2,138 research outputs found
Latency Minimization for Task Offloading in Hierarchical Fog-Computing C-RAN Networks
Fog-computing network combines the cloud computing and fog access points
(FAPs) equipped with mobile edge computing (MEC) servers together to support
computation-intensive tasks for mobile users. However, as FAPs have limited
computational capabilities and are solely assisted by a remote cloud center in
the baseband processing unit (BBU) of the cloud radio access (C-RAN) network,
the latency benefits of this fog-computing C-RAN network may be worn off when
facing a large number of offloading requests. In this paper, we investigate the
delay minimization problem for task offloading in a hierarchical fog-computing
C-RAN network, which consists of three tiers of computational services: MEC
server in radio units, MEC server in distributed units, and the cloud computing
in central units. The receive beamforming vectors, task allocation, computing
speed for offloaded tasks in each server and the transmission bandwidth split
of fronthaul links are optimized by solving the formulated mixed integer
programming problem. The simulation results validate the superiority of the
proposed hierarchical fog-computing C-RAN network in terms of the delay
performance.Comment: accepted by ICC 202
A Computation Offloading Incentive Mechanism with Delay and Cost Constraints under 5G Satellite-ground IoV architecture
The 5G Internet of Vehicles has become a new paradigm alongside the growing
popularity and variety of computation-intensive applications with high
requirements for computational resources and analysis capabilities. Existing
network architectures and resource management mechanisms may not sufficiently
guarantee satisfactory Quality of Experience and network efficiency, mainly
suffering from coverage limitation of Road Side Units, insufficient resources,
and unsatisfactory computational capabilities of onboard equipment, frequently
changing network topology, and ineffective resource management schemes. To meet
the demands of such applications, in this article, we first propose a novel
architecture by integrating the satellite network with 5G cloud-enabled
Internet of Vehicles to efficiently support seamless coverage and global
resource management. A incentive mechanism based joint optimization problem of
opportunistic computation offloading under delay and cost constraints is
established under the aforementioned framework, in which a vehicular user can
either significantly reduce the application completion time by offloading
workloads to several nearby vehicles through opportunistic vehicle-to-vehicle
channels while effectively controlling the cost or protect its own profit by
providing compensated computing service. As the optimization problem is
non-convex and NP-hard, simulated annealing based on the Markov Chain Monte
Carlo as well as the metropolis algorithm is applied to solve the optimization
problem, which can efficaciously obtain both high-quality and cost-effective
approximations of global optimal solutions. The effectiveness of the proposed
mechanism is corroborated through simulation results
Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing
In this article we propose a novel Device-to-Device (D2D) Crowd framework for
5G mobile edge computing, where a massive crowd of devices at the network edge
leverage the network-assisted D2D collaboration for computation and
communication resource sharing among each other. A key objective of this
framework is to achieve energy-efficient collaborative task executions at
network-edge for mobile users. Specifically, we first introduce the D2D Crowd
system model in details, and then formulate the energy-efficient D2D Crowd task
assignment problem by taking into account the necessary constraints. We next
propose a graph matching based optimal task assignment policy, and further
evaluate its performance through extensive numerical study, which shows a
superior performance of more than 50% energy consumption reduction over the
case of local task executions. Finally, we also discuss the directions of
extending the D2D Crowd framework by taking into variety of application
factors.Comment: Xu Chen, Lingjun Pu, Lin Gao, Weigang Wu, and Di Wu, "Exploiting
Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing,"
accepted by IEEE Wireless Communications, 201
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey
With the Internet of Things (IoT) becoming part of our daily life and our
environment, we expect rapid growth in the number of connected devices. IoT is
expected to connect billions of devices and humans to bring promising
advantages for us. With this growth, fog computing, along with its related edge
computing paradigms, such as multi-access edge computing (MEC) and cloudlet,
are seen as promising solutions for handling the large volume of
security-critical and time-sensitive data that is being produced by the IoT. In
this paper, we first provide a tutorial on fog computing and its related
computing paradigms, including their similarities and differences. Next, we
provide a taxonomy of research topics in fog computing, and through a
comprehensive survey, we summarize and categorize the efforts on fog computing
and its related computing paradigms. Finally, we provide challenges and future
directions for research in fog computing.Comment: 48 pages, 7 tables, 11 figures, 450 references. The data (categories
and features/objectives of the papers) of this survey are now available
publicly. Accepted by Elsevier Journal of Systems Architectur
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
Air-Ground Integrated Mobile Edge Networks: Architecture, Challenges and Opportunities
