7,086 research outputs found
Non-cooperative game approach for task offloading in edge clouds
Task offloading provides a promising way to enhance the capability of the
mobile terminal (also called terminal user) that is distributed on network edge
and communicates edge clouds with wireless. Generally, there are multiple edge
cloud nodes with distinct processing capability in a geographic area, which can
offer computing service for various terminal users. Furthermore, the terminal
users are competitive and selfish, i.e., each user takes into account only
maximizing her own profit, while conducting task offloading strategies. In this
paper, we focus on the resource management optimization for edge clouds, and
formulate the problem of resource competition among terminal users as a
non-cooperative game, in which the terminal user who acts as the player always
pursues the minimization of the expected response time for her tasks by
optimizing allocation strategies. We present the utility function of the user
with queuing theory, and then prove the existence of Nash equilibrium for the
formulated game. Using the concept of Nash bargaining solution to calculate the
optimal task offloading scheme for the user, we propose a distributed task
offloading algorithm with low computation complexity. The results of simulated
experiments demonstrate that our method can quickly reach the Nash equilibrium
point, and deliver satisfying performance at the expected response time of the
user's tasks.Comment: 12 pages,11 figure
A study of research trends and issues in wireless ad hoc networks
Ad hoc network enables network creation on the fly without support of any
predefined infrastructure. The spontaneous erection of networks in anytime and
anywhere fashion enables development of various novel applications based on ad
hoc networks. However, at the same ad hoc network presents several new
challenges. Different research proposals have came forward to resolve these
challenges. This chapter provides a survey of current issues, solutions and
research trends in wireless ad hoc network. Even though various surveys are
already available on the topic, rapid developments in recent years call for an
updated account on this topic. The chapter has been organized as follows. In
the first part of the chapter, various ad hoc network's issues arising at
different layers of TCP/IP protocol stack are presented. An overview of
research proposals to address each of these issues is also provided. The second
part of the chapter investigates various emerging models of ad hoc networks,
discusses their distinctive properties and highlights various research issues
arising due to these properties. We specifically provide discussion on ad hoc
grids, ad hoc clouds, wireless mesh networks and cognitive radio ad hoc
networks. The chapter ends with presenting summary of the current research on
ad hoc network, ignored research areas and directions for further research
Energy and Information Management of Electric Vehicular Network: A Survey
The connected vehicle paradigm empowers vehicles with the capability to
communicate with neighboring vehicles and infrastructure, shifting the role of
vehicles from a transportation tool to an intelligent service platform.
Meanwhile, the transportation electrification pushes forward the electric
vehicle (EV) commercialization to reduce the greenhouse gas emission by
petroleum combustion. The unstoppable trends of connected vehicle and EVs
transform the traditional vehicular system to an electric vehicular network
(EVN), a clean, mobile, and safe system. However, due to the mobility and
heterogeneity of the EVN, improper management of the network could result in
charging overload and data congestion. Thus, energy and information management
of the EVN should be carefully studied. In this paper, we provide a
comprehensive survey on the deployment and management of EVN considering all
three aspects of energy flow, data communication, and computation. We first
introduce the management framework of EVN. Then, research works on the EV
aggregator (AG) deployment are reviewed to provide energy and information
infrastructure for the EVN. Based on the deployed AGs, we present the research
work review on EV scheduling that includes both charging and vehicle-to-grid
(V2G) scheduling. Moreover, related works on information communication and
computing are surveyed under each scenario. Finally, we discuss open research
issues in the EVN
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning in communications and networking. Modern networks, e.g.,
Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become
more decentralized and autonomous. In such networks, network entities need to
make decisions locally to maximize the network performance under uncertainty of
network environment. Reinforcement learning has been efficiently used to enable
the network entities to obtain the optimal policy including, e.g., decisions or
actions, given their states when the state and action spaces are small.
However, in complex and large-scale networks, the state and action spaces are
usually large, and the reinforcement learning may not be able to find the
optimal policy in reasonable time. Therefore, deep reinforcement learning, a
combination of reinforcement learning with deep learning, has been developed to
overcome the shortcomings. In this survey, we first give a tutorial of deep
reinforcement learning from fundamental concepts to advanced models. Then, we
review deep reinforcement learning approaches proposed to address emerging
issues in communications and networking. The issues include dynamic network
access, data rate control, wireless caching, data offloading, network security,
and connectivity preservation which are all important to next generation
networks such as 5G and beyond. Furthermore, we present applications of deep
reinforcement learning for traffic routing, resource sharing, and data
collection. Finally, we highlight important challenges, open issues, and future
research directions of applying deep reinforcement learning.Comment: 37 pages, 13 figures, 6 tables, 174 reference paper
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
Fogbanks: Future Dynamic Vehicular Fog Banks for Processing, Sensing and Storage in 6G
Fixed edge processing has become a key feature of 5G networks, while playing
a key role in reducing latency, improving energy efficiency and introducing
flexible compute resource utilization on-demand with added cost savings.
