4,238 research outputs found
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
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
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
Offloadable Apps using SmartDiet: Towards an analysis toolkit for mobile application developers
Offloading work to cloud is one of the proposed solutions for increasing the
battery life of mobile devices. Most prior research has focused on
computation-intensive applications, even though such applications are not the
most popular ones. In this paper, we first study the feasibility of
method-level offloading in network-intensive applications, using an open source
Twitter client as an example. Our key observation is that implementing
offloading transparently to the developer is difficult: various constraints
heavily limit the offloading possibilities, and estimation of the potential
benefit is challenging. We then propose a toolkit, SmartDiet, to assist mobile
application developers in creating code which is suitable for energy-efficient
offloading. SmartDiet provides fine-grained offloading constraint
identification and energy usage analysis for Android applications. In addition
to outlining the overall functionality of the toolkit, we study some of its key
mechanisms and identify the remaining challenges.Comment: 7 pages, 2 figures, 2 table
Energy-Efficient Offloading in Mobile Edge Computing with Edge-Cloud Collaboration
Multiple access mobile edge computing is an emerging technique to bring
computation resources close to end mobile users. By deploying edge servers at
WiFi access points or cellular base stations, the computation capabilities of
mobile users can be extended. Existing works mostly assume the remote cloud
server can be viewed as a special edge server or the edge servers are willing
to cooperate, which is not practical. In this work, we propose an edge-cloud
cooperative architecture where edge servers can rent for the remote cloud
servers to expedite the computation of tasks from mobile users. With this
architecture, the computation offloading problem is modeled as a mixed integer
programming with delay constraints, which is NP-hard. The objective is to
minimize the total energy consumption of mobile devices. We propose a greedy
algorithm as well as a simulated annealing algorithm to effectively solve the
problem. Extensive simulation results demonstrate that, the proposed greedy
algorithm and simulated annealing algorithm can achieve the near optimal
performance. On average, the proposed greedy algorithm can achieve the same
application completing time budget performance of the Brute Force optional
algorithm with only 31\% extra energy cost. The simulated annealing algorithm
can achieve similar performance with the greedy algorithm.Comment: Accepted by the 18th International Conference on Algorithms and
Architectures for Parallel Processing (ICA3PP 2018
Resource Management of energy-aware Cognitive Radio Networks and cloud-based Infrastructures
The field of wireless networks has been rapidly developed during the past
decade due to the increasing popularity of the mobile devices. The great demand
for mobility and connectivity makes wireless networking a field whose
continuous technological development is very important as new challenges and
issues are arising. Many scientists and researchers are currently engaged in
developing new approaches and optimization methods in several topics of
wireless networking. This survey paper study works from the following topics:
Cognitive Radio Networks, Interactive Broadcasting, Energy Efficient Networks,
Cloud Computing and Resource Management, Interactive Marketing and
Optimization
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
With the breakthroughs in deep learning, the recent years have witnessed a
booming of artificial intelligence (AI) applications and services, spanning
from personal assistant to recommendation systems to video/audio surveillance.
More recently, with the proliferation of mobile computing and
Internet-of-Things (IoT), billions of mobile and IoT devices are connected to
the Internet, generating zillions Bytes of data at the network edge. Driving by
this trend, there is an urgent need to push the AI frontiers to the network
edge so as to fully unleash the potential of the edge big data. To meet this
demand, edge computing, an emerging paradigm that pushes computing tasks and
services from the network core to the network edge, has been widely recognized
as a promising solution. The resulted new inter-discipline, edge AI or edge
intelligence, is beginning to receive a tremendous amount of interest. However,
research on edge intelligence is still in its infancy stage, and a dedicated
venue for exchanging the recent advances of edge intelligence is highly desired
by both the computer system and artificial intelligence communities. To this
end, we conduct a comprehensive survey of the recent research efforts on edge
intelligence. Specifically, we first review the background and motivation for
artificial intelligence running at the network edge. We then provide an
overview of the overarching architectures, frameworks and emerging key
technologies for deep learning model towards training/inference at the network
edge. Finally, we discuss future research opportunities on edge intelligence.
We believe that this survey will elicit escalating attentions, stimulate
fruitful discussions and inspire further research ideas on edge intelligence.Comment: Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, and Junshan Zhang,
"Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge
Computing," Proceedings of the IEE
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
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
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