The ever-increasing mobile data demands have posed significant challenges in
the current radio access networks, while the emerging computation-heavy
Internet of things (IoT) applications with varied requirements demand more
flexibility and resilience from the cloud/edge computing architecture. In this
article, to address the issues, we propose a novel air-ground integrated mobile
edge network (AGMEN), where UAVs are flexibly deployed and scheduled, and
assist the communication, caching, and computing of the edge network. In
specific, we present the detailed architecture of AGMEN, and investigate the
benefits and application scenarios of drone-cells, and UAV-assisted edge
caching and computing. Furthermore, the challenging issues in AGMEN are
discussed, and potential research directions are highlighted.Comment: Accepted by IEEE Communications Magazine. 5 figure
Application Management in Fog Computing Environments: A Taxonomy, Review and Future Directions
The Internet of Things (IoT) paradigm is being rapidly adopted for the
creation of smart environments in various domains. The IoT-enabled
Cyber-Physical Systems (CPSs) associated with smart city, healthcare, Industry
4.0 and Agtech handle a huge volume of data and require data processing
services from different types of applications in real-time. The Cloud-centric
execution of IoT applications barely meets such requirements as the Cloud
datacentres reside at a multi-hop distance from the IoT devices. \textit{Fog
computing}, an extension of Cloud at the edge network, can execute these
applications closer to data sources. Thus, Fog computing can improve
application service delivery time and resist network congestion. However, the
Fog nodes are highly distributed, heterogeneous and most of them are
constrained in resources and spatial sharing. Therefore, efficient management
of applications is necessary to fully exploit the capabilities of Fog nodes. In
this work, we investigate the existing application management strategies in Fog
computing and review them in terms of architecture, placement and maintenance.
Additionally, we propose a comprehensive taxonomy and highlight the research
gaps in Fog-based application management. We also discuss a perspective model
and provide future research directions for further improvement of application
management in Fog computing
Joint Optimal Software Caching, Computation Offloading and Communications Resource Allocation for Mobile Edge Computing
As software may be used by multiple users, caching popular software at the
wireless edge has been considered to save computation and communications
resources for mobile edge computing (MEC). However, fetching uncached software
from the core network and multicasting popular software to users have so far
been ignored. Thus, existing design is incomplete and less practical. In this
paper, we propose a joint caching, computation and communications mechanism
which involves software fetching, caching and multicasting, as well as task
input data uploading, task executing (with non-negligible time duration) and
computation result downloading, and mathematically characterize it. Then, we
optimize the joint caching, offloading and time allocation policy to minimize
the weighted sum energy consumption subject to the caching and deadline
constraints. The problem is a challenging two-timescale mixed integer nonlinear
programming (MINLP) problem, and is NP-hard in general. We convert it into an
equivalent convex MINLP problem by using some appropriate transformations and
propose two low-complexity algorithms to obtain suboptimal solutions of the
original non-convex MINLP problem. Specifically, the first suboptimal solution
is obtained by solving a relaxed convex problem using the consensus alternating
direction method of multipliers (ADMM), and then rounding its optimal solution
properly. The second suboptimal solution is proposed by obtaining a stationary
point of an equivalent difference of convex (DC) problem using the penalty
convex-concave procedure (Penalty-CCP) and ADMM. Finally, by numerical results,
we show that the proposed solutions outperform existing schemes and reveal
their advantages in efficiently utilizing storage, computation and
communications resources.Comment: To appear in IEEE Trans. Veh. Technol., 202
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
The Power of Smartphones
Smartphones have been shipped with multiple wireless network interfaces in
order to meet their diverse communication and networking demands. However, as
smartphones increasingly rely on wireless network connections to realize more
functions, the demand of energy has been significantly increased, which has
become the limit for people to explore smartphones' real power. In this paper,
we first review typical smartphone computing systems, energy consumption of
smartphone, and state-of-the-art techniques of energy saving for smartphones.
Then we propose a location-assisted Wi-Fi discovery scheme, which discovers the
nearest Wi-Fi network access points (APs) by using the user's location
information. This allows the user to switch to the Wi-Fi interface in an
intelligent manner when he/she arrives at the nearest Wi-Fi network AP. Thus we
can meet the user's bandwidth needs and provide the best connectivity.
Additionally, it avoids the long periods in idle state and greatly reduces the
number of unnecessary Wi-Fi scans on the mobile device. Our experiments and
simulations demonstrate that our scheme effectively saves energy for
smartphones integrated with Wi-Fi and cellular interfaces.Comment: accepted; Multimedia Systems, 2013. arXiv admin note: text overlap
with arXiv:1201.021
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