755 research outputs found
Distributed and Application-aware Task Scheduling in Edge-clouds
Edge computing is an emerging technology which places computing at the edge
of the network to provide an ultra-low latency. Computation offloading, a
paradigm that migrates computing from mobile devices to remote servers, can now
use the power of edge computing by offloading computation to cloudlets in
edge-clouds. However, the task scheduling of computation offloading in
edge-clouds faces a two-fold challenge. First, as cloudlets are geographically
distributed, it is difficult for each cloudlet to perform load balancing
without centralized control. Second, as tasks of computation offloading have a
wide variety of types, to guarantee the user quality of experience (QoE) in
terms of task types is challenging. In this paper, we present Petrel, a
distributed and application-aware task scheduling framework for edge-clouds.
Petrel implements a sample-based load balancing technology and further adopts
adaptive scheduling policies according to task types. This application-aware
scheduling not only provides QoE guarantee but also improves the overall
scheduling performance. Trace-driven simulations show that Petrel achieves a
significant improvement over existing scheduling strategies
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
Service Capacity Enhanced Task Offloading and Resource Allocation in Multi-Server Edge Computing Environment
An edge computing environment features multiple edge servers and multiple
service clients. In this environment, mobile service providers can offload
client-side computation tasks from service clients' devices onto edge servers
to reduce service latency and power consumption experienced by the clients. A
critical issue that has yet to be properly addressed is how to allocate edge
computing resources to achieve two optimization objectives: 1) minimize the
service cost measured by the service latency and the power consumption
experienced by service clients; and 2) maximize the service capacity measured
by the number of service clients that can offload their computation tasks in
the long term. This paper formulates this long-term problem as a stochastic
optimization problem and solves it with an online algorithm based on Lyapunov
optimization. This NP-hard problem is decomposed into three sub-problems, which
are then solved with a suite of techniques. The experimental results show that
our approach significantly outperforms two baseline approaches.Comment: This paper has been accepted by Early Submission Phase of ICWS201
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
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
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
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
A Dynamic Service-Migration Mechanism in Edge Cognitive Computing
Driven by the vision of edge computing and the success of rich cognitive
services based on artificial intelligence, a new computing paradigm, edge
cognitive computing (ECC), is a promising approach that applies cognitive
computing at the edge of the network. ECC has the potential to provide the
cognition of users and network environmental information, and further to
provide elastic cognitive computing services to achieve a higher energy
efficiency and a higher Quality of Experience (QoE) compared to edge computing.
This paper firstly introduces our architecture of the ECC and then describes
its design issues in detail. Moreover, we propose an ECC-based dynamic service
migration mechanism to provide an insight into how cognitive computing is
combined with edge computing. In order to evaluate the proposed mechanism, a
practical platform for dynamic service migration is built up, where the
services are migrated based on the behavioral cognition of a mobile user. The
experimental results show that the proposed ECC architecture has ultra-low
latency and a high user experience, while providing better service to the user,
saving computing resources, and achieving a high energy efficiency
Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach
Mobile edge computing (MEC) emerges recently as a promising solution to
relieve resource-limited mobile devices from computation-intensive tasks, which
enables devices to offload workloads to nearby MEC servers and improve the
quality of computation experience. Nevertheless, by considering a MEC system
consisting of multiple mobile users with stochastic task arrivals and wireless
channels in this paper, the design of computation offloading policies is
challenging to minimize the long-term average computation cost in terms of
power consumption and buffering delay. A deep reinforcement learning (DRL)
based decentralized dynamic computation offloading strategy is investigated to
build a scalable MEC system with limited feedback. Specifically, a continuous
action space-based DRL approach named deep deterministic policy gradient (DDPG)
is adopted to learn efficient computation offloading policies independently at
each mobile user. Thus, powers of both local execution and task offloading can
be adaptively allocated by the learned policies from each user's local
observation of the MEC system. Numerical results are illustrated to demonstrate
that efficient policies can be learned at each user, and performance of the
proposed DDPG based decentralized strategy outperforms the conventional deep
Q-network (DQN) based discrete power control strategy and some other greedy
strategies with reduced computation cost. Besides, the power-delay tradeoff is
also analyzed for both the DDPG based and DQN based strategies
UAV-aided urban target tracking system based on edge computing
Target tracking is an important issue of social security. In order to track a
target, traditionally a large amount of surveillance video data need to be
uploaded into the cloud for processing and analysis, which put stremendous
bandwidth pressure on communication links in access networks and core networks.
At the same time, the long delay in wide area network is very likely to cause a
tracking system to lose its target. Often, unmanned aerial vehicle (UAV) has
been adopted for target tracking due to its flexibility, but its limited flight
time due to battery constraint and the blocking by various obstacles in the
field pose two major challenges to its target tracking task, which also very
likely results in the loss of target. A novel target tracking model that
coordinates the tracking by UAV and ground nodes in an edge computing
environment is proposed in this study. The model can effectively reduce the
communication cost and the long delay of the traditional surveillance camera
system that relies on cloud computing, and it can improve the probability of
finding a target again after an UAV loses the tracing of that target. It has
been demonstrated that the proposed system achieved a significantly better
performance in terms of low latency, high reliability, and optimal quality of
experience (QoE)
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