented as well
Energy-Efficient Joint Offloading and Wireless Resource Allocation Strategy in Multi-MEC Server Systems
Mobile edge computing (MEC) is an emerging paradigm that mobile devices can
offload the computation-intensive or latency-critical tasks to the nearby MEC
servers, so as to save energy and extend battery life. Unlike the cloud server,
MEC server is a small-scale data center deployed at a wireless access point,
thus it is highly sensitive to both radio and computing resource. In this
paper, we consider an Orthogonal Frequency-Division Multiplexing Access (OFDMA)
based multi-user and multi-MEC-server system, where the task offloading
strategies and wireless resources allocation are jointly investigated. Aiming
at minimizing the total energy consumption, we propose the joint offloading and
resource allocation strategy for latency-critical applications. Through the
bi-level optimization approach, the original NP-hard problem is decoupled into
the lower-level problem seeking for the allocation of power and subcarrier and
the upper-level task offloading problem. Simulation results show that the
proposed algorithm achieves excellent performance in energy saving and
successful offloading probability (SOP) in comparison with conventional
schemes.Comment: 6 pages, 5 figures, to appear in IEEE ICC 2018, May 20-2
Heterogeneous Services Provisioning in Small Cell Networks with Cache and Mobile Edge Computing
In the area of full duplex (FD)-enabled small cell networks, limited works
have been done on consideration of cache and mobile edge communication (MEC).
In this paper, a virtual FD-enabled small cell network with cache and MEC is
investigated for two heterogeneous services, high-data-rate service and
computation-sensitive service. In our proposed scheme, content caching and FD
communication are closely combined to offer high-data-rate services without the
cost of backhaul resource. Computing offloading is conducted to guarantee the
delay requirement of users. Then we formulate a virtual resource allocation
problem, in which user association, power control, caching and computing
offloading policies and resource allocation are jointly considered. Since the
original problem is a mixed combinatorial problem, necessary variables
relaxation and reformulation are conducted to transfer the original problem to
a convex problem. Furthermore, alternating direction method of multipliers
(ADMM) algorithm is adopted to obtain the optimal solution. Finally, extensive
simulations are conducted with different system configurations to verify the
effectiveness of the proposed scheme
Intelligent networking with Mobile Edge Computing: Vision and Challenges for Dynamic Network Scheduling
Mobile edge computing (MEC) has been considered as a promising technique for
internet of things (IoT). By deploying edge servers at the proximity of
devices, it is expected to provide services and process data at a relatively
low delay by intelligent networking. However, the vast edge servers may face
great challenges in terms of cooperation and resource allocation. Furthermore,
intelligent networking requires online implementation in distributed mode. In
such kinds of systems, the network scheduling can not follow any previously
known rule due to complicated application environment. Then statistical
learning rises up as a promising technique for network scheduling, where edges
dynamically learn environmental elements with cooperations. It is expected such
learning based methods may relieve deficiency of model limitations, which
enhance their practical use in dynamic network scheduling. In this paper, we
investigate the vision and challenges of the intelligent IoT networking with
mobile edge computing. From the systematic viewpoint, some major research
opportunities are enumerated with respect to statistical learning
Information-Centric Wireless Networks with Mobile Edge Computing
In order to better accommodate the dramatically increasing demand for data
caching and computing services, storage and computation capabilities should be
endowed to some of the intermediate nodes within the network. In this paper, we
design a novel virtualized heterogeneous networks framework aiming at enabling
content caching and computing. With the virtualization of the whole system, the
communication, computing and caching resources can be shared among all users
associated with different virtual service providers. We formulate the virtual
resource allocation strategy as a joint optimization problem, where the gains
of not only virtualization but also caching and computing are taken into
consideration in the proposed architecture. In addition, a distributed
algorithm based on alternating direction method of multipliers is adopted to
solve the formulated problem, in order to reduce the computational complexity
and signaling overhead. Finally, extensive simulations are presented to show
the effectiveness of the proposed scheme under different system parameters
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