2,876 research outputs found
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
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
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
Computation Offloading and Activation of Mobile Edge Computing Servers: A Minority Game
With the ever-increasing popularity of resource-intensive mobile
applications, Mobile Edge Computing (MEC), e.g., offloading computationally
expensive tasks to the cellular edge, has become a prominent technology for the
next generation wireless networks. Despite its great performance in terms of
delay and energy, MEC suffers from restricted power allowance and computational
capability of the edge nodes. Therefore, it is imperative to develop
distributed mechanisms for computation offloading, so that not only the
computational servers are utilized at their best capacity, but also the users'
latency constraints are fulfilled. In this paper, by using the theory of
Minority Games, we develop a novel distributed server activation mechanism for
computational offloading. Our scheme guarantees energy-efficient activation of
servers as well as satisfaction of users' quality-of-experience (QoE)
requirements in terms of latency
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
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
Context-Aware Wireless Connectivity and Processing Unit Optimization for IoT Networks
A novel approach is presented in this work for context-aware connectivity and
processing optimization of Internet of things (IoT) networks. Different from
the state-of-the-art approaches, the proposed approach simultaneously selects
the best connectivity and processing unit (e.g., device, fog, and cloud) along
with the percentage of data to be offloaded by jointly optimizing energy
consumption, response-time, security, and monetary cost. The proposed scheme
employs a reinforcement learning algorithm, and manages to achieve significant
gains compared to deterministic solutions. In particular, the requirements of
IoT devices in terms of response-time and security are taken as inputs along
with the remaining battery level of the devices, and the developed algorithm
returns an optimized policy. The results obtained show that only our method is
able to meet the holistic multi-objective optimisation criteria, albeit, the
benchmark approaches may achieve better results on a particular metric at the
cost of failing to reach the other targets. Thus, the proposed approach is a
device-centric and context-aware solution that accounts for the monetary and
battery constraints
Multi-Armed Bandit for Energy-Efficient and Delay-Sensitive Edge Computing in Dynamic Networks with Uncertainty
In the edge computing paradigm, mobile devices offload the computational
tasks to an edge server by routing the required data over the wireless network.
The full potential of edge computing becomes realized only if a smart device
selects the most appropriate server in terms of the latency and energy
consumption, among many available ones. The server selection problem is
challenging due to the randomness of the environment and lack of prior
information about the environment. Therefore, a smart device, which
sequentially chooses a server under uncertainty, aims to improve its decision
based on the historical time and energy consumption. The problem becomes more
complicated in a dynamic environment, where key variables might undergo abrupt
changes. To deal with the aforementioned problem, we first analyze the required
time and energy to data transmission and processing. We then use the analysis
to cast the problem as a budget-limited multi-armed bandit problem, where each
arm is associated with a reward and cost, with time-variant statistical
characteristics. We propose a policy to solve the formulated problem and prove
a regret bound. The numerical results demonstrate the superiority of the
proposed method compared to a number of existing solutions.Comment: 30 pages, 7 figure
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
Exploiting Non-Causal CPU-State Information for Energy-Efficient Mobile Cooperative Computing
Scavenging the idling computation resources at the enormous number of mobile
devices can provide a powerful platform for local mobile cloud computing. The
vision can be realized by peer-to-peer cooperative computing between edge
devices, referred to as co-computing. This paper considers a co-computing
system where a user offloads computation of input-data to a helper. The helper
controls the offloading process for the objective of minimizing the user's
energy consumption based on a predicted helper's CPU-idling profile that
specifies the amount of available computation resource for co-computing.
Consider the scenario that the user has one-shot input-data arrival and the
helper buffers offloaded bits. The problem for energy-efficient co-computing is
formulated as two sub-problems: the slave problem corresponding to adaptive
offloading and the master one to data partitioning. Given a fixed offloaded
data size, the adaptive offloading aims at minimizing the energy consumption
for offloading by controlling the offloading rate under the deadline and buffer
constraints. By deriving the necessary and sufficient conditions for the
optimal solution, we characterize the structure of the optimal policies and
propose algorithms for computing the policies. Furthermore, we show that the
problem of optimal data partitioning for offloading and local computing at the
user is convex, admitting a simple solution using the sub-gradient method.
Last, the developed design approach for co-computing is extended to the
scenario of bursty data arrivals at the user accounting for data causality
constraints. Simulation results verify the effectiveness of the proposed
algorithms.Comment: Submitted to possible journa
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