3,777 research outputs found
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
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey
With the Internet of Things (IoT) becoming part of our daily life and our
environment, we expect rapid growth in the number of connected devices. IoT is
expected to connect billions of devices and humans to bring promising
advantages for us. With this growth, fog computing, along with its related edge
computing paradigms, such as multi-access edge computing (MEC) and cloudlet,
are seen as promising solutions for handling the large volume of
security-critical and time-sensitive data that is being produced by the IoT. In
this paper, we first provide a tutorial on fog computing and its related
computing paradigms, including their similarities and differences. Next, we
provide a taxonomy of research topics in fog computing, and through a
comprehensive survey, we summarize and categorize the efforts on fog computing
and its related computing paradigms. Finally, we provide challenges and future
directions for research in fog computing.Comment: 48 pages, 7 tables, 11 figures, 450 references. The data (categories
and features/objectives of the papers) of this survey are now available
publicly. Accepted by Elsevier Journal of Systems Architectur
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
Along with the rapid developments in communication technologies and the surge
in the use of mobile devices, a brand-new computation paradigm, Edge Computing,
is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications
are thriving with the breakthroughs in deep learning and the many improvements
in hardware architectures. Billions of data bytes, generated at the network
edge, put massive demands on data processing and structural optimization. Thus,
there exists a strong demand to integrate Edge Computing and AI, which gives
birth to Edge Intelligence. In this paper, we divide Edge Intelligence into AI
for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial
Intelligence on Edge). The former focuses on providing more optimal solutions
to key problems in Edge Computing with the help of popular and effective AI
technologies while the latter studies how to carry out the entire process of
building AI models, i.e., model training and inference, on the edge. This paper
provides insights into this new inter-disciplinary field from a broader
perspective. It discusses the core concepts and the research road-map, which
should provide the necessary background for potential future research
initiatives in Edge Intelligence.Comment: 13 pages, 3 figure
A Computation Offloading Incentive Mechanism with Delay and Cost Constraints under 5G Satellite-ground IoV architecture
The 5G Internet of Vehicles has become a new paradigm alongside the growing
popularity and variety of computation-intensive applications with high
requirements for computational resources and analysis capabilities. Existing
network architectures and resource management mechanisms may not sufficiently
guarantee satisfactory Quality of Experience and network efficiency, mainly
suffering from coverage limitation of Road Side Units, insufficient resources,
and unsatisfactory computational capabilities of onboard equipment, frequently
changing network topology, and ineffective resource management schemes. To meet
the demands of such applications, in this article, we first propose a novel
architecture by integrating the satellite network with 5G cloud-enabled
Internet of Vehicles to efficiently support seamless coverage and global
resource management. A incentive mechanism based joint optimization problem of
opportunistic computation offloading under delay and cost constraints is
established under the aforementioned framework, in which a vehicular user can
either significantly reduce the application completion time by offloading
workloads to several nearby vehicles through opportunistic vehicle-to-vehicle
channels while effectively controlling the cost or protect its own profit by
providing compensated computing service. As the optimization problem is
non-convex and NP-hard, simulated annealing based on the Markov Chain Monte
Carlo as well as the metropolis algorithm is applied to solve the optimization
problem, which can efficaciously obtain both high-quality and cost-effective
approximations of global optimal solutions. The effectiveness of the proposed
mechanism is corroborated through simulation results
Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing
In this article we propose a novel Device-to-Device (D2D) Crowd framework for
5G mobile edge computing, where a massive crowd of devices at the network edge
leverage the network-assisted D2D collaboration for computation and
communication resource sharing among each other. A key objective of this
framework is to achieve energy-efficient collaborative task executions at
network-edge for mobile users. Specifically, we first introduce the D2D Crowd
system model in details, and then formulate the energy-efficient D2D Crowd task
assignment problem by taking into account the necessary constraints. We next
propose a graph matching based optimal task assignment policy, and further
evaluate its performance through extensive numerical study, which shows a
superior performance of more than 50% energy consumption reduction over the
case of local task executions. Finally, we also discuss the directions of
extending the D2D Crowd framework by taking into variety of application
factors.Comment: Xu Chen, Lingjun Pu, Lin Gao, Weigang Wu, and Di Wu, "Exploiting
Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing,"
