3,605 research outputs found
Multi-user Multi-task Offloading and Resource Allocation in Mobile Cloud Systems
We consider a general multi-user Mobile Cloud Computing (MCC) system where
each mobile user has multiple independent tasks. These mobile users share the
computation and communication resources while offloading tasks to the cloud. We
study both the conventional MCC where tasks are offloaded to the cloud through
a wireless access point, and MCC with a computing access point (CAP), where the
CAP serves both as the network access gateway and a computation service
provider to the mobile users. We aim to jointly optimize the offloading
decisions of all users as well as the allocation of computation and
communication resources, to minimize the overall cost of energy, computation,
and delay for all users. The optimization problem is formulated as a non-convex
quadratically constrained quadratic program, which is NP-hard in general. For
the case without a CAP, an efficient approximate solution named MUMTO is
proposed by using separable semidefinite relaxation (SDR), followed by recovery
of the binary offloading decision and optimal allocation of the communication
resource. To solve the more complicated problem with a CAP, we further propose
an efficient three-step algorithm named MUMTO-C comprising of generalized MUMTO
SDR with CAP, alternating optimization, and sequential tuning, which always
computes a locally optimal solution. For performance benchmarking, we further
present numerical lower bounds of the minimum system cost with and without the
CAP. By comparison with this lower bound, our simulation results show that the
proposed solutions for both scenarios give nearly optimal performance under
various parameter settings, and the resultant efficient utilization of a CAP
can bring substantial cost benefit.Comment: to appear IEEE Trans. Wireless Communications. arXiv admin note: text
overlap with arXiv:1712.0003
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
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
Device vs Edge Computing for Mobile Services: Delay-aware Decision Making to Minimize Power Consumption
A promising technique to provide mobile applications with high computation
resources is to offload the processing task to the cloud. Utilizing the
abundant processing capabilities of the clouds, mobile edge computing enables
mobile devices with limited batteries to run resource hungry applications and
to save power. However, it is not always true that edge computing consumes less
power compared to device computing. It may take more power for the mobile
device to transmit a file to the cloud than running the task itself. This paper
investigates the power minimization problem for the mobile devices by data
offloading in multi-cell multi-user OFDMA mobile edge computing networks. We
consider the maximum acceptable delay as QoS metric to be satisfied in our
network. We formulate the problem as a mixed integer nonlinear problem which is
converted into a convex form using D.C. approximation. To solve the converted
optimization problem, we have proposed centralized and distributed algorithms
for joint power allocation and channel assignment together with
decision-making. Simulation results illustrate that by utilizing the proposed
algorithms, considerable power savings can be achieved, e.g., about 60 % for
large bit stream size compared to local computing baseline
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
Non-cooperative game approach for task offloading in edge clouds
Task offloading provides a promising way to enhance the capability of the
mobile terminal (also called terminal user) that is distributed on network edge
and communicates edge clouds with wireless. Generally, there are multiple edge
cloud nodes with distinct processing capability in a geographic area, which can
offer computing service for various terminal users. Furthermore, the terminal
users are competitive and selfish, i.e., each user takes into account only
maximizing her own profit, while conducting task offloading strategies. In this
paper, we focus on the resource management optimization for edge clouds, and
formulate the problem of resource competition among terminal users as a
non-cooperative game, in which the terminal user who acts as the player always
pursues the minimization of the expected response time for her tasks by
optimizing allocation strategies. We present the utility function of the user
with queuing theory, and then prove the existence of Nash equilibrium for the
formulated game. Using the concept of Nash bargaining solution to calculate the
optimal task offloading scheme for the user, we propose a distributed task
offloading algorithm with low computation complexity. The results of simulated
experiments demonstrate that our method can quickly reach the Nash equilibrium
point, and deliver satisfying performance at the expected response time of the
user's tasks.Comment: 12 pages,11 figure
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
Latency Optimization for Resource Allocation in Mobile-Edge Computation Offloading
By offloading intensive computation tasks to the edge cloud located at the
cellular base stations, mobile-edge computation offloading (MECO) has been
regarded as a promising means to accomplish the ambitious millisecond-scale
end-to-end latency requirement of the fifth-generation networks. In this paper,
we investigate the latency-minimization problem in a multi-user time-division
multiple access MECO system with joint communication and computation resource
allocation. Three different computation models are studied, i.e., local
compression, edge cloud compression, and partial compression offloading. First,
closed-form expressions of optimal resource allocation and minimum system delay
for both local and edge cloud compression models are derived. Then, for the
partial compression offloading model, we formulate a piecewise optimization
problem and prove that the optimal data segmentation strategy has a piecewise
structure. Based on this result, an optimal joint communication and computation
resource allocation algorithm is developed. To gain more insights, we also
analyze a specific scenario where communication resource is adequate while
computation resource is limited. In this special case, the closed-form solution
of the piecewise optimization problem can be derived. Our proposed algorithms
are finally verified by numerical results, which show that the novel partial
compression offloading model can significantly reduce the end-to-end latency.Comment: Submitted for Journal Publicatio
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
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