946 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
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
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
Optimal Task Offloading and Resource Allocation in Mobile-Edge Computing with Inter-user Task Dependency
Mobile-edge computing (MEC) has recently emerged as a cost-effective paradigm
to enhance the computing capability of hardware-constrained wireless devices
(WDs). In this paper, we first consider a two-user MEC network, where each WD
has a sequence of tasks to execute. In particular, we consider task dependency
between the two WDs, where the input of a task at one WD requires the final
task output at the other WD. Under the considered task-dependency model, we
study the optimal task offloading policy and resource allocation (e.g., on
offloading transmit power and local CPU frequencies) that minimize the weighted
sum of the WDs' energy consumption and task execution time. The problem is
challenging due to the combinatorial nature of the offloading decisions among
all tasks and the strong coupling with resource allocation. To tackle this
problem, we first assume that the offloading decisions are given and derive the
closed-form expressions of the optimal offloading transmit power and local CPU
frequencies. Then, an efficient bi-section search method is proposed to obtain
the optimal solutions. Furthermore, we prove that the optimal offloading
decisions follow an one-climb policy, based on which a reduced-complexity Gibbs
Sampling algorithm is proposed to obtain the optimal offloading decisions. We
then extend the investigation to a general multi-user scenario, where the input
of a task at one WD requires the final task outputs from multiple other WDs.
Numerical results show that the proposed method can significantly outperform
the other representative benchmarks and efficiently achieve low complexity with
respect to the call graph size.Comment: This paper has been accepted for publication in IEEE Transactions on
Wireless Communication
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
Resource Sharing of a Computing Access Point for Multi-user Mobile Cloud Offloading with Delay Constraints
We consider a mobile cloud computing system with multiple users, a remote
cloud server, and a computing access point (CAP). The CAP serves both as the
network access gateway and a computation service provider to the mobile users.
It can either process the received tasks from mobile users or offload them to
the cloud. We jointly optimize the offloading decisions of all users, together
with the allocation of computation and communication resources, to minimize the
overall cost of energy consumption, computation, and maximum delay among users.
The joint optimization problem is formulated as a mixed-integer program. We
show that the problem can be reformulated and transformed into a non-convex
quadratically constrained quadratic program, which is NP-hard in general. We
then propose an efficient solution to this problem by semidefinite relaxation
and a novel randomization mapping method. Furthermore, when there is a strict
delay constraint for processing each user's task, we further propose a
three-step algorithm to guarantee the feasibility and local optimality of the
obtained solution. Our simulation results show that the proposed solutions give
nearly optimal performance under a wide range of parameter settings, and the
addition of a CAP can significantly reduce the cost of multi-user task
offloading compared with conventional mobile cloud computing where only the
remote cloud server is available.Comment: in IEEE Transactions on Mobile Computing, 201
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
DeepWear: Adaptive Local Offloading for On-Wearable Deep Learning
Due to their on-body and ubiquitous nature, wearables can generate a wide
range of unique sensor data creating countless opportunities for deep learning
tasks. We propose DeepWear, a deep learning (DL) framework for wearable devices
to improve the performance and reduce the energy footprint. DeepWear
strategically offloads DL tasks from a wearable device to its paired handheld
device through local network. Compared to the remote-cloud-based offloading,
DeepWear requires no Internet connectivity, consumes less energy, and is robust
to privacy breach. DeepWear provides various novel techniques such as
context-aware offloading, strategic model partition, and pipelining support to
efficiently utilize the processing capacity from nearby paired handhelds.
Deployed as a user-space library, DeepWear offers developer-friendly APIs that
are as simple as those in traditional DL libraries such as TensorFlow. We have
implemented DeepWear on the Android OS and evaluated it on COTS smartphones and
smartwatches with real DL models. DeepWear brings up to 5.08X and 23.0X
execution speedup, as well as 53.5% and 85.5% energy saving compared to
wearable-only and handheld-only strategies, respectively
Base Station ON-OFF Switching in 5G Wireless Networks: Approaches and Challenges
To achieve the expected 1000x data rates under the exponential growth of
traffic demand, a large number of base stations (BS) or access points (AP) will
be deployed in the fifth generation (5G) wireless systems, to support high data
rate services and to provide seamless coverage. Although such BSs are expected
to be small-scale with lower power, the aggregated energy consumption of all
BSs would be remarkable, resulting in increased environmental and economic
concerns. In existing cellular networks, turning off the under-utilized BSs is
an efficient approach to conserve energy while preserving the quality of
service (QoS) of mobile users. However, in 5G systems with new physical layer
techniques and the highly heterogeneous network architecture, new challenges
arise in the design of BS ON-OFF switching strategies. In this article, we
begin with a discussion on the inherent technical challenges of BS ON-OFF
switching. We then provide a comprehensive review of recent advances on
switching mechanisms in different application scenarios. Finally, we present
open research problems and conclude the paper.Comment: Appear to IEEE Wireless Communications, 201
Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning
To improve the quality of computation experience for mobile devices,
mobile-edge computing (MEC) is a promising paradigm by providing computing
capabilities in close proximity within a sliced radio access network (RAN),
which supports both traditional communication and MEC services. Nevertheless,
the design of computation offloading policies for a virtual MEC system remains
challenging. Specifically, whether to execute a computation task at the mobile
device or to offload it for MEC server execution should adapt to the
time-varying network dynamics. In this paper, we consider MEC for a
representative mobile user in an ultra-dense sliced RAN, where multiple base
stations (BSs) are available to be selected for computation offloading. The
problem of solving an optimal computation offloading policy is modelled as a
Markov decision process, where our objective is to maximize the long-term
utility performance whereby an offloading decision is made based on the task
queue state, the energy queue state as well as the channel qualities between MU
and BSs. To break the curse of high dimensionality in state space, we first
propose a double deep Q-network (DQN) based strategic computation offloading
algorithm to learn the optimal policy without knowing a priori knowledge of
network dynamics. Then motivated by the additive structure of the utility
function, a Q-function decomposition technique is combined with the double DQN,
which leads to novel learning algorithm for the solving of stochastic
computation offloading. Numerical experiments show that our proposed learning
algorithms achieve a significant improvement in computation offloading
performance compared with the baseline policies
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