13,184 research outputs found
An Edge-Computing Based Architecture for Mobile Augmented Reality
In order to mitigate the long processing delay and high energy consumption of
mobile augmented reality (AR) applications, mobile edge computing (MEC) has
been recently proposed and is envisioned as a promising means to deliver better
quality of experience (QoE) for AR consumers. In this article, we first present
a comprehensive AR overview, including the indispensable components of general
AR applications, fashionable AR devices, and several existing techniques for
overcoming the thorny latency and energy consumption problems. Then, we propose
a novel hierarchical computation architecture by inserting an edge layer
between the conventional user layer and cloud layer. Based on the proposed
architecture, we further develop an innovated operation mechanism to improve
the performance of mobile AR applications. Three key technologies are also
discussed to further assist the proposed AR architecture. Simulation results
are finally provided to verify that our proposals can significantly improve the
latency and energy performance as compared against existing baseline schemes.Comment: This manuscript has been accepted by IEEE Networ
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
Edge Computing and Dynamic Vision Sensing for Low Delay Access to Visual Medical Information
A new method is proposed to decrease the transmission delay of visual and
non-visual medical records by using edge computing and Dynamic Vision Sensing
(DVS) technologies. The simulation results show that the proposed scheme can
decrease the transmission delay by 89.15% to 93.23%. The maximum number of
patients who can be served by edge devices is analysed
Recent Advances in Fog Radio Access Networks: Performance Analysis and Radio Resource Allocation
As a promising paradigm for the fifth generation wireless communication (5G)
system, the fog radio access network (F-RAN) has been proposed as an advanced
socially-aware mobile networking architecture to provide high spectral
efficiency (SE) while maintaining high energy efficiency (EE) and low latency.
Recent advents are advocated to the performance analysis and radio resource
allocation, both of which are fundamental issues to make F-RANs successfully
rollout. This article comprehensively summarizes the recent advances of the
performance analysis and radio resource allocation in F-RANs. Particularly, the
advanced edge cache and adaptive model selection schemes are presented to
improve SE and EE under maintaining a low latency level. The radio resource
allocation strategies to optimize SE and EE in F-RANs are respectively
proposed. A few open issues in terms of the F-RAN based 5G architecture and the
social-awareness technique are identified as well
Device vs Edge Computing for Mobile Services: Delay-aware Decision Making to Minimize Power Consumption
A promising technique to provide mobile applications with high computation
resources is to offload the processing task to the cloud. Utilizing the
abundant processing capabilities of the clouds, mobile edge computing enables
mobile devices with limited batteries to run resource hungry applications and
to save power. However, it is not always true that edge computing consumes less
power compared to device computing. It may take more power for the mobile
device to transmit a file to the cloud than running the task itself. This paper
investigates the power minimization problem for the mobile devices by data
offloading in multi-cell multi-user OFDMA mobile edge computing networks. We
consider the maximum acceptable delay as QoS metric to be satisfied in our
network. We formulate the problem as a mixed integer nonlinear problem which is
converted into a convex form using D.C. approximation. To solve the converted
optimization problem, we have proposed centralized and distributed algorithms
for joint power allocation and channel assignment together with
decision-making. Simulation results illustrate that by utilizing the proposed
algorithms, considerable power savings can be achieved, e.g., about 60 % for
large bit stream size compared to local computing baseline
Fog Computing based Radio Access Networks: Issues and Challenges
A fog computing based radio access network (F-RAN) is presented in this
article as a promising paradigm for the fifth generation (5G) wireless
communication system to provide high spectral and energy efficiency. The core
idea is to take full advantages of local radio signal processing, cooperative
radio resource management, and distributed storing capabilities in edge
devices, which can decrease the heavy burden on fronthaul and avoid large-scale
radio signal processing in the centralized baseband unit pool. This article
comprehensively presents the system architecture and key techniques of F-RANs.
In particular, key techniques and their corresponding solutions, including
transmission mode selection and interference suppression, are discussed. Open
issues in terms of edge caching, software-defined networking, and network
function virtualization, are also identified.Comment: 21 pages, 7 figures, accepted by IEEE Networks Magazin
Intelligent networking with Mobile Edge Computing: Vision and Challenges for Dynamic Network Scheduling
Mobile edge computing (MEC) has been considered as a promising technique for
internet of things (IoT). By deploying edge servers at the proximity of
devices, it is expected to provide services and process data at a relatively
low delay by intelligent networking. However, the vast edge servers may face
great challenges in terms of cooperation and resource allocation. Furthermore,
intelligent networking requires online implementation in distributed mode. In
such kinds of systems, the network scheduling can not follow any previously
known rule due to complicated application environment. Then statistical
learning rises up as a promising technique for network scheduling, where edges
dynamically learn environmental elements with cooperations. It is expected such
learning based methods may relieve deficiency of model limitations, which
enhance their practical use in dynamic network scheduling. In this paper, we
investigate the vision and challenges of the intelligent IoT networking with
mobile edge computing. From the systematic viewpoint, some major research
opportunities are enumerated with respect to statistical learning
Decentralized Edge-to-Cloud Load-balancing: Service Placement for the Internet of Things
Internet of Things (IoT) requires a new processing paradigm that inherits the
scalability of the cloud while minimizing network latency using resources
closer to the network edge. Building up such flexibility within the
edge-to-cloud continuum consisting of a distributed networked ecosystem of
heterogeneous computing resources is challenging. Load-balancing for fog
computing becomes a cornerstone for cost-effective system management and
operations. This paper studies two optimization objectives and formulates a
decentralized load-balancing problem for IoT service placement: (global) IoT
workload balance and (local) quality of service, in terms of minimizing the
cost of deadline violation, service deployment, and unhosted services. The
proposed solution, EPOS Fog, introduces a decentralized multiagent system for
collective learning that utilizes edge-to-cloud nodes to jointly balance the
input workload across the network and minimize the costs involved in service
execution. The agents locally generate possible assignments of requests to
resources and then cooperatively select an assignment such that their
combination maximizes edge utilization while minimizes service execution cost.
Extensive experimental evaluation with realistic Google cluster workloads on
various networks demonstrates the superior performance of EPOS Fog in terms of
workload balance and quality of service, compared to approaches such as First
Fit and exclusively Cloud-based. The findings demonstrate how distributed
computational resources on the edge can be utilized more cost-effectively by
harvesting collective intelligence.Comment: 16 pages and 15 figure
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
Delay-aware Resource Allocation in Fog-assisted IoT Networks Through Reinforcement Learning
Fog nodes in the vicinity of IoT devices are promising to provision low
latency services by offloading tasks from IoT devices to them. Mobile IoT is
composed by mobile IoT devices such as vehicles, wearable devices and
smartphones. Owing to the time-varying channel conditions, traffic loads and
computing loads, it is challenging to improve the quality of service (QoS) of
mobile IoT devices. As task delay consists of both the transmission delay and
computing delay, we investigate the resource allocation (i.e., including both
radio resource and computation resource) in both the wireless channel and fog
node to minimize the delay of all tasks while their QoS constraints are
satisfied. We formulate the resource allocation problem into an integer
non-linear problem, where both the radio resource and computation resource are
taken into account. As IoT tasks are dynamic, the resource allocation for
different tasks are coupled with each other and the future information is
impractical to be obtained. Therefore, we design an on-line reinforcement
learning algorithm to make the sub-optimal decision in real time based on the
system's experience replay data. The performance of the designed algorithm has
been demonstrated by extensive simulation results
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