3,426 research outputs found
Exploiting Computation Replication for Mobile Edge Computing: A Fundamental Computation-Communication Tradeoff Study
Existing works on task offloading in mobile edge computing (MEC) networks
often assume a task is executed once at a single edge node (EN). Downloading
the computed result from the EN back to the mobile user may suffer long delay
if the downlink channel experiences strong interference or deep fading. This
paper exploits the idea of computation replication in MEC networks to speed up
the downloading phase. Computation replication allows each user to offload its
task to multiple ENs for repetitive execution so as to create multiple copies
of the computed result at different ENs which can then enable transmission
cooperation and hence reduce the communication latency for result downloading.
Yet, computation replication may also increase the communication latency for
task uploading, despite the obvious increase in computation load. The main
contribution of this work is to characterize asymptotically an order-optimal
upload-download communication latency pair for a given computation load in a
multi-user multi-server MEC network. Analysis shows when the computation load
increases within a certain range, the downloading time decreases in an
inversely proportional way if it is binary offloading or decreases linearly if
it is partial offloading, both at the expense of linear increase in the
uploading time.Comment: To appear in IEEE Transactions on Wireless Communication
Computation Rate Maximization for Wireless Powered Mobile-Edge Computing with Binary Computation Offloading
In this paper, we consider a multi-user mobile edge computing (MEC) network
powered by wireless power transfer (WPT), where each energy-harvesting WD
follows a binary computation offloading policy, i.e., data set of a task has to
be executed as a whole either locally or remotely at the MEC server via task
offloading. In particular, we are interested in maximizing the (weighted) sum
computation rate of all the WDs in the network by jointly optimizing the
individual computing mode selection (i.e., local computing or offloading) and
the system transmission time allocation (on WPT and task offloading). The major
difficulty lies in the combinatorial nature of multi-user computing mode
selection and its strong coupling with transmission time allocation. To tackle
this problem, we first consider a decoupled optimization, where we assume that
the mode selection is given and propose a simple bi-section search algorithm to
obtain the conditional optimal time allocation. On top of that, a coordinate
descent method is devised to optimize the mode selection. The method is simple
in implementation but may suffer from high computational complexity in a
large-size network. To address this problem, we further propose a joint
optimization method based on the ADMM (alternating direction method of
multipliers) decomposition technique, which enjoys much slower increase of
computational complexity as the networks size increases. Extensive simulations
show that both the proposed methods can efficiently achieve near-optimal
performance under various network setups, and significantly outperform the
other representative benchmark methods considered.Comment: This paper has been accepted for publication in IEEE Transactions on
Wireless Communication
Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks
Opportunistic computation offloading is an effective method to improve the
computation performance of mobile-edge computing (MEC) networks under dynamic
edge environment. In this paper, we consider a multi-user MEC network with
time-varying wireless channels and stochastic user task data arrivals in
sequential time frames. In particular, we aim to design an online computation
offloading algorithm to maximize the network data processing capability subject
to the long-term data queue stability and average power constraints. The online
algorithm is practical in the sense that the decisions for each time frame are
made without the assumption of knowing future channel conditions and data
arrivals. We formulate the problem as a multi-stage stochastic mixed integer
non-linear programming (MINLP) problem that jointly determines the binary
offloading (each user computes the task either locally or at the edge server)
and system resource allocation decisions in sequential time frames. To address
the coupling in the decisions of different time frames, we propose a novel
framework, named LyDROO, that combines the advantages of Lyapunov optimization
and deep reinforcement learning (DRL). Specifically, LyDROO first applies
Lyapunov optimization to decouple the multi-stage stochastic MINLP into
deterministic per-frame MINLP subproblems. By doing so, it guarantees to
satisfy all the long-term constraints by solving the per-frame subproblems that
are much smaller in size. Then, LyDROO integrates model-based optimization and
model-free DRL to solve the per-frame MINLP problems with low computational
complexity. Simulation results show that under various network setups, the
proposed LyDROO achieves optimal computation performance while stabilizing all
queues in the system. Besides, it induces very low execution latency that is
particularly suitable for real-time implementation in fast fading environments.Comment: The paper has been accepted for publication by IEEE Trans. Wireless
Communications, the source codes associated with the paper are available at
https://github.com/revenol/LyDROO. arXiv admin note: text overlap with
arXiv:2102.0328
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
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
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
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
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
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
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
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