2,319 research outputs found
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
Service Capacity Enhanced Task Offloading and Resource Allocation in Multi-Server Edge Computing Environment
An edge computing environment features multiple edge servers and multiple
service clients. In this environment, mobile service providers can offload
client-side computation tasks from service clients' devices onto edge servers
to reduce service latency and power consumption experienced by the clients. A
critical issue that has yet to be properly addressed is how to allocate edge
computing resources to achieve two optimization objectives: 1) minimize the
service cost measured by the service latency and the power consumption
experienced by service clients; and 2) maximize the service capacity measured
by the number of service clients that can offload their computation tasks in
the long term. This paper formulates this long-term problem as a stochastic
optimization problem and solves it with an online algorithm based on Lyapunov
optimization. This NP-hard problem is decomposed into three sub-problems, which
are then solved with a suite of techniques. The experimental results show that
our approach significantly outperforms two baseline approaches.Comment: This paper has been accepted by Early Submission Phase of ICWS201
Decentralized Computation Offloading and Resource Allocation in Heterogeneous Networks with Mobile Edge Computing
We consider a heterogeneous network with mobile edge computing, where a user
can offload its computation to one among multiple servers. In particular, we
minimize the system-wide computation overhead by jointly optimizing the
individual computation decisions, transmit power of the users, and computation
resource at the servers. The crux of the problem lies in the combinatorial
nature of multi-user offloading decisions, the complexity of the optimization
objective, and the existence of inter-cell interference. Then, we decompose the
underlying problem into two subproblems: i) the offloading decision, which
includes two phases of user association and subchannel assignment, and ii)
joint resource allocation, which can be further decomposed into the problems of
transmit power and computation resource allocation. To enable distributed
computation offloading, we sequentially apply a many-to-one matching game for
user association and a one-to-one matching game for subchannel assignment.
Moreover, the transmit power of offloading users is found using a bisection
method with approximate inter-cell interference, and the computation resources
allocated to offloading users is achieved via the duality approach. The
proposed algorithm is shown to converge and is stable. Finally, we provide
simulations to validate the performance of the proposed algorithm as well as
comparisons with the existing frameworks.Comment: Submitted to IEEE Journa
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
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 Assignment and Power Allocation for NOMA Mobile-Edge Computing Networks
Mobile edge computing (MEC) can enhance the computing capability of mobile
devices, and non-orthogonal multiple access (NOMA) can provide high data rates.
Combining these two technologies can effectively benefit the network with
spectrum and energy efficiency. In this paper, we investigate the task
completion time minimization in NOMA multiuser MEC networks, where multiple
users can offload their tasks simultaneously via the same frequency band. We
adopt the \emph{partial} offloading, in which each user can partition its
computation task into offloading computing and locally computing parts. We aim
to minimize the maximum task latency among users by optimizing their tasks
partition ratios and offloading transmit power. By considering the energy
consumption and transmitted power limitation of each user, the formulated
problem is quasi-convex. Thus, a bisection search (BSS) iterative algorithm is
proposed to obtain the minimum task completion time. To reduce the complexity
of the BSS algorithm and evaluate its optimality, we further derive the
closed-form expressions of the optimal task partition ratio and offloading
power for two-user NOMA MEC networks based on the analysed results. Simulation
results demonstrate the convergence and optimality of the proposed a BSS
algorithm and the effectiveness of the proposed optimal derivation
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
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
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
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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