accepted by IEEE Wireless Communications, 201
Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach
Mobile edge computing (MEC) emerges recently as a promising solution to
relieve resource-limited mobile devices from computation-intensive tasks, which
enables devices to offload workloads to nearby MEC servers and improve the
quality of computation experience. Nevertheless, by considering a MEC system
consisting of multiple mobile users with stochastic task arrivals and wireless
channels in this paper, the design of computation offloading policies is
challenging to minimize the long-term average computation cost in terms of
power consumption and buffering delay. A deep reinforcement learning (DRL)
based decentralized dynamic computation offloading strategy is investigated to
build a scalable MEC system with limited feedback. Specifically, a continuous
action space-based DRL approach named deep deterministic policy gradient (DDPG)
is adopted to learn efficient computation offloading policies independently at
each mobile user. Thus, powers of both local execution and task offloading can
be adaptively allocated by the learned policies from each user's local
observation of the MEC system. Numerical results are illustrated to demonstrate
that efficient policies can be learned at each user, and performance of the
proposed DDPG based decentralized strategy outperforms the conventional deep
Q-network (DQN) based discrete power control strategy and some other greedy
strategies with reduced computation cost. Besides, the power-delay tradeoff is
also analyzed for both the DDPG based and DQN based strategies
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 Incentive-Aware Job Offloading Control Framework for Mobile Edge Computing
This paper considers a scenario in which an access point (AP) is equipped
with a mobile edge server of finite computing power, and serves multiple
resource-hungry mobile users by charging users a price. Pricing provides users
with incentives in offloading. However, existing works on pricing are based on
abstract concave utility functions (e.g, the logarithm function), giving no
dependence on physical layer parameters. To that end, we first introduce a
novel utility function, which measures the cost reduction by offloading as
compared with executing jobs locally. Based on this utility function we then
formulate two offloading games, with one maximizing individual's interest and
the other maximizing the overall system's interest. We analyze the structural
property of the games and admit in closed form the Nash Equilibrium and the
Social Equilibrium, respectively. The proposed expressions are functions of the
user parameters such as the weights of computational time and energy, the
distance from the AP, thus constituting an advancement over prior economic
works that have considered only abstract functions. Finally, we propose an
optimal pricing-based scheme, with which we prove that the interactive
decision-making process with self-interested users converges to a Nash
Equilibrium point equal to the Social Equilibrium point.Comment: 13 pages, 9 figure
Hierarchical Fog-Cloud Computing for IoT Systems: A Computation Offloading Game
Fog computing, which provides low-latency computing services at the network
edge, is an enabler for the emerging Internet of Things (IoT) systems. In this
paper, we study the allocation of fog computing resources to the IoT users in a
hierarchical computing paradigm including fog and remote cloud computing
services. We formulate a computation offloading game to model the competition
between IoT users and allocate the limited processing power of fog nodes
efficiently. Each user aims to maximize its own quality of experience (QoE),
which reflects its satisfaction of using computing services in terms of the
reduction in computation energy and delay. Utilizing a potential game approach,
we prove the existence of a pure Nash equilibrium and provide an upper bound
for the price of anarchy. Since the time complexity to reach the equilibrium
increases exponentially in the number of users, we further propose a
near-optimal resource allocation mechanism and prove that in a system with
IoT users, it can achieve an -Nash equilibrium in
time. Through numerical studies, we evaluate the users' QoE as well as the
equilibrium efficiency. Our results reveal that by utilizing the proposed
mechanism, more users benefit from computing services in comparison to an
existing offloading mechanism. We further show that our proposed mechanism
significantly reduces the computation delay and enables low-latency fog
computing services for delay-sensitive IoT applications
Air-Ground Integrated Mobile Edge Networks: Architecture, Challenges and Opportunities
The ever-increasing mobile data demands have posed significant challenges in
the current radio access networks, while the emerging computation-heavy
Internet of things (IoT) applications with varied requirements demand more
flexibility and resilience from the cloud/edge computing architecture. In this
article, to address the issues, we propose a novel air-ground integrated mobile
edge network (AGMEN), where UAVs are flexibly deployed and scheduled, and
assist the communication, caching, and computing of the edge network. In
specific, we present the detailed architecture of AGMEN, and investigate the
benefits and application scenarios of drone-cells, and UAV-assisted edge
caching and computing. Furthermore, the challenging issues in AGMEN are
discussed, and potential research directions are highlighted.Comment: Accepted by IEEE Communications Magazine. 5 figure
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