1,720 research outputs found
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
Delay constrained Energy Optimization for Edge Cloud Offloading
Resource limited user-devices may offload computation to a cloud server, in
order to reduce power consumption and lower the execution time. However, to
communicate to the cloud server over a wireless channel, additional energy is
consumed for transmitting the data. Also a delay is introduced for offloading
the data and receiving the response. Therefore, an optimal decision needs to be
made that would reduce the energy consumption, while simultaneously satisfying
the delay constraint. In this paper, we obtain an optimal closed form solution
for these decision variables in a multi-user scenario. Furthermore, we
optimally allocate the cloud server resources to the user devices, and evaluate
the minimum delay that the system can provide, for a given bandwidth and number
of user devices.Comment: Published in ICC workshop 201
Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices
Mobile-edge computing (MEC) is an emerging paradigm to meet the
ever-increasing computation demands from mobile applications. By offloading the
computationally intensive workloads to the MEC server, the quality of
computation experience, e.g., the execution latency, could be greatly improved.
Nevertheless, as the on-device battery capacities are limited, computation
would be interrupted when the battery energy runs out. To provide satisfactory
computation performance as well as achieving green computing, it is of
significant importance to seek renewable energy sources to power mobile devices
via energy harvesting (EH) technologies. In this paper, we will investigate a
green MEC system with EH devices and develop an effective computation
offloading strategy. The execution cost, which addresses both the execution
latency and task failure, is adopted as the performance metric. A
low-complexity online algorithm, namely, the Lyapunov optimization-based
dynamic computation offloading (LODCO) algorithm is proposed, which jointly
decides the offloading decision, the CPU-cycle frequencies for mobile
execution, and the transmit power for computation offloading. A unique
advantage of this algorithm is that the decisions depend only on the
instantaneous side information without requiring distribution information of
the computation task request, the wireless channel, and EH processes. The
implementation of the algorithm only requires to solve a deterministic problem
in each time slot, for which the optimal solution can be obtained either in
closed form or by bisection search. Moreover, the proposed algorithm is shown
to be asymptotically optimal via rigorous analysis. Sample simulation results
shall be presented to verify the theoretical analysis as well as validate the
effectiveness of the proposed algorithm.Comment: 33 pages, 11 figures, submitted to IEEE Journal on Selected Areas in
Communication
Joint Optimization of Radio Resources and Code Partitioning in Mobile Edge Computing
The aim of this paper is to propose a computation offloading strategy for
mobile edge computing. We exploit the concept of call graph, which models a
generic computer program as a set of procedures related to each other through a
weighted directed graph. Our goal is to derive the optimal partition of the
call graph establishing which procedures are to be executed locally or
remotely. The main novelty of our work is that the optimal partition is
obtained jointly with the selection of radio parameters, e.g., transmit power
and constellation size, in order to minimize the energy consumption at the
mobile handset, under a latency constraint taking into account transmit time
and execution time. We consider both single and multi-channel transmission
strategies and we prove that a globally optimal solution can be achieved in
both cases. Finally, we propose a suboptimal strategy aimed at solving a
relaxed version of the original problem in order to tradeoff complexity and
performance of the proposed framework. Finally, several numerical results
illustrate under what conditions in terms of call graph topology, communication
strategy, and computation parameters, the proposed offloading strategy provides
large performance gains.Comment: Submitted to IEEE Transactions on Signal Processin
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
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
Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading (Extended Version)
Mobile-edge computation offloading (MECO) offloads intensive mobile
computation to clouds located at the edges of cellular networks. Thereby, MECO
is envisioned as a promising technique for prolonging the battery lives and
enhancing the computation capacities of mobiles. In this paper, we study
resource allocation for a multiuser MECO system based on time-division multiple
access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First,
for the TDMA MECO system with infinite or finite computation capacity, the
optimal resource allocation is formulated as a convex optimization problem for
minimizing the weighted sum mobile energy consumption under the constraint on
computation latency. The optimal policy is proved to have a threshold-based
structure with respect to a derived offloading priority function, which yields
priorities for users according to their channel gains and local computing
energy consumption. As a result, users with priorities above and below a given
threshold perform complete and minimum offloading, respectively. Moreover, for
the cloud with finite capacity, a sub-optimal resource-allocation algorithm is
proposed to reduce the computation complexity for computing the threshold.
Next, we consider the OFDMA MECO system, for which the optimal resource
allocation is formulated as a non-convex mixed-integer problem. To solve this
challenging problem and characterize its policy structure, a sub-optimal
low-complexity algorithm is proposed by transforming the OFDMA problem to its
TDMA counterpart. The corresponding resource allocation is derived by defining
an average offloading priority function and shown to have close-to-optimal
performance by simulation.Comment: Accepted to IEEE Trans. on Wireless Communicatio
Computation Rate Maximization in UAV-Enabled Wireless Powered Mobile-Edge Computing Systems
Mobile edge computing (MEC) and wireless power transfer (WPT) are two
promising techniques to enhance the computation capability and to prolong the
operational time of low-power wireless devices that are ubiquitous in Internet
of Things. However, the computation performance and the harvested energy are
significantly impacted by the severe propagation loss. In order to address this
issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless powered system is
studied in this paper. The computation rate maximization problems in a
UAV-enabled MEC wireless powered system are investigated under both partial and
binary computation offloading modes, subject to the energy harvesting causal
constraint and the UAV's speed constraint. These problems are non-convex and
challenging to solve. A two-stage algorithm and a three-stage alternative
algorithm are respectively proposed for solving the formulated problems. The
closed-form expressions for the optimal central processing unit frequencies,
user offloading time, and user transmit power are derived. The optimal
selection scheme on whether users choose to locally compute or offload
computation tasks is proposed for the binary computation offloading mode.
Simulation results show that our proposed resource allocation schemes
outperforms other benchmark schemes. The results also demonstrate that the
proposed schemes converge fast and have low computational complexity.Comment: This paper has been accepted by IEEE JSA
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
- …