4,285 research outputs found
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
Efficient Resource Allocation for On-Demand Mobile-Edge Cloud Computing
Mobile-edge cloud computing is a new paradigm to provide cloud computing
capabilities at the edge of pervasive radio access networks in close proximity
to mobile users. Aiming at provisioning flexible on-demand mobile-edge cloud
service, in this paper we propose a comprehensive framework consisting of a
resource-efficient computation offloading mechanism for users and a joint
communication and computation (JCC) resource allocation mechanism for network
operator. Specifically, we first study the resource-efficient computation
offloading problem for a user, in order to reduce user's resource occupation by
determining its optimal communication and computation resource profile with
minimum resource occupation and meanwhile satisfying the QoS constraint. We
then tackle the critical problem of user admission control for JCC resource
allocation, in order to properly select the set of users for resource demand
satisfaction. We show the admission control problem is NP-hard, and hence
develop an efficient approximation solution of a low complexity by carefully
designing the user ranking criteria and rigourously derive its performance
guarantee. To prevent the manipulation that some users may untruthfully report
their valuations in acquiring mobile-edge cloud service, we further resort to
the powerful tool of critical value approach to design truthful pricing scheme
for JCC resource allocation. Extensive performance evaluation demonstrates that
the proposed schemes can achieve superior performance for on-demand mobile-edge
cloud computing.Comment: Xu Chen,Wenzhong Li,Sanglu Lu,Zhi Zhou,and Xiaoming Fu, "Efficient
Resource Allocation for On-Demand Mobile-Edge Cloud Computing," IEEE
Transactions on Vehicular Technology, June 201
Computation Efficiency Maximization in OFDMA-Based Mobile Edge Computing Networks
Computation-efficient resource allocation strategies are of crucial
importance in mobile edge computing networks. However, few works have focused
on this issue. In this letter, weighted sum computation efficiency (CE)
maximization problems are formulated in a mobile edge computing (MEC) network
with orthogonal frequency division multiple access (OFDMA). Both partial
offloading mode and binary offloading mode are considered. The closed-form
expressions for the optimal subchannel and power allocation schemes are
derived. In order to address the intractable non-convex weighted sum-of ratio
problems, an efficiently iterative algorithm is proposed. Simulation results
demonstrate that the CE achieved by our proposed resource allocation scheme is
better than that obtained by the benchmark schemes.Comment: This paper has been accepted by IEEE Communications Letter
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
Multiuser Computation Offloading and Downloading for Edge Computing with Virtualization
Mobile-edge computing (MEC) is an emerging technology for enhancing the
computational capabilities of mobile devices and reducing their energy
consumption via offloading complex computation tasks to the nearby servers.
Multiuser MEC at servers is widely realized via parallel computing based on
virtualization. Due to finite shared I/O resources, interference between
virtual machines (VMs), called I/O interference, degrades the computation
performance. In this paper, we study the problem of joint radio-and-computation
resource allocation (RCRA) in multiuser MEC systems in the presence of I/O
interference. Specifically, offloading scheduling algorithms are designed
targeting two system performance metrics: sum offloading throughput
maximization and sum mobile energy consumption minimization. Their designs are
formulated as non-convex mixed-integer programming problems, which account for
latency due to offloading, result downloading and parallel computing. A set of
low-complexity algorithms are designed based on a decomposition approach and
leveraging classic techniques from combinatorial optimization. The resultant
algorithms jointly schedule offloading users, control their offloading sizes,
and divide time for communication (offloading and downloading) and computation.
They are either optimal or can achieve close-to-optimality as shown by
simulation. Comprehensive simulation results demonstrate considering of I/O
interference can endow on an offloading controller robustness against the
performance-degradation factor
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
Energy-Efficient Mobile-Edge Computation Offloading for Applications with Shared Data
Mobile-edge computation offloading (MECO) has been recognized as a promising
solution to alleviate the burden of resource-limited Internet of Thing (IoT)
devices by offloading computation tasks to the edge of cellular networks (also
known as {\em cloudlet}). Specifically, latency-critical applications such as
virtual reality (VR) and augmented reality (AR) have inherent collaborative
properties since part of the input/output data are shared by different users in
proximity. In this paper, we consider a multi-user fog computing system, in
which multiple single-antenna mobile users running applications featuring
shared data can choose between (partially) offloading their individual tasks to
a nearby single-antenna cloudlet for remote execution and performing pure local
computation. The mobile users' energy minimization is formulated as a convex
problem, subject to the total computing latency constraint, the total energy
constraints for individual data downloading, and the computing frequency
constraints for local computing, for which classical Lagrangian duality can be
applied to find the optimal solution. Based upon the semi-closed form solution,
the shared data proves to be transmitted by only one of the mobile users
instead of multiple ones. Besides, compared to those baseline algorithms
without considering the shared data property or the mobile users' local
computing capabilities, the proposed joint computation offloading and
communications resource allocation provides significant energy saving.Comment: 6 pages, 3 figures, accepted by IEEE Globecom 201
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
Joint Optimization of Service Caching Placement and Computation Offloading in Mobile Edge Computing Systems
In mobile edge computing (MEC) systems, edge service caching refers to
pre-storing the necessary programs for executing computation tasks at MEC
servers. At resource-constrained edge servers, service caching placement is in
general a complicated problem that highly correlates to the offloading
decisions of computation tasks. In this paper, we consider a single edge server
that assists a mobile user (MU) in executing a sequence of computation tasks.
In particular, the MU can run its customized programs at the edge server, while
the server can selectively cache the previously generated programs for future
service reuse. To minimize the computation delay and energy consumption of the
MU, we formulate a mixed integer non-linear programming (MINLP) that jointly
optimizes the service caching placement, computation offloading, and system
resource allocation. We first derive the closed-form expressions of the optimal
resource allocation, and subsequently transform the MINLP into an equivalent
pure 0-1 integer linear programming (ILP). To further reduce the complexity in
solving the ILP, we exploit the underlying structures in optimal solutions, and
devise a reduced-complexity alternating minimization technique to update the
caching placement and offloading decision alternately. Simulations show that
the proposed techniques achieve substantial resource savings compared to other
representative benchmark methods.Comment: The paper has been accepted for publication by IEEE Transactions on
Wireless Communications (April 2020
Computation Efficiency Maximization in Wireless-Powered Mobile Edge Computing Networks
Energy-efficient computation is an inevitable trend for mobile edge computing
(MEC) networks. Resource allocation strategies for maximizing the computation
efficiency are critically important. In this paper, computation efficiency
maximization problems are formulated in wireless-powered MEC networks under
both partial and binary computation offloading modes. A practical non-linear
energy harvesting model is considered. Both time division multiple access
(TDMA) and non-orthogonal multiple access (NOMA) are considered and evaluated
for offloading. The energy harvesting time, the local computing frequency, and
the offloading time and power are jointly optimized to maximize the computation
efficiency under the max-min fairness criterion. Two iterative algorithms and
two alternative optimization algorithms are respectively proposed to address
the non-convex problems formulated in this paper. Simulation results show that
the proposed resource allocation schemes outperform the benchmark schemes in
terms of user fairness. Moreover, a tradeoff is elucidated between the
achievable computation efficiency and the total number of computed bits.
Furthermore, simulation results demonstrate that the partial computation
offloading mode outperforms the binary computation offloading mode and NOMA
outperforms TDMA in terms of computation efficiency.Comment: This paper has been accepted for publication in IEEE Transactions on
Wireless Communication
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