Autonomous vehicles are expected to possess significantly more on-board
processing capabilities and with improved connectivity. Vehicles continue to be
used for a fraction of the day, and as such there is a potential to increase
processing capacity by utilizing these resources while vehicles are in
short-term and long-term car parks, in roads and at road intersections. Such
car parks and road segments can be transformed, through 6G networks, into
vehicular fog clusters, or Fogbanks, that can provide processing, storage and
sensing capabilities, making use of underutilized vehicular resources. We
introduce the Fogbanks concept, outline current research efforts underway in
vehicular clouds, and suggest promising directions for 6G in a world where
autonomous driving will become commonplace. Moreover, we study the processing
allocation problem in cloud-based Fogbank architecture. We solve this problem
using Mixed Integer Programming (MILP) to minimize the total power consumption
of the proposed architecture, taking into account two allocation strategies,
single allocation of tasks and distributed allocation. Finally, we describe
additional future directions needed to establish reliability, security,
virtualisation, energy efficiency, business models and standardization
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
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
Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach
In recent years, multi-access edge computing (MEC) is a key enabler for
handling the massive expansion of Internet of Things (IoT) applications and
services. However, energy consumption of a MEC network depends on volatile
tasks that induces risk for energy demand estimations. As an energy supplier, a
microgrid can facilitate seamless energy supply. However, the risk associated
with energy supply is also increased due to unpredictable energy generation
from renewable and non-renewable sources. Especially, the risk of energy
shortfall is involved with uncertainties in both energy consumption and
generation. In this paper, we study a risk-aware energy scheduling problem for
a microgrid-powered MEC network. First, we formulate an optimization problem
considering the conditional value-at-risk (CVaR) measurement for both energy
consumption and generation, where the objective is to minimize the expected
residual of scheduled energy for the MEC networks and we show this problem is
an NP-hard problem. Second, we analyze our formulated problem using a
multi-agent stochastic game that ensures the joint policy Nash equilibrium, and
show the convergence of the proposed model. Third, we derive the solution by
applying a multi-agent deep reinforcement learning (MADRL)-based asynchronous
advantage actor-critic (A3C) algorithm with shared neural networks. This method
mitigates the curse of dimensionality of the state space and chooses the best
policy among the agents for the proposed problem. Finally, the experimental
results establish a significant performance gain by considering CVaR for high
accuracy energy scheduling of the proposed model than both the single and
random agent models.Comment: Accepted Article BY IEEE Transactions on Network and Service
Management, DOI: 10.1109/TNSM.2021.304938
Quality of Service (QoS) Modelling in Federated Cloud Computing
Building around the idea of a large scale server infrastructure with a
potentially large number of tailored resources, which are capable of
interacting to facilitate the deployment, adaptation, and support of services,
cloud computing needs to frequently reschedule and manage various application
tasks in order to accommodate the requests of a wide range and number of users.
One of the challenges of cloud computing is to support and manage
Quality-of-Service (QoS) by designing efficient techniques for the allocation
of tasks between users and the cloud virtual resources, as well as assigning
virtual resources to the cloud physical resources. The migration of virtual
resources across physical resources is another challenge that requires
considerable attention; especially in federated cloud computing environments
wherein, providers might be willing to offer their unused resources as a
service to the federation (cooperative allocation) and pull back these
resources for their own use when they are needed (competitive allocation). This
paper revisits the issue of QoS in cloud computing by formulating and
presenting i) a multi-QoS task allocation model for the assignment of tasks to
virtual machines and ii) a virtual machine migration model for a federated
cloud computing environment by considering cases where resource providers are
operating in cooperative or competitive mode. A new differential evolution (DE)
based binding policy for task allocation and a novel virtual machine model are
proposed as solutions for the problem of QoS support in federated cloud
environments. The experimental results show that the proposed solutions
improved the quality of service in the cloud computing environment and reveal
the relative advantages of operating a mixed cooperation and competition model
in a federated cloud environment.Comment: 21 pages, 9 figures, 9 table
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