2,760 research outputs found
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
TARCO: Two-Stage Auction for D2D Relay Aided Computation Resource Allocation in Hetnet
In heterogeneous cellular network, task scheduling for computation offloading
is one of the biggest challenges. Most works focus on alleviating heavy burden
of macro base stations by moving the computation tasks on macro-cell user
equipment (MUE) to remote cloud or small-cell base stations. But the
selfishness of network users is seldom considered. Motivated by the cloud edge
computing, this paper provides incentive for task transfer from macro cell
users to small cell base stations. The proposed incentive scheme utilizes small
cell user equipment to provide relay service. The problem of computation
offloading is modelled as a two-stage auction, in which the remote MUEs with
common social character can form a group and then buy the computation resource
of small-cell base stations with the relay of small cell user equipment. A
two-stage auction scheme named TARCO is contributed to maximize utilities for
both sellers and buyers in the network. The truthful, individual rationality
and budget balance of the TARCO are also proved in this paper. In addition, two
algorithms are proposed to further refine TARCO on the social welfare of the
network. Extensive simulation results demonstrate that, TARCO is better than
random algorithm by about 104.90% in terms of average utility of MUEs, while
the performance of TARCO is further improved up to 28.75% and 17.06% by the
proposed two algorithms, respectively.Comment: 22 pages, 9 figures, Working paper, SUBMITTED to IEEE TRANSACTIONS ON
SERVICES COMPUTIN
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
UAV-Enabled Mobile Edge Computing: Offloading Optimization and Trajectory Design
With the emergence of diverse mobile applications (such as augmented
reality), the quality of experience of mobile users is greatly limited by their
computation capacity and finite battery lifetime. Mobile edge computing (MEC)
and wireless power transfer are promising to address this issue. However, these
two techniques are susceptible to propagation delay and loss. Motivated by the
chance of short-distance line-of-sight achieved by leveraging unmanned aerial
vehicle (UAV) communications, an UAV-enabled wireless powered MEC system is
studied. A power minimization problem is formulated subject to the constraints
on the number of the computation bits and energy harvesting causality. The
problem is non-convex and challenging to tackle. An alternative optimization
algorithm is proposed based on sequential convex optimization. Simulation
results show that our proposed design is superior to other benchmark schemes
and the proposed algorithm is efficient in terms of the convergence.Comment: This paper has been accepted by IEEE ICC 201
Information-Centric Wireless Networks with Mobile Edge Computing
In order to better accommodate the dramatically increasing demand for data
caching and computing services, storage and computation capabilities should be
endowed to some of the intermediate nodes within the network. In this paper, we
design a novel virtualized heterogeneous networks framework aiming at enabling
content caching and computing. With the virtualization of the whole system, the
communication, computing and caching resources can be shared among all users
associated with different virtual service providers. We formulate the virtual
resource allocation strategy as a joint optimization problem, where the gains
of not only virtualization but also caching and computing are taken into
consideration in the proposed architecture. In addition, a distributed
algorithm based on alternating direction method of multipliers is adopted to
solve the formulated problem, in order to reduce the computational complexity
and signaling overhead. Finally, extensive simulations are presented to show
the effectiveness of the proposed scheme under different system parameters
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
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
Bi-Directional Mission Offloading for Agile Space-Air-Ground Integrated Networks
Space-air-ground integrated networks (SAGIN) provide great strengths in
extending the capability of ground wireless networks. On the other hand, with
rich spectrum and computing resources, the ground networks can also assist
space-air networks to accomplish resource-intensive or power-hungry missions,
enhancing the capability and sustainability of the space-air networks.
Therefore, bi-directional mission offloading can make full use of the
advantages of SAGIN and benefits both space-air and ground networks. In this
article, we identify the key role of network reconfiguration in coordinating
heterogeneous resources in SAGIN, and study how network function virtualization
(NFV) and service function chaining (SFC) enable agile mission offloading. A
case study validates the performance gain brought by bi-directional mission
offloading. Future research issues are outlooked as the bi-directional mission
offloading framework opens a new trail in releasing the full potentials of
SAGIN.Comment: accepted by IEEE Wireless Communications Magazin
Aqua Computing: Coupling Computing and Communications
The authors introduce a new vision for providing computing services for
connected devices. It is based on the key concept that future computing
resources will be coupled with communication resources, for enhancing user
experience of the connected users, and also for optimising resources in the
providers' infrastructures. Such coupling is achieved by Joint/Cooperative
resource allocation algorithms, by integrating computing and communication
services and by integrating hardware in networks. Such type of computing, by
which computing services are not delivered independently but dependent of
networking services, is named Aqua Computing. The authors see Aqua Computing as
a novel approach for delivering computing resources to end devices, where
computing power of the devices are enhanced automatically once they are
connected to an Aqua Computing enabled network. The process of resource
coupling is named computation dissolving. Then, an Aqua Computing architecture
is proposed for mobile edge networks, in which computing and wireless
networking resources are allocated jointly or cooperatively by a Mobile Cloud
Controller, for the benefit of the end-users and/or for the benefit of the
service providers. Finally, a working prototype of the system is shown and the
gathered results show the performance of the Aqua Computing prototype.Comment: A shorter version of this paper will be submitted to an IEEE magazin
AACT: Application-Aware Cooperative Time Allocation for Internet of Things
As the number of Internet of Things (IoT) devices keeps increasing, data is
required to be communicated and processed by these devices at unprecedented
rates. Cooperation among wireless devices by exploiting Device-to-Device (D2D)
connections is promising, where aggregated resources in a cooperative setup can
be utilized by all devices, which would increase the total utility of the
setup. In this paper, we focus on the resource allocation problem for
cooperating IoT devices with multiple heterogeneous applications. In
particular, we develop Application-Aware Cooperative Time allocation (AACT)
framework, which optimizes the time that each application utilizes the
aggregated system resources by taking into account heterogeneous device
constraints and application requirements. AACT is grounded on the concept of
Rolling Horizon Control (RHC) where decisions are made by iteratively solving a
convex optimization problem over a moving control window of estimated system
parameters. The simulation results demonstrate significant performance gains
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